Future Solving

How to use LLMs to lose to your competitors

Brian Evergreen — Sat, 11 Apr 2026 13:02:51 GMT

Hi, it’s Brian.

I teamed up with VentureBeat to bring you an AI mixology because sometimes an analogy is the best way to make things clear.

If you’ve ever felt like LLMs alone shouldn’t be the strategy, this is for you.

Things are moving so quickly in the era of AI. New open-sourced models, research breakthroughs, and product launches are announced daily. Who has time for setting a vision or creating a strategy? It’s impractical, and we have to make investments now.

For fast-moving executives, this is a set of quick and to-the-point recipes for different approaches you can take when it comes to language models. Each of these recipes is best prepared on the rocks.

The skeptic: Do absolutely nothing about AI

This is the most efficient approach, as it requires no mobilization of resources. Stay the course. Focus on incremental improvements to your value proposition and driving cost out of the bottom line. If your leadership or board asks about your plans for AI, take the position of the skeptic and chalk all of this AI stuff up as hype. Keep a log of failure stories of other organizations trying to use AI and use them as leverage against anyone making the case for why AI is an important investment focus for your organization. Take bets on when it will become clear that competitors have overtaken you, and make sure you time your exit before that becomes clear to the rest of the market.

Pairs best with a disjointed, low-trust leadership team and increasing employee turnover.

Announcement: My new course will continue to run live for the next 2 cohorts, with 12 hours of live sessions & a capstone project. My gift to subscribers of this newsletter is $1000 off the list price of my course with the promo code: SPRING

Enroll here.

The Bias for action: Make aimless investments in AI

If you want to draw out and savor the experience of losing, make investments in large language models (LLMs) without doing the strategy work to determine if language models are the next right investment for building on your organization’s core competencies or for your customers. Search for use cases targeting return on investment that can be reported to your leadership and double down on those metrics. Don’t worry yourself with transformation when you can meet your numbers with “Digital Reformation.”* There isn’t time for that.

Pairs best with multiple pilots with different vendors across the organization.

*Digital Reformation is the process of reforming, or improving the performance of, an organization’s products, services, processes or structures through the conversion of analog processes and assets to digital processes and assets without changing the nature of those products, services, processes or structures.

The wild ride: Move fast and deploy things

If you enjoy the adrenaline rush of roller coasters, schedule time later this week to announce a plan for how you’re going to infuse AI into everything you do and release an initial, topical layer of AI as quickly as possible so that your stock price can surge, enabling a slow decline followed by an exciting dive as your competitors who did the harder strategy, recruiting, and culture-building work build momentum and overtake you.

Pairs best with a marketing strategy repurposed from the Metaverse boom of 2022.

The antidote: Start with vision and work backwards

This option is much less exciting at first, but it will be worth it in the long run. It starts with setting a vision for the future you want to create. This means setting aside time outside of operational meetings to sit, brainstorm and set a vision as a leader (no, not a mission statement) such as “Our organization should have zero workplace safety incidents,” “We need to lower the effort involved in getting access to healthcare expertise,” or “Our company should be the kind of company where the younger generations of the workforce want to build their whole career.”

Once the vision is set, it should be socialized with peers and team members — a vulnerable but necessary step for getting feedback, building consensus, and generating momentum. After everyone is aligned on a shared vision for the future, work together to chart what would have to be true to bring that vision to fruition. Language models may very well play a role in that, but they also may not, and that’s okay.

Pairs best with an engaged, dynamic leadership team and a human-centered workplace culture.

Thanks for reading,

Brian

(I first wrote this on April 28th, 2024 in VentureBeat. You can read the original on VentureBeat here)

💻Live Course (e.g. The Antidote): Future Solving for Leaders

My new course just wrapped up its first cohort, and it’s packed with new frameworks and methodologies I’m willing to guarantee you haven’t seen before. If you join the first session and don’t agree, we will issue you a full refund.

Here’s what one of the recent students (a VP at Visa) shared about their experience:

“At a moment when organizations are under enormous pressure to respond to AI without a clear sense of direction, Brian’s Future Solving methodology offers exactly what’s missing: a rigorous, human-centered way to start with vision and work backwards — solving for the future rather than just reacting to it as it arrives. This course changes how you think — about strategy, about how to communicate ideas, and about the systems you’re trying to change. For any leader trying to create the future rather than just react to the moment, this course is essential.”

Learn more

(be sure to use the $1000 discount code for subscribers: SPRING)

If you know a leader who might benefit from joining, I’d be grateful if you forward this email along to them.

Whenever you’re ready, here are 5 ways I can help you:

1. Future Solving for Leaders Course: The most comprehensive course on Future Solving that empowers leaders to take the future into their own hands and create value with it. Join the community of Fortune 500 leaders who are leveraging Future Solving to create the future of their team, department, organization, and market.

2. Keynote Speaking: I’ve lectured everywhere from The Wharton School to Mastercard to Microsoft HQ on AI, AI agents, strategy, the future of work, leadership, and skills of the future. Learn more about booking me as a speaker

3. Sponsor & Cohost a Future Solving Club Breakfast, Dinner, or Virtual Event: The Club hosts breakfasts and dinners in dozens of cities and virtually, in partnership with top enterprise companies, startups, and consulting firms. Here are a few examples: London, Seattle, New York. When we convene these leaders (VP through C-level from over 200 of the Fortune 500) with world-renowned thought leaders to discuss the most pressing topics of today, our cohosts deepen client relationships and expand their network.

4. Future Solving Podcast: My new show (in partnership with Thinkers50) that explores how top thinkers, business leaders, and technologists solve for the future. Previous guests include Rita McGrath, Whitney Johnson, Alexander Osterwalder, Tomas Chamorro-Premuzic, and more.

5. The Future Solving Program: Join 50+ of the FORTUNE 500 and NASA, who have positioned themselves for the future in the era of AI by adopting Future Solving to take the future into their own hands and create value with it.

How to become an AI Influencer

Brian Evergreen — Sat, 04 Apr 2026 13:01:23 GMT

Hi, it’s Brian.

I first wrote this on April 5th, 2024 to more appreciation than I had anticipated.

In celebration of its 2-year anniversary, I’m sharing it here on Substack. Perhaps unsurprisingly, it’s even more relevant now than it was then.

If you’ve ever been confused or frustrated by people claiming to be AI experts and it not ringing as true, this is for you.

(next week I promise to get back on topic—I simply had to share this one)

💻Future Solving for Leaders Course

Here’s what one of the recent students (a VP at Visa) shared about their experience:

Enroll

Enroll here.

“Follow me or be left behind.”

Curate your LinkedIn profile to sound like an AI expert. It’s okay that you don’t have experience—just put “AI Researcher,” “Startup Advisor and Investor,” or “Chief AI Officer of Stealth AI Startup.” If anyone asks, you’re not at liberty to say. Besides, you kind of are starting an AI startup, right?
Create a newsletter on an app where people can’t see how many subscribers you have. This is important so that you can tell them to join over 50K subscribers to your newsletter since you’re just starting out. Instant credibility. Update this number as you gain followers - a 50:1 ratio is a good start and you can taper once you get over 100K LinkedIn followers.
Sign up for X and Reddit and follow people who actually have experience in AI. Copy/paste the best videos and posts into LinkedIn. The people who notice won’t say anything publicly because they won’t want their personal brand to be detracting from/policing others on social media.
Write this down: Everything is going to change everything. The slippery slope fallacy is your best friend (don’t look it up, just trust me). A research lab has found that ChatGPT scores higher than a small group of MBA students on a test? That’s the end of MBAs. Which will change education forever. We won’t even need professors anymore. Or universities. See? It’s easy.
Mark your calendar for all the major tech events throughout the year. You want to be first to post a breaking video from CES or from NVIDIA, Microsoft, Amazon, Google, etc.’s conferences. Your fellow aspiring AI influencers will be doing the same thing - flooding LinkedIn with hundreds of copies of the same video, so it’s critical that you’re the first one.
You’re basically a reporter, and your take is: 👍👍 Here are some starters for posts: JUST announced!; Game changer; Just in!; BREAKING; AI is moving so fast, here’s what you missed this week:; OpenAI just...; AI is coming for your job; Someone with AI is coming for your job; This is the end of [leading company in sector]
Your other job is to entertain. People’s feeds are cluttered with news and “Thought Leaders” who are trying to give them resources and guidance for creating more value at work (yawn). Break up their feed with a meme that makes it seem like you understand data science or a video clip about how Tom from Tom and Jerry was the first person to have his job replaced by AI. Whew. That’s better.
When you’re not reporting or entertaining, tap into fear. People are on LinkedIn because they want to become better professionals, find jobs, or hire people. All of those people will react strongly to a statement about how an advancement in AI is going to replace jobs or a list of which jobs are most likely to become obsolete. Fish in a barrel.
Always remember your competitive advantage over the majority of others on LinkedIn—anyone with real experience in AI has something to lose by engaging with your posts. Someone who has worked at Microsoft or Apple has a real possibility of working at Google someday, so they’re never going to post something saying “Google is dead.” But you are never going to work at Google, so you have nothing to lose being as provocative as possible.
Once you have enough followers, you have enough credibility to land features in high-profile publications and at real events. Tread lightly, as this could backfire if people realize you don’t understand AI or have any experience in the field. Lean on your background in crypto, Web 3, or NFTs if you have it and act like people just don’t get it. This won’t work with people who do get it but can get you out of tight situations.
If you do get caught in a situation where there’s a real risk that someone will recognize you don’t have experience or expertise in AI, pivot the conversation to concerns about ethics, safety, and regulation of AI. One guy who’s never worked in AI wrote an article about bombing data centers and it got featured in TIME Magazine! Anything’s possible if you believe.

Thanks for reading,

Brian

Whenever you’re ready, here are 5 ways I can help you:

All learning is human

Brian Evergreen — Sat, 21 Mar 2026 13:02:22 GMT

Hi, it’s Brian.

There’s this myth going around that AI is going to transform or even replace Learning & Development in our organizations. This idea has even made its way into conversations I’ve been having with Chief People Officers, and it needs to be addressed.

Today’s piece is about the future of learning and dispelling some of the misconceptions currently running rampant. If learning within your organization—or even your own learning and growth—is important to you, this is for you.

📅 Upcoming Events

Last Wednesday night, we held a Future Solving Club Dinner in Manhattan - check out the photos here.
Apr 15 | 🍽️ The Future Solving Club Dinner Salon (Atlanta) – an application-only evening for VPs of Innovation.
May 27 | 🍽️ The Future Solving Club Dinner Salon (Bentonville) – an application-only evening for VPs of Innovation.
Jun 23 | 🍽️ The Future Solving Club Dinner Salon (Chicago) – an application-only evening for VPs of Innovation.

If you’d like to apply to join our community and our events, let us know here.

If you’d like to be a sponsor and cohost, let us know here.

💻Future Solving for Leaders Course

Here’s what one of the recent students (a VP at Visa) shared about their experience:

“At a moment when organizations are under enormous pressure to respond to AI without a clear sense of direction, Brian’s Future Solving methodology offers exactly what’s missing: a rigorous, human-centered way to start with vision and work backwards — solving for the future rather than just reacting to it as it arrives. This course changes how you think — about strategy, about how to communicate ideas, and about the systems you’re trying to change. For any leader trying to create the future rather than just react to the moment, this course is essential.”

Enroll

Enroll here.

I used to teach eight-year-old children how to play chess blindfolded. Now I teach senior executives about Future Solving (more on that in another article coming soon). In every stage of my life, I’ve been voraciously learning or teaching others.

The concept of learning is largely unexamined, and on the surface, when running through an organization with the “Let’s-AIify-It” hammer, Learning & Development could seem like a good opportunity to save money and even improve outcomes. It’ll be personalized, right? Can’t the AI can just tailor the learning to each individual person? Without further examination, this sounds ideal.

Here’s why it isn’t:

1. Learning isn’t just about access to information

Being able to ask AI anything is nice, but we don’t learn through genie-style prompting (especially from a system that hallucinates and is designed to guess what we want to hear).

Think about the most important lessons you’ve learned in your life. When, where, and how did you learn it?

…is even one of them something you learned from ChatGPT or Claude?

Random acts of transformation

Brian Evergreen — Fri, 13 Mar 2026 13:02:05 GMT

Hi, it’s Brian.

It’s been over a decade since the term “Digital Transformation” was first used. Transformation has since ballooned into an umbrella term that’s lost most of its meaning. I know this firsthand, as I published Autonomous Transformation (Wiley) in August 2023 and have researched, keynoted, and had panel, fireside, and coffee conversations ad nauseum about “transformation.”

Today’s article is about the assumptions and bad choices we make when we commit “random acts of transformation.” If you want to be someone who creates meaningful change, this is for you.

📅 Upcoming Events

On Wednesday night, we held a Future Solving Club Dinner in Manhattan - check out the photos here.
Apr 15 | 🍽️ The Future Solving Club Dinner Salon (Atlanta) – an application-only evening for VPs of Innovation.
May 27 | 🍽️ The Future Solving Club Dinner Salon (Bentonville) – an application-only evening for VPs of Innovation.
Jun 23 | 🍽️ The Future Solving Club Dinner Salon (Chicago) – an application-only evening for VPs of Innovation.

If you’d like to apply to join our community and our events, let us know here.

If you’d like to be a sponsor and cohost, let us know here.

💻Future Solving for Leaders Course

Here’s what one of the recent students (a VP at Visa) shared about their experience:

“At a moment when organizations are under enormous pressure to respond to AI without a clear sense of direction, Brian’s Future Solving methodology offers exactly what’s missing: a rigorous, human-centered way to start with vision and work backwards — solving for the future rather than just reacting to it as it arrives. This course changes how you think — about strategy, about how to communicate ideas, and about the systems you’re trying to change. For any leader trying to create the future rather than just react to the moment, this course is essential.”

Enroll

Enroll here.

What are we, if not in the business of transformation?

Digital Transformation. Autonomous Transformation. Transformational leadership.

“Give me something, and I’ll transform it” we say. Transform teams, transform companies, transform industries, transform markets. Transform, transform, transform.

We are at the peak of inflated expectations for transformation, and the cracks are starting to show. Whether you’ve thought this before or this is a new idea to you, read on to learn how you can avoid committing random acts of transformation.

1. Digital Transformation is not innovation anymore

The term “Digital Transformation” was coined in 2014, as organizations around the world were exploring investments in the cloud and building “Digital Native” products and services.

In the ensuing decade, it’s become the mantra for pundits, the theme for events, the basis of job titles—it’s hard to spend a single hour on the internet without seeing the phrase “Digital Transformation,” let alone walk a conference expo floor.

According to Merriam-Webster, innovation has two definitions:

Innovation

1 : a new idea, method, or device

2 : the introduction of something new

Digital Transformation no longer fits into either definition. It fits much more neatly into the definition of

Semantic Satiation

1 : a psychological phenomenon in which repetition causes a word or phrase to temporarily lose meaning for the listener,who then perceives the speech as repeated meaningless sounds.

Going from analog to digital is conventional now. It’s still valuable, but it simply isn’t innovation anymore.

2. Transformation is not the goal

To transform is to change in composition or structure. Organizations commit random acts of transformation when transformation becomes the goal rather than the process.

When digital transformation becomes the goal, the work of developing a vision and strategy for what the organization will transform into becomes unimportant or is even considered a distraction from the tactical work that needs to be done.

When people try to dissect failed digital transformation initiatives, the natural instinct for many is to look for failures of the team, the technology, the process, or internal politics. More often than not, digital transformation became the goal, with an improvement in performance or reduction in cost as the justification. Unfortunately, this is has never and will never work as a key for unlocking systems change.

3. Most things don’t need to be transformed

When we pick up the digital transformation or AI hammer and start looking for a nail, we’re all but guaranteed to commit a random act of transformation.

I once watched helplessly as a group of leaders decided to invest nearly $10 million transforming something no one asked to be transformed or even “fixed.” It was a nail head and they wanted to swing their transformation-budget-hammer.

In parallel, one of the largest manufacturers in the United States was struggling with retention in their plants. Their IT department was convinced that if they launched a multimillion-dollar connected-worker program, retention would increase.

They asked workers why they were quitting: “Because I don’t know who to ask when I get stuck or what to do next.”

The solution?

Different colored shirts for managers.

Retention saw a record increase—from below industry averages to above.

Total budget? A few hundred dollars per plant.

Before you think through all the ways you can apply AI in your organization, start with understanding the goal and/or the need. You can almost always find a way AI or digital transformation could hypothetically help, but in many, many cases, you can address the issue without spending millions on technology.

4. Use cases are the base unit of random acts of transformation

Engineering teams need use cases when engineering a product to ensure the product has real-world applicability, which is why use cases have become so pervasive in discussions about technology.

But starting with use cases is like starting the build of a cathedral with a door. Then adding a wall. Perhaps a window?

👆🏼 Random acts of transformation.

The organizations who are seeing real results are first setting down ALL tools (including AI), defining what they want to achieve, then working backwards to determine the best path to their ambitions.

5. How to create intentional acts of transformation

All of the pitfalls mentioned above are related to people making sincere attempts to create value and make the world a better place.

If you are one of those people and you are interested in a more direct path to creating value and making the world a better place, here’s where to start:

Treat transformation as the means to an end, not the end itself, and set it aside as a goal or outcome.
Choose what value or future you want to create
Solve for that future by asking “What would have to be true to make this future inevitable?”
Write down everything you can think of
Call a meeting with others you trust and who have authority and influence to help you create that future
Show them what you’ve come up with and invite them to join you, sharpen your thinking, and align resources

Let me know how it goes.

Thanks for reading,

Brian

Whenever you’re ready, here are 5 ways I can help you:

The Future of Work is not a prize

Brian Evergreen — Sat, 07 Mar 2026 14:02:33 GMT

Hi, it’s Brian.

Every day there is a new prediction about the future of work. Most of them are wrong under scrutiny, but also at the start. Their assumptions about how we move into the future are flawed, and we’ve found ourselves in a moment where the future of work is working against the present of work.

Today’s article is about exactly that. If you want to be someone who has a hand in creating the future of work (and doesn’t make or fall for bad predictions), this is for you.

📅 Upcoming Events

Mar 11 | 🍽️ The Future Solving Club Dinner (NYC) – an application-only evening for VPs of Innovation.
Apr 15 | 🍽️ The Future Solving Club Dinner (Atlanta) – an application-only evening for VPs of Innovation.
May 27 | 🍽️ The Future Solving Club Dinner (Bentonville) – an application-only evening for VPs of Innovation.
Jun 23 | 🍽️ The Future Solving Club Dinner (Chicago) – an application-only evening for VPs of Innovation.

If you’d like to apply to join our community and our events, let us know here.

If you’d like to be a sponsor and cohost, let us know here.

💻Future Solving for Leaders Course

My new course is wrapping up its first cohort, and it’s packed with new frameworks and methodologies I’ve built in practice with Fortune 500 C-suite execs since publishing Autonomous Transformation in 2023. The next 2 cohorts will be fully live.

Enroll

Enroll here.

I’ve been in the business of the future for a number of years now. Across my roles at Microsoft (first as US AI Strategy Lead, then as Global Head of Autonomous AI Co-Innovation for Microsoft Research), in my consulting years, and since leaving Microsoft in 2023 to start The Future Solving Company, I’ve led thousands of dialogues, workshops, briefings, keynotes, and discussions on the future with people across functions, seniorities (emphasis on VP+), and industries.

We are in a moment where the future of work is working against the present of work, and in this article, I hope to clarify some key underlying assumptions and misunderstandings that can unblock individuals and teams when it comes to thinking about, planning for, and ultimately creating the future of work.

1. Stop preparing for the Future of Work

If you were to put yourself on a dot in the middle of a whiteboard, and you had to write down where the future is in relationship to you, where would you put it?

More importantly, who is moving toward whom?

Every futures discipline I’ve come across (except Future Solving) is inherently reactive. “Future proofing,” “future-ready,” the future is coming toward you. And you need to prepare and defend and fortify yourself and your business.

This is why we don’t flinch when someone says something like “we need to prepare our organization for when AI agents are doing all the work.”

There’s nothing more immobilizing than the idea that the future of work is coming at you at lightning speed and there’s nothing you can do but try to prepare and hope it doesn’t sweep you out of work and relevance.

There are people this idea benefits: 1) those who claim to have more insight on the future which they will share with you if you pay for their services and 2) those whose tools are the foundation of the alleged future.

2. Beware of sensationalism

30 years ago, distribution was a signal of credibility. You had to demonstrate your credibility before you could get a book deal, go on national television, or write articles for major publications.

So somewhere deep in our somatic system, when we see a viral post or quotation, there’s a temptation to misread its virality for credibility and seriousness.

This is compounded by the current state of digital platforms, which are oriented toward attention. Reasonable perspectives rarely go viral. Bold claims about the end of xyz job? Telling people they’re going to lose their jobs if they don’t learn the latest framework or technology? These spike adrenaline and stop the scroll. Even if you disagree, if you read it, you’re feeding into the virality of the post from your “dwell time,” even more so if you repost to disagree.

If you disagree and want to “downvote” an idea or someone sharing an outrageous idea, the best thing you can do is scroll past their post or click the … and say “I’m not interested.”

3. Take agency and find others who do too

The Future of Work is not a prize waiting behind a door, and it’s only moving toward you if you’re standing still.

Take Sam Altman, CEO of OpenAI. He’s actively solving for and building toward a future he wants to live in. He’s openly described his dream of AI becoming super intelligent, solving all of humanity’s problems, and eliminating the need for people to work.

Interestingly, Dario Amodei, CEO of Anthropic, has described a very similar future, predicting in May 2025 that AI will eliminate half of all entry-level jobs in the next 1-5 years.

If they’re the only one moving toward, solving for, and working to create the future they want to live in and the rest of us wait, the future we’re going to end up in is going to look like theirs (even if we don’t get to superintelligence, it will be closer to their vision for the future if others do not envision a different future and work with others to solve for it).

Ask yourself: What future do I want to live in? How can I play an active role in creating that future?

4. Beware of science fiction masked as AI research

We’ve been taught to take research seriously.

So how do we handle it when someone who claims they’re an AI researcher publishes a NYT bestseller, a TIME Magazine article, or a research report?

We take it seriously.

But should we? What if that “AI Researcher” has only “researched” how things could go wrong if AI ever became super intelligent? In other words, thinking about how things could go wrong, interviewing people and documenting potential risks, and writing stories about how it could all go wrong and how bad it could be?

That’s not AI research, that’s science fiction.

5. Follow the economic incentives

Anthropic and OpenAI have been releasing reports about how many jobs are exposed to AI. Here’s an example from a report released by Anthropic yesterday:

Note the term “theoretical capability.”

It’s like if Expedia released a report on the “theoretical vacationing” people could be doing versus how much they’re vacationing now.

It’s interesting, but not indicative of “where things are heading.”

It’s much more indicative of a company highlighting how much more they think their product could be used.

6. Don’t trust trends

When NFTs were trending, the data was clear: invest in NFTs or you’ll be left behind.

Remember the prompt engineering trend?

Trends indicate interest. Interest is reactive and viral. This creates a self-reinforcing loop independent of proof that there’s anything to the trend worth learning about or investing in.

7. Identify the speed shenanigans

There’s no shortage of people who will say something like “AI is moving so fast, here’s what you missed this week.”

CEOs of Fortune 500 companies (people who have tens of thousands of employees) have shared with me that they spent their weekend reading AI news and experimenting with vibe-coding so they can keep up.

If you zoom out and look at the pace of “AI progress” since ChatGPT was released in November 2022, what step change in capability would dramatically change how you would apply an LLM in your work?

Some of the updates have made LLMs materially better.

But if you’re in a manufacturing facility where you can’t afford hallucinations or you risk physical harm to your employees, you haven’t missed anything.

If you’re a marketer who wants your marketing copy to read like the average of the internet, you can do that better now with the newer models. But should you?

8. Decide what future you want to create

Who is creating the future of work?

Whoever chooses to and has the authority or can influence those who have authority over making investments that will change the trajectory of work.

If you’re reading this and that’s you, then here’s where to start:

Choose the future you want to solve for (anchored on core competency and customer value)
Solve for that future by asking “What would have to be true to make this future inevitable?”
Write down everything you can think of
Call a meeting with others you trust and who have authority and influence to help you create that future
Show them what you’ve come up with and invite them to join you, sharpen your thinking, and align resources

Let me know how it goes.

Thanks for reading,

Brian

Whenever you’re ready, here are 5 ways I can help you:

People will be the UI

Brian Evergreen — Wed, 25 Feb 2026 14:03:00 GMT

Hi, it’s Brian.

Sorry for the hiatus. I took a short break from writing — I’ve been busy launching our new podcast, Future Solving, planning for 2026, developing a more comprehensive portfolio of offerings for organizations (reply and let me know if you’d like to see our portfolio) and writing for other publications, like this one: No strategy without vision.

Today’s piece was inspired by a recent conversation with a Fortune 500 CEO on the future of products and services.

📅 Upcoming Events

Mar 11 | 🍽️ The Future Solving Club Dinner (NYC) – an application-only evening for VPs of Innovation.
Apr 15 | 🍽️ The Future Solving Club Dinner (Atlanta) – an application-only evening for VPs of Innovation.
May 27 | 🍽️ The Future Solving Club Dinner (Bentonville) – an application-only evening for VPs of Innovation.
Jun 23 | 🍽️ The Future Solving Club Dinner (Chicago) – an application-only evening for VPs of Innovation.

If you’d like to apply to join our community and our events, let us know here.

If you’d like to be a sponsor and cohost, let us know here.

100 years ago, 750,000 women were employed in the United States as “washerwomen.” They provided a service washing clothing for families.

Then the washer-dryer was invented and the service washerwomen provided was replaced with a new product.

30 years ago, the same thing happened to travel agents. As people could book their own travel through the likes of Travelocity, Expedia, and (later) AirBnB, the service provided by travel agents was replaced by a product.

In the past 100 years, innovators have focused on transforming as many services into products as possible, thanks to the new ability to mass produce physical goods, hardware, and software.

Now we have a product management problem

(but not the kind you might think)

Most of us wake up in the morning with a long backlog of to-dos, most of which involve calling call centers and waiting, sending emails to faceless customer service inboxes, logging into apps to order or book something, and so on.

With each product that’s been created to make our lives easier, more work has been incrementally shifted back onto our plates.

With AI, this risks becoming exponential. I don’t know about you, but I don’t want to "quickly chat” with an AI chatbot from every company I interact with. Even if it seems like it could be useful (which is so often is not), now I’m locked into that browser window until the conversation is finished.

AI as the new UI is backward

People are accepting chatbots and AI as the updated UI because it’s the best we have. In the paradigm where the “digital storefront” has been a website or app that requires manually clicking through menus, searching and scrolling through options, and filling out forms, the idea of an AI that can understand intent and handle tasks for us is appealing.

But the trust isn’t there. Neither for understanding what we actually want, nor for taking the right action. It also doesn’t bear any social responsibility the way a person does—even someone we’ve never met.

This is why most of us would have a worn out 0 button from customer service calls if we were still using home phones.

Why people should be the new UI

Speaking to other people for booking appointments, resolving issues, planning, and answering quick questions is much easier.

Despite user preference, companies have moved further and further from the ability to reach a person on the first call because of cost.

With all that AI can now do, the cost can finally go down, but not through removing humans from the loop (see case study from Klarna). They should instead embrace being human-first, placing humans back into the front of the customer interaction as the interface, with AI as the middleware (e.g. the AI empowers the person taking the customer calls to handle whatever’s needed as quickly as possible).

The 2 forms this could take

1. Companies differentiate from each other by going human-first (e.g. humans as the new UI)

Example: Expedia creates a new user experience for an added subscription or for users planning a trip with a budget above a certain threshold (in exchange for a small percentage of the overall booking), where they will have the option to do a phone call or video call with a travel agent who will use a network of relationships, AI, and AI agents to design the trip, present it to the customer for feedback and approval, proceed with booking, and be available throughout the trip as a virtual concierge.

Example: Uber decides to differentiate from its competitors by letting you text or call someone when you need to be picked up as a new subscription or a loss-leading differentiator.

2. Companies wait, and startups pop up with humans as the new UI

- Human-first domain-specific startups will start to pop up and disrupt industries that are not insulated

Example: A new travel agency where you can speak to a person who will use a network of relationships, AI, and AI agents to design the trip, present it to customers for feedback and approval, proceed with booking, and be available throughout the trip as a virtual concierge. Thanks to the technology available today, this would be achievable for a very small percentage fee added to the overall booking.

Example: A new shopping boutique that uses a human as the interface for anything a customer would have bought across grocery stores, Amazon, Uber Eats, DoorDash, Home Depot, etc. so the customer has a single, trusted point of entry that will be up-to-date on the best pricing across stores, logistics, and understands the style, allergies, and quality preferences of the customer. Anything someone needs, they can just text or call them and they handle it.

- Human-first domain-agnostic concierge startups will start to pop up and create a new category (this may happen either way)

Example: A new concierge service where customers are assigned a concierge team who can text with them throughout their waking hours to answer questions, take on tasks such as scheduling, research, booking, shopping, budget management. Like the example above, they would be able to use a network of relationships, AI, and AI agents as the middleware, so the human time could primarily be spent customer-facing and the commercial model viable.

Where to start

It starts with vision. In this case I’ve done some lifting for you, and you can start with the question: “How might a human-first interface for my organization or industry be a competitive advantage or

create a new growth category?”

Let me know what you come up with. I’ll be watching this space closely.

Thanks for reading,

Brian

Whenever you’re ready, here are 5 ways I can help you:

1. Future Solving Podcast: My new show (in partnership with Thinkers50) that explores how top thinkers, business leaders, and technologists solve for the future. Previous guests include Rita McGrath, Whitney Johnson, Alexander Osterwalder, and more.

2. Sponsor & Cohost a Future Solving Club Breakfast, Dinner, or Virtual Event: The Club hosts breakfasts and dinners in dozens of cities and virtually, in partnership with top enterprise companies, startups, and consulting firms. Here are a few examples: London, Seattle, New York. When we convene these leaders (VP through C-level from over 200 of the Fortune 500) with world-renowned thought leaders to discuss the most pressing topics of today, our cohosts deepen client relationships and expand their network.

3. Future Solving for Leaders Course: The most comprehensive course on Future Solving that empowers leaders to take the future into their own hands and create value with it. Join the community of Fortune 500 leaders who are leveraging Future Solving to create the future of their team, department, organization, and market.

4. Keynote Speaking: I’ve lectured everywhere from The Wharton School to Mastercard to Microsoft HQ on AI, AI agents, strategy, the future of work, leadership, and skills of the future. Learn more about booking me as a speaker

The Most Useful AI Startup

Brian Evergreen — Thu, 18 Dec 2025 21:35:11 GMT

AMESA invited & sponsored me to dive into their platform and learn about their tech stack, their methodology, their customer base, their roadmap, and what they’ve achieved. Here’s my take.

The man who designed 200+ AI agents at Microsoft

In 2019, I was the AI Strategy Lead for Microsoft U.S. and an SVP from a Fortune 500 customer I was partnering with wanted an autonomous AI system for one of their manufacturing plants.

I asked around internally and the unanimous answer was: “You need to connect with Kence Anderson.”

Kence Anderson, Founder & CEO, AMESA

Kence worked in Microsoft Research and had been acqui-hired as part of an acquisition of an autonomous systems startup in 2018.

Kence had designed over one hundred autonomous systems for clients by the time I met him, and introduced me to a concept called “Machine Teaching” (first introduced by researchers at Microsoft in 2017), which opened the aperture for thousands of use cases there weren’t possible or were too expensive with machine learning.

If you haven’t heard of machine teaching, here’s a simple explanation:

Machine Learning focuses on the “learners” (machines/algorithms). Machine Teaching focuses on the “teachers” (human experts).

Machine Teaching decouples knowledge about machine learning and data science from the process of translating human expertise to machine language, and it opens up a world of new capabilities that aren’t possible with machine learning. It will be a core capability for any organization hoping to achieve Autonomous Transformation.

Does it replace machine learning? No. It’s more like having a screwdriver when you’ve been using a hammer. We still need both, and which tool you should use depends on what you’re working to achieve.

If you want to learn more about Machine Teaching, you can enroll in Kence’s free Coursera/University of Washington course or you can read his book Designing Autonomous AI (O’Reilly, 2022).

The startup that achieves 7-figure ROI consistently

In February 2023, while everyone was talking about ChatGPT, Kence and I were talking about how GenAI posed a distraction from the potential of autonomous systems. We decided to address the issue from 2 angles.

I left Microsoft to found The Future Solving Company, launch my book (Autonomous Transformation), and help organization develop a vision and strategy for the future of work, leadership, creating new growth categories, moonshots, and how they want to position themselves in the era of AI.

Kence left Microsoft to start a company that builds and orchestrates autonomous AI agents that can actually be put into production because they don’t hallucinate: AMESA.

Why don’t they hallucinate, Brian?

Because they don’t have LLMs in the decision layer.

They don’t have “mouth-for-brains.”

They’re built using the Machine Teaching methodology. And what they’ve built is phenomenal.

In 2024, I started officially partnering with Kence and AMESA. When everyone was talking about Prompt Engineering, we came up with a new term we introduced in a VentureBeat article: Intelligence Engineering.

Created by Brian Evergreen & Kence Anderson, 2024

In 2025, we hosted dinners together for Fortune 500 and public sector leaders in Detroit, Seattle, and Washington D.C. - spreading knowledge and frameworks for building real-world, practical AI agents.

Other Agentic AI startups saw my article with Kence and our dinners together and reached out to me and asked me if I would post about them, cohost dinners with them, etc. but I graciously declined because AMESA is the only agentic AI platform I’ve seen to which I can give my full endorsement.

If you’re swimming in the sea of Agentic AI startups and well-meaning-but-broken-promises and you’re looking for a lighthouse, reach out to Kence and the team at AMESA. What they’re building is practical, real, rooted in a proven methodology, and is not an attempt to ride the LLM hype cycle or a rebrand from something else.

On top of that, Kence Anderson is a trustworthy person who is solving for a future where we all can create more value in partnership with autonomous systems.

If you would like an introduction, feel free to reply to this email and I’ll connect you with the team.

Thanks for reading,

Brian

Whenever you’re ready, here are 5 ways I can help you:

1. Future Solving Podcast: My weekly show (in partnership with Thinkers50) that explores how top thinkers, business leaders, and technologists solve for the future. Previous guests include Rita McGrath & Whitney Johnson.

3. LinkedIn Learning Course: A 48min introduction to Future Solving in a course called Organizational Leadership in the Era of AI. Join 17K+ learners who’ve taken the course and learned that “technology breakthroughs need management breakthroughs” and the basics of a new system of leadership purpose-built for the era of AI.

4. Keynote Speaking: I’ve lectured everywhere from The Wharton School to Mastercard to Microsoft HQ on AI, AI agents, strategy, future of work, leadership, and skills of the future. Learn more about booking me as a speaker

5. The Future Solving Program: Join 50+ of the FORTUNE 500 and NASA, who have positioned themselves for the future in the era of AI by leveraging new frameworks from the Future Solving Methodology I introduced in my book, Autonomous Transformation, to sidestep AI hype and ground their decision-making in a visible, strategic system built for today’s complexities.

Future Solving Podcast Launch

Brian Evergreen — Fri, 12 Dec 2025 23:53:20 GMT

The first episode of my new weekly podcast called Future Solving is out now, and who better to start with than Rita McGrath?

Why this podcast, why now?

I’m launching this podcast because we’re taught in school and at work to solve problems and leave things a little better than we found them, but there are almost no resources for people who want to go beyond solving problems to setting a vision and solving for a bold & achievable future for their life, their team, their organization, their ecosystem, or their market.

Because most people believe the future is something to be predicted or a problem to be solved, but I believe the future is more like an equation, where we determine the world we want to live in and solve for it: with vision, strategy, and bold leadership.

I believe the future is more like an equation, where we determine the world we want to live in and solve for it: with vision, strategy, and bold leadership.

I am solving for a future where anyone can become like the leaders and visionaries we all admire, because we all have ideas about creating a better future, but how do you get others to show up in the room, to make the decisions, invest the actual dollars, and do the work?

THAT is Future Solving.

Where to listen, watch, and subscribe?

🎧 Apple: Future Solving | Podcast on Apple Podcasts
🎧 Spotify: Future Solving | Podcast on Spotify
🎥 YouTube: Future Solving | YouTube
Thanks for reading Future Solving! Subscribe for free to receive new posts and support my work.

Episode 1: Rita McGrath on Strategy in the era of AI

Rita McGrath is one of the world’s foremost experts on strategy & innovation. She’s a strategy professor at Columbia Business School, and a longtime member of the Thinkers50 list, where she has consistently ranked among the top 10 management thinkers in the world.

Rita and I sat down together and discussed:

How to think about the term AI Strategy and the term strategy itself
The history of disruption
The great modern tragedy of management thinking and academia
Whether we still have visionary leaders (and how to cultivate a more visionary mindset)
How Rita would help one of the top AI companies win if given full rein
How she would reinvent Hollywood if she were named CEO of a major studio
and so much more.

We were able to produce this episode in partnership with Thinkers50 at Guildhall in London. Thank you to Mikko Leskelä, Stuart Crainer, & Des Dearlove for your partnership.

How to get involved?

I only have one ask: that if you listen to the podcast and find it useful or enjoyable, share it with someone else you think might enjoy it.

Thanks for reading,

Brian

Whenever you’re ready, here are 4 ways I can help you:

1. Sponsor & Cohost a Future Solving Club Breakfast, Dinner, or Virtual Event: The Club hosts breakfasts and dinners in dozens of cities and virtually, in partnership with top enterprise companies, startups, and consulting firms. Here are a few examples: London, Seattle, New York. When we convene these leaders (VP through C-level from over 200 of the Fortune 500) with world-renowned thought leaders to discuss the most pressing topics of today, our cohosts deepen client relationships and expand their network.

2. LinkedIn Learning Course: A 48min introduction to Future Solving in a course called Organizational Leadership in the Era of AI. Join 17K+ learners who’ve taken the course and learned that “technology breakthroughs need management breakthroughs” and the basics of a new system of leadership purpose-built for the era of AI.

3. Keynote Speaking: I’ve lectured everywhere from The Wharton School to Mastercard to Microsoft HQ on AI, AI agents, strategy, future of work, leadership, and skills of the future. Learn more about booking me as a speaker

4. The Future Solving Program: Join 50+ of the FORTUNE 500 and NASA, who have positioned themselves for the future in the era of AI by leveraging new frameworks from the Future Solving Methodology I introduced in my book, Autonomous Transformation, to sidestep AI hype and ground their decision-making in a visible, strategic system built for today’s complexities.

The Future Solving Club Launch 🚀

Brian Evergreen — Fri, 29 Aug 2025 19:39:37 GMT

I'm excited to (finally) announce something we've been working on for over a year.

How it started

At The Future Solving Company, we started bringing senior executives together because we believe the future should be created rather than predicted, that the future is co-created in gatherings amongst leaders, and that bringing leaders together in the right environment sparks something you can't spark anywhere else.

Our goal was to test and see if we could recreate magic—the sparks flying kind—consistently.

We started by experimenting with:

Size of the room
Visual setting
Time of day
Food quality
Guest curation
Topic curation
Conversation pace

We learned that every 👏🏼 little 👏🏼 detail 👏🏼 matters 👏🏼

And we learned that yes, you can recreate that magic consistently.

We sparked magic in London with Cassie Kozyrkov, in Detroit, Seattle, NYC, San Francisco, Chicago, Atlanta, & Dallas.

The main feedback we've received?

“The level of conversation exceeded anything I’ve ever experienced.” – Chief Digital Officer, $5B company.

and...

🙋More, please

Feedback on our London Dinner with IBM feat. Cassie Kozyrkov

How it's going

We ramped up—working with some of the biggest brands (e.g. AWS, IBM), with startups, and with boutique consulting firms who want to either: 1) bring clients together to discuss the future of the industry or 2) bring internal leaders together to dialogue with a thought leader.

We expanded our thought leader list—it used to just be yours truly (👋🏼). This year we started adding other speakers, the first being none else than Cassie Kozyrkov.

And we kept getting the same feedback: once or twice per year is not enough.

That's why we're launching The Future Solving Club.

We’re turning those rare moments and the magic they spark into a weekly practice—global, intentional, and useful.

So what is it?

The Future Solving Club is a private group of senior executives who are shaping the future, dialoguing with world-renowned thought leaders and executives weekly and at exclusive dinners and events throughout the year.

📅 Our first virtual session is September 24, 2025.

🎤 Speakers will be announced shortly (thanks to our partnership with Thinkers50—expect the best).

Who’s been in the room already?

Senior leadership from Microsoft, Starbucks, Google, L’Oréal, Ralph Lauren, PepsiCo, S&P Global, Amazon, ADM, IBM, Salesforce, F5, Nestlé, ServiceNow, P&G, Mars, Lumen, Johnson Controls, Eaton, Owens Corning, Dow Chemical, Analog Devices, PPG, Georgia-Pacific, Ford Motor Company, The LEGO Group, Citi, JPMorgan Chase, General Motors, AB InBev, Netflix, and others.

My ask

I’ll be sending invitations to my network. But I know there are brilliant leaders I haven’t met yet.

So Substack—do your thing:

👉 Follow The Future Solving Club for insights and takeaways

👉 Comment “Interested” if you'd like to request an invitation

👉 Tag a leader shaping the future who belongs in this room

👉 Or reply to this email with “The Club”

This isn’t about being exclusive. It’s about being intentional and building the room we all wish existed.

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

1. LinkedIn Learning Course: I recently introduced a 48min introduction to Future Solving in a course with LinkedIn Learning called Organizational Leadership in the Era of AI. Join 15K+ learners who've taken the course and learned that "technology breakthroughs need management breakthroughs" and learned the basics of a new system of leadership purpose-built for the era of AI.

2. Keynote Speaking: I’ve lectured everywhere from The Wharton School to Mastercard to Microsoft HQ on AI, AI agents, strategy, future of work, leadership, and skills of the future. Learn more about booking me as a speaker

3. Future Solving Workshops: Join 50+ of the FORTUNE 500 and NASA, who have positioned themselves for the future in the era of AI by leveraging new frameworks from the Future Solving Method I introduced in my book, Autonomous Transformation, to sidestep AI hype and ground their decision-making in a visible, strategic system built for today’s complexities.

The Future of Work according to Agentforce

Brian Evergreen — Thu, 17 Jul 2025 16:58:14 GMT

Salesforce invited & sponsored me to attend the launch of Agentforce 3 and learn about the latest updates to their AI agent platform. Here's my take.

Let's talk about AI agents.

I'm the author of Autonomous Transformation, a book about the next era beyond digital transformation that was named one of Thinkers50’s Top 10 Best New Management Books for 2024. Since it was published, over $20B of investment has been architected on the ideas and frameworks in the book, and last year I launched a LinkedIn Learning Course on Autonomous Transformation, AI Agents, and a new way of leading built-for-purpose in the era of AI.

Agents are a core concept in Autonomous Transformation, and I’ve written about them on Substack (here and here), in VentureBeat (here and here), in Fast Company (here), and I recently teamed up with Cassie Kozyrkov and Pascal BORNET to create a course dedicated to the topic: Agentic AI for Leaders.

So let's talk about Agentforce AI agents.

Now that I’ve established who I am and where I’m coming from when it comes to conversations about AI agents, here’s how I got involved with Salesforce:

Salesforce reached out to me a couple of months ago asking if I would be interested in partnering with them during their Week of Agentforce to learn more about Agentforce 3, then writing my take on it. My neutrality and credibility with all of you is important to me, so we agreed that my point of view would be 100% my point of view (e.g. none of what you're about to read is copy/pasted or written by anyone but me).

Here’s what we did:

Salesforce sent me embargoed materials about the investments Salesforce is making and the capabilities they're launching with Agentforce 3
I watched the Agentforce Release Event and participated in a live Q&A for press & analysts

Salesforce Week of Agentforce Keynote 2025

I interviewed Meredith Brown (Salesforce's Senior Vice President of Trailhead & Community). You can watch the full interview below:

I interviewed Ryan Gavin, Slack's Chief Marketing Officer, and learned about Salesforce and Slack's vision for integrating AI agents directly into the flow of work.

Here's my point of view

1. Salesforce has a strong vision for the future of work

According to Ryan Gavin (Slack's CMO), we're moving into the "Productivity Boom of the 2020s." I couldn't agree more.

He put it well: "Work is going to change more in the next 5 years than it has in the last 25 years."

Since 60% of most organizational capital is spent on employees, leaders are asking how they can get a greater return on their investment.

AI agents provide an elegant potential answer, since we spend ~40% of our time looking for and formatting information. Time lost on the work of work.

The future Meredith Brown (SVP at Salesforce) shared with me is one in which our human personnel get that 40% of their time back to be more connected, more creative—dare we say more human?

Our individuality, creativity, and "humanness" is our greatest competitive advantage. Freeing up more of that is going to have a direct impact on organizations’ top and bottom line.

2. Agentforce 3 is a thoughtfully engineered advancement, focused on addressing three key blockers to scaling AI agents: visibility, interoperability, and time to value

Building AI agents is hard, pioneering work. There's no easy button, and there are so many directions companies at the size and scale of Salesforce could focus their investments.

Without diving too deep into the technical aspects (let me know if you want to dive deep and I can connect you with the right team at Salesforce), the capabilities Salesforce is announcing with Agentforce 3 are important building blocks toward the future of Autonomous Transformation.

Unlike the "walled-garden" approach many leading companies took with operating systems and mobile phones, Salesforce sees the truth: organizations are going to build AI agents using many different platforms and visibility and interoperability between those agents regardless of where and how they were built will be critical to long-term success.

Lastly, because we live in a world of shareholder primacy, organizations who take too long to design, develop, implement, and create value with agents run the risk of funding cuts and discontinued pilots and projects. Salesforce has invested in creating the resources organizations will need to shorten time to value so they can earn deeper investment in the longer term, higher-value transformation.

3. Salesforce and Slack have a significant moat: the long-term memory of work

Every agent system can access structured data (e.g. data in tables like a list of customers or orders), but very few can ingest unstructured data (like conversations, pdfs, slides, etc.).

Between Salesforce and Slack, organizations who decide to build AI agents using Agentforce will have immediate access to their organization's structured and unstructured data, which will enable those agents to better understand organizational nuances that can't be explicitly trained.

If I ask an agent to pull up and analyze a 360 customer report from 2024 and create a template for my 2025 360 customer report, here's the difference:

Agent that only has access to my tables: "Unable to find ‘"360 customer report from 2024.’ Can you be more specific?"

Agent that has access to my tables and conversational history: "Here's the file: 'ACME 360_Final_July_2024'. It looks like your colleague Jack was concerned about whether or not you could break into the customer's marketing function in 2025, which was echoed by your leadership in the review. You now have 3 signed SOWs with the marketing function and a healthy pipeline going into H2, so this will be important to highlight to demonstrate your team's ability to break into dark accounts and functions."

Any organization that's already using Slack can tap into what Ryan Gavin (Slack's CMO) called "the long-term memory of work" of their organizations as they develop AI agents using Agentforce. This is important to note when reviewing AI agent offerings from various vendors. 1. Where is the long-term memory of your organization collected? 2. Which organization has the most native understanding and access to leverage that information to give AI agents rich context for every task?

4. My only critique (so you know this is a fair and honest piece): Calling AI agents "coworkers" or "teammates" could confuse people and inhibit adoption

AI and jobs is an extremely sensitive topic right now, as pundits and company leaders make pronouncements about how much longer entry-level jobs will exist, whether AI will take people's jobs, etc.

While these statements are immediately regarded as hype by AI experts, the majority of knowledge workers and front-line workers could experience uncertainty about their roles when they hear this kind of language (research shows we experience uncertainty as physical pain), which could significantly affect productivity and adoption (e.g. if a business is approaching it under the guise as "a new AI coworker" versus if a business is approaching it as "a new supporting tool that can take care of more complex tasks than previous tools").

We as people hold accountability for the work in our organizations, so when we request a coworker or teammate to handle something and they agree, we're transferring accountability and/or responsibility to that other person for the quality, speed, and (perhaps even) cost of that task or range of tasks. We can't and shouldn't try to transfer accountability and/or responsibility to AI agents for our work, so referring to them as coworkers, colleagues, or teammates subtly sets us up for a cognitive fallacy and abdication of responsibility.

I'd love to see Salesforce double down on the language they’re already using that does not anthropomorphize AI tools.

I'm grateful for the opportunity to see these developments unfold firsthand, and am excited for the future of work Salesforce and Slack are building toward.

I asked Salesforce if leaders from my community interested in learning more about Agentforce 3 and AI agents can skip the line and be introduced to the head of Agentforce for their function or industry, to which they’ve graciously agreed. If that’s you, DM me and let me know.

If you'd rather explore on your own, learn more about Agentforce here.

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

1. Keynote Speaking: I've briefed dozens of Fortune 500 C-suite executive teams, spoken to live, in-person audiences of more than 10,000 attendees, am a guest lecturer at Kellogg School of Management, and have led panels of distinguished guests ranging from academia to public sector leaders to Fortune 500 C-suite executives.

2. Future Solving Advising: Join hundreds FORTUNE 500 C-level executives and startup founders who have leveraged my advisement on AI, the future of technology, and how to position yourself for the future in the era of AI.

1 Evergreen, B. (2023, pg. 14). Autonomous transformation: Creating a More Human Future in the Era of Artificial Intelligence. John Wiley & Sons.

My honest take on IBM AI Agents

Brian Evergreen — Thu, 15 May 2025 21:58:38 GMT

Let's talk about AI agents.

I'm the author of Autonomous Transformation, a book about the next era beyond digital transformation that was named one of Thinkers50’s Top 10 Best New Management Books for 2024. Since it was published, over $20B of investment has been architected on the ideas and frameworks in the book, and last year I launched a LinkedIn Learning Course on Autonomous Transformation, AI Agents, and a new way of leading built-for-purpose in the era of AI.

Agents are a core concept in Autonomous Transformation, and I’ve written about them on my Substack (here and here), in VentureBeat (here and here), in Fast Company (here), and I recently teamed up with Cassie Kozyrkov and Pascal BORNET to create a course dedicated to the topic: Agentic AI for Leaders.

So let's talk about IBM watsonx Orchestrate and AI agents.

Now that I’ve established who I am and where I’m coming from when it comes to conversations about AI agents, here’s how I got involved with IBM:

IBM reached out to me a couple of months ago asking if I would be interested in partnering with them at their Think2025 conference to learn more about watsonx Orchestrate and its recent developments, then writing my take on it. My neutrality and credibility with all of you is important to me, so we agreed that my point of view would be 100% my point of view.

Here’s what we did:

I flew to Boston to attend Think2025, where I met with multiple senior IBM execs and attended keynotes and panels (starting with the opening keynote featuring IBM CEO Arvind Krishna), and the below session with Alon Cohen (SVP, R&D at UFC) and Stephanie Mehta (CEO & Chief Content Officer, Mansueto Ventures, Partner of Inc. and Fast Company)

Arvind Krishna (IBM CEO)

Alon Cohen and Stephanie Mehta

I met with industry analysts and AI experts to trade notes on what we were hearing and seeing, such as Pascal BORNET, Brij kishore Pandey, Kristen Kehrer, Ravit Jain, and Helen Yu

Helen Yu, Pascal Bornet, Kristen Kehrer, Brian Evergreen, and Brij Kishore Pandey

I learned about brands who are already using IBM watsonx Agents like L'Oreal, Heineken, PepsiCo, and UFC
I interviewed Bruno Aziza (IBM's Global Vice President of Data, BI & AI). You can watch the full interview below:

Here's my point of view:

1. IBM watsonx Orchestrate fills a much-needed gap in the Agentic AI ecosystem: Mission Control

True to their history in creating the computing technology that powered NASA's Mission Control during the Apollo missions, IBM has focused their investments on creating Mission Control for AI Agents, our next moonshot as we move into the era of Autonomous Transformation.

In 2025, enterprises are multi-cloud and hybrid, and IBM has created a pane of glass for building, managing, and running agents anywhere. This vision is an elegant example of systemic design because it acknowledges that enterprises are going to experiment and build agents with many vendors, creating an agnostic capability to build, manage, and run agents across the entire agentic ecosystem.

This also introduces a two-sided market dynamic for AI agents anchored on IBM's enterprise expertise. As enterprises adopt watsonx Orchestrate, IBM will have leverage to prioritize partner and vendor investment on their client needs and challenges, which in turn will fuel higher adoption from enterprises.

2. IBM watsonx Agents are (importantly) skill-based

Imagine a one-person band playing multiple instruments on the street corner. If they sounded decent, you might be impressed.

If you bought tickets to a symphony at the concert hall and, as the curtain opened, you saw that same one-person band, not only would you not be impressed, but you would want a refund.

The same performer that was impressive in one context is laughable in another.

This is how AI agents are today. Impressive capabilities in controlled experiments do not always translate to value in the enterprise context.

One of the key differences has to do with an architectural choice: do you build agents that attempt to do everything, or do you build specialized agents with "skills" trained on data specific to that skill?

IBM has chosen to build AI agents and agentic AI workbenches based on skills. This empowers organizations to "compose," "orchestrate," and "conduct" a symphony of agents to cohesively create scalable, real-world value.

Here are some examples of pre-built agents they’ve launched:

HR Agents

Sales Agents

Procurement Agents

3. IBM invented SQL in the 1970s, scaled parallel processing to beat the world chess champion in the 1990s, and they're ready to help enterprises navigate the complexities of Autonomous Transformation in the 2020s and 2030s

This is an era of invention, and IBM has demonstrated its ability to invent within constraints and produce real-world value for over a century.

Leaders seeking thoughtful and strategic partners to navigate these complexities would be well-served taking a meeting with IBM and learning from their perspective, investments, and approach to AI agents.

Based on what I saw last week, I would not be surprised to see IBM create and lead new market categories in the next decade.

4. My only critique (so you know this is a fair and honest piece) is reasonable given IBM's rich history, client confidentiality, and solution complexity—clients need a simpler way to understand what watsonx Orchestrate can (and can't) do for them

At Think2025, there were powerful demos and client stories from F1, UFC, L'Oreal, with IBM admirably centering their client outcomes over their own products and services.

As senior executives from clients praised IBM and watsonx's role in supporting their forays into the era of Autonomous Transformation, the specific role IBM watsonx played took some digging to uncover.

Technology companies have long been criticized for leading with their products, but in the case of IBM watsonx Orchestrate and agents and the next moonshot for enterprises, I believe leaders will be excited about whatever resources, frameworks, and guides IBM chooses to share.

I'm thrilled by IBM's vision and strategy, and excited to see how they have thoughtfully created practical capabilities anchored on their core competencies and translating their rich history into today's moonshot.

I asked IBM if leaders from my community interested in learning more about watsonx and AI agents can skip the line and be introduced to the head of watsonx Orchestrate for their function or industry, to which they’ve graciously agreed. If that’s you, DM me and let me know.

If you'd rather explore on your own, learn more about IBM watsonx Orchestrate here.

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

1 Evergreen, B. (2023, pg. 14). Autonomous transformation: Creating a More Human Future in the Era of Artificial Intelligence. John Wiley & Sons.

How to see the bigger picture re: AI agents

Brian Evergreen — Thu, 10 Apr 2025 19:43:14 GMT

Below is an excerpt from a recent VentureBeat article I co-authored with Pascal Bornet. You can read the full article here.

As we step fully into the era of Autonomous Transformation, AI agents are transforming how businesses operate and create value. But with hundreds of vendors claiming to offer “AI agents,” how do we cut through the hype and understand what these systems can truly accomplish and, more importantly, how we should use them?

The answer is more complicated than creating a list of tasks that could be automated and testing whether an AI agent can achieve those tasks against benchmarks. A jet can move faster than a car, but it’s the wrong choice for a trip to the grocery store.

Why we shouldn’t be trying to replace our work with AI agents

Every organization creates a certain amount of value for their customers, partners, and employees.

This amount is a fraction of the total addressable value creation (that is, the total amount of value the organization is capable of creating that would be welcomed by its customers, partners, and employees).

If every employee leaves the workday with a long list of to-dos for the next day and another list of to-dos to deprioritize altogether — items that would have created value if they could have been prioritized — there is an imbalance of value, time, and effort, leaving value on the table.

The easiest place to start with AI agents is looking at the work already being done and the value being created. This makes the initial mental math easy, as you can map the value that already exists and analyze opportunities to create the same value faster or more reliably.

There’s nothing wrong with this exercise as a phase in a transformation process, but where most organizations and AI initiatives fail is in only considering how AI can apply to value already being created. This narrows their focus and investments to the narrow overlapping sliver in the Venn diagram below, leaving the majority of the addressable value on the table.

Humans and machines inherently have different strengths and weaknesses. Organizations that collaboratively reinvent work with their business, technology, and industry partners will outplay those who merely focus on one body of value and endlessly pursue greater degrees of automation without increasing total value output.

Understanding AI agent capabilities through the SPAR framework

To help explain how AI agents work, we’ve created what we call the SPAR framework: sense, plan, act, and reflect. This framework mirrors how humans achieve their own goals and provides a natural way to understand how AI agents operate.

Sensing: Just as we use our senses to gather information about the world around us, AI agents collect signals from their environment. They track triggers, gather relevant information, and monitor their operating context.

Planning: Once an agent has collected signals about its environment, it doesn’t just jump into execution. Like humans considering their options before acting, AI agents are developed to process available information in the context of their objectives and rules to make informed decisions about achieving their goals.

Acting: The ability to take concrete action sets AI agents apart from simple analytical systems. They can coordinate multiple tools and systems to execute tasks, monitor their actions in real time, and make adjustments to stay on course.

Reflecting: Perhaps the most sophisticated capability is learning from experience. Advanced AI agents can evaluate their performance, analyze outcomes, and refine their approaches based on what works best — creating a continuous improvement cycle.

What makes AI agents powerful is how these four capabilities work together in an integrated cycle, creating a system that can pursue complex goals with increasing sophistication.

This exploratory capability can be contrasted against existing processes that have already been optimized several times through digital transformation. Their reinvention might yield small short-term gains, but exploring new methods of creating value and making new markets could yield exponential growth.

5 Steps to build your AI agent strategy

Most technologists, consultants, and business leaders follow a traditional approach when introducing AI (accounting for an 87% failure rate):

Create a list of problems;

Examine your data;
Pick a set of potential use cases;
Analyze use cases for return on investment (ROI), feasibility, cost, and timeline;
Choose a subset of use cases and invest in execution.

This approach may seem defensible because it’s commonly understood to be best practice, but the data shows that it isn’t working. It’s time for a new approach.

Map the total addressable value creation your organization could provide to your customers and partners, given your core competencies and the regulatory and geopolitical conditions of the market.
Assess the current value creation of your organization.
Choose the top five most valuable and market-making opportunities for your organization to create new value.
Analyze for ROI, feasibility, cost, and timeline to engineer AI agent solutions (repeat steps 3 and 4 as necessary).
Choose a subset of value cases and invest in execution.

Creating new value with AI

The journey into the era of Autonomous Transformation (with more autonomous systems creating value continuously) isn’t a sprint — it’s a strategic progression, building organizational capability alongside technological advancement. By initially identifying value and growing ambitions methodically, you’ll position your organization to thrive in the era of AI agents.

This article is inspired by ideas from Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work, and Life, the new best-seller I co-authored with Pascal Bornet.

Together with Cassie Kozyrkov, we have also launched the first Executive Masterclass on Agentic AI, where we teach how to successfully develop a strategy for Agentic AI and lead your organization into the era of Autonomous Transformation. The first cohort starts May 12th.

Agentic AI for Leaders on Maven

🎥Catch me live next week:

⚡45-minute (free) lightning lesson on Maven next Thursday:

How Leaders Should Approach AI Agents on Maven

🔥1-hour (free) fireside with Graphy on Monday:

How to build authority to make money on and off LinkedIn with Graphy

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

If you want to lead in the era of AI agents, I've partnered with Pascal Bornet and Cassie Kozyrkov to create a new course on Maven: Agentic AI for Leaders. This one-week intensive is packed with 12 hours of instruction, 3 live AMAs, and interviews with 15 world-class guest speakers to provide you with the knowledge, examples, and strategic frameworks you need to lead Agentic AI initiatives. The first live cohort starts May 12.
If you are looking for a keynote speaker for an event or team workshop, I’ve lectured everywhere from The Wharton School to Mastercard to Microsoft HQ on AI, AI agents, strategy, future of work, leadership, and skills of the future. Learn more about booking me as a speaker
If you want to up-level your vision and strategy as a team or organization, I’ve partnered with LinkedIn Learning, Emeritus, and Duarte to create tailored Future Solving™️programs that leaders at Fortune 500 companies, startups, and mid-sized firms are implementing to help reembrace science in their decision-making and meet the complexities of the 21st century with a system of leadership, strategy, and decision-making that is built-for-purpose for era of artificial intelligence. As of this writing, over $20B of investment is architected on these frameworks. Reach out to connect and learn more

My honest take on Agentforce

Brian Evergreen — Thu, 05 Dec 2024 19:26:50 GMT

Let’s talk about AI agents.

I started having something to say about AI agents back in 2019, when I joined Microsoft US to lead AI Strategy co-creation with iconic customers (after 5 years working in AI as a practitioner and leader at multiple consulting firms).

Starting my second week on the job, I began traveling around the United States to meet with Fortune 500 C-suite teams to talk about AI and how we could co-create strategies for harnessing its potential.

Customers were coming to the table like AI was the next iteration of the cloud—an opportunity to retool the same processes to be more efficient and generate savings.

They were applying the Digital Transformation playbook to AI.

But it wasn’t working because AI doesn’t exactly fit into Digital Transformation, because, by definition, the goal of digital transformation is to transform an organization’s products, services, processes, and structures through the reimagining and converting of analog processes and assets to digital processes and assets.1

AI can be leveraged to reimagine and convert analog processes and assets to digital processes and assets, but only using AI to convert from analog to digital is like only using a plane to drive on the freeway—you end up missing the bigger opportunity.

So, I set about defining the next mile marker, moving from analog or digital to:

au·ton·o·mous

1: having the right or power of self-government

2: existing or capable of existing independently2

I worked on defining this by writing a book called Autonomous Transformation, which was named one of Thinkers50’s Top 10 Best New Management Books for 2024, over $20B of investment has been architected on the ideas and frameworks in the book, and I recently launched a LinkedIn Learning Course on Autonomous Transformation, AI Agents, and a new way of leading built-for-purpose in the era of AI.

And agents? They’re a core concept in Autonomous Transformation, and I’ve written about them on my Substack (here and here), in VentureBeat (here), in CIO (here), and in Fast Company (here).

So let’s talk about Agentforce.

Now that I’ve established who I am and where I’m coming from when it comes to conversations about AI agents, here’s how I got involved with Salesforce:

Salesforce reached out to me a couple of months ago asking if I would be interested in partnering with them on their Agentforce World Tour Seattle to learn more about Agentforce and its recent developments, then writing my take on it. My neutrality and credibility with all of you is critically important to me, so we agreed that my point of view would be 100% my point of view.

Here’s what we did:

I dove under the hood with Muralidhar Krishnaprasad (MK) President and CTO of Agents and Data, Rahul Auradkar EVP and GM of Data Cloud, Alice Steinglass EVP and GM of Salesforce Platform, and Rob Katz VP of Product for Responsible AI in a private meeting with 3 other experts
I attended their 1-hour keynote session with 1K other attendees
Muralidhar Krishnaprasad, President and CTO of Agents and Data
I learned about brands who are already using Agentforce like Wiley, FedEx, and Saks
We came back together for personal hands-on demos and I had another 30 minutes with the executives to ask more questions

Here’s my point of view

Agentforce gets it right when it comes to Autonomous Transformation
Instead of focusing on automation or increasing efficiencies, Agentforce’s focus is on enabling entirely new value and better customer experiences that weren’t possible before the development of AI agents.
A second key distinction is that one of the critical enablers for organizations to begin their Autonomous Transformation journeys is scalable workbenches that democratize access to powerful models without needing an entire data science team for every initiative. This is because, according to Gartner, there are 10M software developers and only 10K data scientists. I experienced Agentforce’s workbench firsthand, and it has created the means for non-data-scientists to build an AI agent* end-to-end, with the extensibility to plug in more advanced models if you have them.
*the distinction here being that the AI agent would primarily exist (for now) in the realm of sales, marketing, and customer service given Salesforce’s existing data footprint
This video with Saks is a great example and is worth watching:
Agentforce is not a shallow rebrand, it’s anchored on a powerful vision (Humans with Agents drive customer success together), a strong strategy, and it's articulated clearly and simply
With so many companies jumping on the Generative AI and now AI agent bandwagon in the past 2 years, it would be easy to assume that’s what Salesforce is doing. It’s not. This is a carefully thought through vision, a well-developed strategy, and the articulation is understandable to the least technical person in the room.
I recall reading articles earlier this year about Marc Benioff being all-in on Agentforce, and now that I see what they’ve developed and what they’ve already put into production with customers, I can see why.
These are agents that actually make decisions and take action
In a previous article, I wrote about how AI Agents are not automation, and that one of the key distinctions between whether something is automation or an AI agent is in how it’s trained, how it makes decisions, and how it interacts with humans. Agentforce falls on the agent side one 3 out of 3 while still being capable of tapping into existing automation so organizations don’t have to reinvent the wheel or write off millions of dollars of investment they’ve already made. An agent is only one piece of the puzzle, and while it’s a useful piece, being able to tap into existing automation and existing APIs is a critical capability to ensure it can be leveraged in tandem with the rest of the pieces.
My only critique (so you know this is a fair and honest piece) is something that I think is a reasonable stepping stone given the trajectory they’re on—the UX for building agents needs to continue to evolve to be even more intuitive
It’s already intuitive enough that agents are getting put into production, which is a high bar. But I hope the next revolution of the platform takes on a new form factor that’s unexpected and truly delightful, with less text, because as we move into the era of Autonomous Transformation, the Kano Model is going to shift, and the ability to create value with agents, which is currently a delighter, will shift to a linear satisfier and eventually a baseline expectation (visual below), so Agentforce will need to continue building new delighters into their platform to enable users to even more intuitively build out the Internet of Agents.
Kano Model

I was genuinely excited after seeing what they’ve accomplished and learning more about their roadmap.

I asked Salesforce if leaders from my community interested in learning more about Agentforce can skip the line and be introduced to the head of Agentforce for their function or industry, to which they’ve graciously agreed. If that’s you, reply to this email and let me know.

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

My LinkedIn Learning Course launched on October 3rd: Organizational Leadership in the Era of AI. This 48-minute course is packed with frameworks and insights to help you lead in the era of AI using the new system of leadership I introduced in Autonomous Transformation and is free to anyone with a LinkedIn Learning subscription.
If you want to go deeper as an individual, you can sign up for my live course Future Solving Fundamentals. My flagship live course on how to position yourself in the future in the era of AI. I share over a decade of AI strategy expertise, proven methods, and actionable strategies. This course sets the stage for a new era of value creation with artificial intelligence. Join leaders from Microsoft, Accenture, Amazon, Disney, Mastercard, IKEA, Oracle, Intel, and more.
If you want to go deeper as a team or organization, I’ve partnered with LinkedIn Learning, Emeritus, and Duarte to create tailored Future Solving™️ programs that leaders at Fortune 500 companies are implementing to help reembrace science in their decision-making and meet the complexities of the 21st century with a system of leadership, strategy, and decision-making that is built-for-purpose for era of artificial intelligence. You can reach out to connect and learn more here.

Evergreen, B. (2023, pg. 14). Autonomous transformation: Creating a More Human Future in the Era of Artificial Intelligence. John Wiley & Sons.

“Autonomous,” Merriam-Webster Dictionary, https://www.merriam-webster.com/dictionary/autonomous.

But how do you change someone's mind?

Brian Evergreen — Tue, 05 Nov 2024 16:31:00 GMT

Let’s talk about overcoming friction.

It's one thing when our ideas are readily accepted by others, but what do we do when our ideas are met with friction or flat-out rejected?

I was asked this question recently by a Chief Strategy Officer when running a workshop for senior executives.

For some, the answer is data. Just show them the data, and they’ll be compelled to agree.

For others, it’s to escalate. If someone disagrees, go over their head and convince their leadership to compel them.

For others, it’s to build a wave of momentum with influencers across the organization.

Any of these may work, depending on the situation, but they’re less effective, especially long-term, than the approach I’m going to share with you.

Job Protectionism, Fatalism, and Pragmatism

Brian Evergreen — Sat, 02 Nov 2024 14:00:36 GMT

Are you on the side of protecting jobs, or should we make way for our robot overlords?

These represent the two dominant job philosophies in the media regarding jobs in the era of artificial intelligence.

The first is Job Protectionism, the belief that certain jobs or industries should be shielded from competition or other factors that may threaten their e…

How to make (more) intelligent decisions

Brian Evergreen — Tue, 08 Oct 2024 17:41:28 GMT

Let’s talk about decisions.

We make them every day in the workplace and at home. But how often do we think about how we make decisions? Or what decisions even are? For most of us, the process of decision-making has become an implicit process—meaning we make them without conscious awareness or deliberate effort.

Think about it. An email comes through. A co…

Transformation is not the goal

Brian Evergreen — Thu, 03 Oct 2024 00:29:06 GMT

"We're investing heavily in our Digital Transformation."

"That's great. What are you transforming into?"

"..."

Digital transformation is only valuable if it is helping an organization achieve a specific goal it could not have achieved in the analog paradigm.

If an organization is awarded the distinction of becoming the most digitally transformed organization in the world, but no one is buying their products and they cannot retain talent, Digital Transformation will have been a means to the wrong end.

Likewise with Autonomous Transformation, Digital or Autonomous Reformation and acts of creation.

If transformation is not a goal, but a process by which to achieve outcomes, how do we define the right outcomes?

How to not leave people behind in the era of AI

Brian Evergreen — Fri, 27 Sep 2024 23:06:15 GMT

Let's talk about the Fear of Being Left Behind

It's a slick sales tool.

"I don't want you to be left behind, so here's my [product or service] that will help you keep up."

It taps into the fear center in our brain, motivating us to action.

Put simply, it's manipulative. (usually)

It speaks to the character of the person saying it. If someone says "I don't want you to be left behind" followed by "so buy my" or "subscribe to my"—that person is manipulating you (and I wouldn't recommend buying anything from them).

It also implies that they're moving ahead faster than you are, which, in every case I've seen, is so untrue it's hard to decide whether to cringe or laugh. Reading someone else's thoughts on the latest releases in the AI world, tips for prompting LLMs, or gossip about the leadership team at OpenAI isn't the magic bullet for "keeping up with AI."

There are exceptions, and they sound more like: "I don't want people to be left behind, so I'm going to donate $500K or 1 hour a week to helping others stay ahead."

People will only be left behind if we leave them behind

AI is not self-directing. There are some in the field (and fearmongers masquerading as AI researchers) who believe we're moving toward AI that is self-directing, but there are no technically or logically sound arguments from here to there as of this writing.

That means AI will only be implemented based on choices we as people make. Every decision to use AI will be decided by a human.

So what will you decide?

3 ways to not leave people behind in the era of AI:

1. Change the conversation

Words matter. Every time someone says something about being left behind, reframe it:

I would never leave you behind, so here's my plan.

I would never leave you behind, so let's figure this out.

We would never leave our people behind, so we need to find another way.

2. Focus on growth

As a species, we're just getting started. There is so much work left to do before we automate everything we've already done and sit back WALL-E-style.

The real opportunity with AI is in creating new value and accelerating our ability to envision, reason, test, and learn.

When planning for AI initiatives, many focus purely on increasing the efficiency of an existing process, product, or service. While this can be an important step, especially if you're just starting and need to build capability and put points on the board, the real opportunity with AI is in the white space. Just like the real opportunity with the Internet for Blockbuster wasn't to just create a digital database of their movies for better tracking of late fees.

3. We're in this together, so let's act like it

Research shows that our brains experience uncertainty the same as physical pain. And there's a lot of uncertainty in the world right now.

We're not going to stumble into a better future for ourselves or those who will come after us.

Someone has to stop and decide to create a better future.

And it's a team sport.

If not us, then who?

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

My LinkedIn Learning Course launched on October 3rd: Organizational Leadership in the Era of AI. This 48-minute course is packed with frameworks and insights to help you lead in the era of AI using the new system of leadership I introduced in Autonomous Transformation and is free to anyone with a LinkedIn Learning subscription.
If you want to go deeper as an individual, you can sign up for my live course Future Solving Fundamentals. My flagship live course on how to position yourself in the future in the era of AI. I share over a decade of AI strategy expertise, proven methods, and actionable strategies. This course sets the stage for a new era of value creation with artificial intelligence. Join leaders from Microsoft, Accenture, Amazon, Disney, Mastercard, IKEA, Oracle, Intel, and more.
If you want to go deeper as a team or organization, I’ve partnered with LinkedIn Learning, Emeritus, and Duarte to create tailored Future Solving™️ programs that leaders at Fortune 500 companies are implementing to help reembrace science in their decision-making and meet the complexities of the 21st century with a system of leadership, strategy, and decision-making that is built-for-purpose for era of artificial intelligence. You can reach out to connect and learn more here.

Taking the jet to the grocery store

Brian Evergreen — Wed, 18 Sep 2024 18:34:02 GMT

Let’s talk about investments

On the one hand, there is pressure to find ways to capitalize on the potential of AI. On the other hand, you don’t want to bet the farm on something that isn’t proven. Especially when your CFO expects a clear timeline for ROI, and you know that if you can just meet it, you’ll be a hero in your organization with increased budgets, headcount—you can really be the spark of ushering your organization into the era of Autonomous Transformation.

So where to begin?

The temptation to take the jet to the grocery store

There are so many things we’re already doing that could be done so much more efficiently. Imagine if you could shorten your grocery store commute from 5 minutes to 10 seconds. That’s 30 times more efficient!

But wait. How much more expensive is it to fly a jet to the grocery store than to take a car? And do I need my commute to the grocery store to be more efficient than 5 minutes? Also, what’s the difference in fuel cost?

If you’re incorporating AI into your strategy by starting with quick and easy applications of AI where you can prove there will be a return on investment and guarantee a timeline, the only path forward is to choose things others have already invested in and gotten results from—a game of catch up—and the value of your investments are unlikely to justify the cost of the project, let alone the ongoing cost of data science staff.

So what should you do instead?

Assuming you’ve been putting off the task of incorporating AI into your strategy, here are 7 steps for getting started. (If you’ve already taken a swing at incorporating AI into your strategy, stay tuned for my post on Friday)

AI Agents are not automation

Brian Evergreen — Mon, 19 Aug 2024 19:16:09 GMT

AI agents are so hot right now.

There are more and more articles coming out on why autonomous AI agents are relevant for businesses, what they are (which I covered here), and speculating on how things are going to play out.

I mean this in a very demure, very mindful way: most of them are wrong.

I’ll explain.

Automation is like a Rube Goldberg machine

A Rube Goldberg machine, playfully demonstrated below, is built on a chain of reactions.

Source: Momentix Toys

In an organization, a chain of reactions might look like this:

Someone submits a purchase order
The purchase order is uploaded to a shared drive
The purchase order is also scanned with OCR to extract data
The data is then used to update various spreadsheets
BI dashboards are triggered to updated snapshots of the new total spend
The data is also fed into an LLM to summarize
The BI snapshots + the LLM summary are sent to an executive

Useful? Yes.

An agent? No.

Being positioned as an agent in marketing and sales? Believe it or not, yes.

Whether it’s an agent or not doesn’t matter if it’s useful for your organization. If the process doesn’t deviate, it would be far less expensive to invest in automation than autonomy.

But for leaders to harness the potential of these technologies, the distinction is important to understand.

An agent is like autopilot

A flight is not something that can be turned into a simple automation because it’s too complex.

There are too many variables: weather, bird strike, another airplane, systems failing, etc.

So, there are three distinct differences:

How it’s trained
Agents and autopilots are not trained like Rube Goldberg machines with simple “first this, then this” and “if this, then that” statements, though undoubtedly some of the sub-processes follow this pattern, the key distinction is that the training is instead based on principles.
Examples:
1. “The plane needs to be stable—run x simulations of different conditions and practice maintaining stability regardless of the wind, rain, altitude, etc.”
2. In a business context: “We need to notify the development team and reprioritize their workload when a customer has reported an issue that has financial implications for the customer—here’s a large sample of all customer reports and whether they ended up having financial implications. Analyze and find the key patterns between them and use those patterns to predict how likely each new customer issue is to have financial implications.”
How it makes decisions
Instead of basing decisions on binary inputs (If this, then that), agents make decisions autonomously based on confidence levels.
Examples:
1. “Based on the hundreds of systems and conditions the autopilot monitoring, there’s an 80% chance we will face adverse conditions if we don’t change course by x degrees.”
2. In a business context: “Based on hundreds of variables and patterns (sometimes imperceptible to humans), the agent is 92% confident this new customer reported issue will have significant financial implications and is therefore reprioritizing all of dev team x’s work and a hotfix deadline by EOD."
How it interacts with humans
Automation workflows often have human-in-the-loop steps depending on different conditions. Agents, however, have an overarching threshold across their entire flow of work and monitoring systems, at which they notify the human that it’s time to intervene or make a decision. This means that a human can be pulled in whenever needed, versus automation, where if the “check-if-we-need-a-human” step has cleared, a human may not be notified at the next step even if they should be. Importantly, they are also often set up to learn from the human decision.
Examples:
1. “The weather conditions are too unpredictable for our systems to maintain visibility and stability, so the pilot needs to take over.”
2. In a business context: “This customer reported issue does not fit into any category or pattern of previously reported issue, so the Customer Support team member needs to take over and categorize this issue or create a new category.”

An example from teaching chess to children

I used to train national chess champions, and I had some serious beef with how many “teachers” taught children to play chess.

They were teaching them like this: Move this pawn here on the first move. Then if they move this pawn here, bring the knight out. If they move that, bring the bishop out, and so on.

They had children memorizing hundreds of “if this, then that” scenarios. (let’s call it “Chess automation”)

When I knew my students were playing against someone taught this way, I told them to play something surprising that would break away from the memorized pattern, at which point the other player wouldn’t know what to do and would often start making random moves.

What I taught my students was to play based on principles: control the center of the board, develop your pieces, avoid isolating your pawns, etc.

They analyzed every game they played and every grandmaster game they reviewed through the lens of those principles, experimenting with and learning different ways of controlling the center, developing their pieces, etc.

Chess-playing computers used to be trained the first way (if this, then that), but they weren’t able to beat the best chess-playing humans in the world until they were trained on principles.

So how can you tell the difference? The easiest way is to look for memorized steps versus principles.

If someone tells you they have AI agents that do xyz, you can ask them to walk you through how: how are they trained, how do they make decisions, and how do they interact with humans.

Much, much more to come on what in the world AI agents are, how to think about and understand them, and how you and your organization can use them to create value.

Thanks for reading,

Brian

Whenever you're ready, here are 3 ways I can help you:

My LinkedIn Learning Course launched on October 3rd: Organizational Leadership in the Era of AI. This 48-minute course is packed with frameworks and insights to help you lead in the era of AI using the new system of leadership I introduced in Autonomous Transformation and is free to anyone with a LinkedIn Learning subscription.
If you want to go deeper as an individual, you can sign up for my live course Future Solving Fundamentals. My flagship live course on how to position yourself in the future in the era of AI. I share over a decade of AI strategy expertise, proven methods, and actionable strategies. This course sets the stage for a new era of value creation with artificial intelligence. Join leaders from Microsoft, Accenture, Amazon, Disney, Mastercard, IKEA, Oracle, Intel, and more.
If you want to go deeper as a team or organization, I’ve partnered with LinkedIn Learning, Emeritus, and Duarte to create tailored Future Solving™️ programs that leaders at Fortune 500 companies are implementing to help reembrace science in their decision-making and meet the complexities of the 21st century with a system of leadership, strategy, and decision-making that is built-for-purpose for era of artificial intelligence. You can reach out to connect and learn more here.

Thanks for reading Future Solving! This post is public so feel free to share it with others you think might find it helpful.

Future Solving

How to use LLMs to lose to your competitors

The skeptic: Do absolutely nothing about AI

The Bias for action: Make aimless investments in AI

The wild ride: Move fast and deploy things

The antidote: Start with vision and work backwards

💻Live Course (e.g. The Antidote): Future Solving for Leaders

How to become an AI Influencer

💻Future Solving for Leaders Course

“Follow me or be left behind.”

All learning is human

📅 Upcoming Events

💻Future Solving for Leaders Course

1. Learning isn’t just about access to information

Random acts of transformation

📅 Upcoming Events

💻Future Solving for Leaders Course

1. Digital Transformation is not innovation anymore

Innovation

Semantic Satiation

2. Transformation is not the goal

3. Most things don’t need to be transformed

4. Use cases are the base unit of random acts of transformation

5. How to create intentional acts of transformation

The Future of Work is not a prize

📅 Upcoming Events

💻Future Solving for Leaders Course

1. Stop preparing for the Future of Work

2. Beware of sensationalism

3. Take agency and find others who do too

4. Beware of science fiction masked as AI research

5. Follow the economic incentives

6. Don’t trust trends

7. Identify the speed shenanigans

8. Decide what future you want to create

People will be the UI

📅 Upcoming Events

Now we have a product management problem

(but not the kind you might think)

AI as the new UI is backward

Why people should be the new UI

The 2 forms this could take

1. Companies differentiate from each other by going human-first (e.g. humans as the new UI)

2. Companies wait, and startups pop up with humans as the new UI

- Human-first domain-specific startups will start to pop up and disrupt industries that are not insulated

- Human-first domain-agnostic concierge startups will start to pop up and create a new category (this may happen either way)

Where to start

The Most Useful AI Startup

The man who designed 200+ AI agents at Microsoft

The startup that achieves 7-figure ROI consistently

Future Solving Podcast Launch

Why this podcast, why now?

Where to listen, watch, and subscribe?

Episode 1: Rita McGrath on Strategy in the era of AI

How to get involved?

The Future Solving Club Launch 🚀

How it started

🙋More, please

How it's going

Who’s been in the room already?

My ask

The Future of Work according to Agentforce

Let's talk about AI agents.

So let's talk about Agentforce AI agents.

Here's my point of view

1. Salesforce has a strong vision for the future of work

2. Agentforce 3 is a thoughtfully engineered advancement, focused on addressing three key blockers to scaling AI agents: visibility, interoperability, and time to value

3. Salesforce and Slack have a significant moat: the long-term memory of work

4. My only critique (so you know this is a fair and honest piece): Calling AI agents "coworkers" or "teammates" could confuse people and inhibit adoption

My honest take on IBM AI Agents

Let's talk about AI agents.

So let's talk about IBM watsonx Orchestrate and AI agents.

Here's my point of view:

1. IBM watsonx Orchestrate fills a much-needed gap in the Agentic AI ecosystem: Mission Control

2. IBM watsonx Agents are (importantly) skill-based

3. IBM invented SQL in the 1970s, scaled parallel processing to beat the world chess champion in the 1990s, and they're ready to help enterprises navigate the complexities of Autonomous Transformation in the 2020s and 2030s

4. My only critique (so you know this is a fair and honest piece) is reasonable given IBM's rich history, client confidentiality, and solution complexity—clients need a simpler way to understand what watsonx Orchestrate can (and can't) do for them

How to see the bigger picture re: AI agents

Why we shouldn’t be trying to replace our work with AI agents

Understanding AI agent capabilities through the SPAR framework

2. Agentforce 3 is a thoughtfully engineered advancement, focused on addressing three key blockers to scaling AI agents: visibility, interoperability, and time to value