Hey there 👋, I’m Bryan
Welcome to my newsletter. I share practical advice and tackle tough questions on putting AI to work inside your business. Thank you for being here. Send me your questions and in return I’ll humbly offer actionable real-talk advice 🤜🤛
Four Ways Companies Do AI
I have observed four types of AI transformation approaches inside organizations.
The Mandate. The board says “go do AI.” This message is cascaded, teams are given a tool, and AI is unleashed. It’s fast, it’s loose, and it’s more common than you think. In a 2026 Harris Poll of 900 CEOs for Dataiku, 72% of U.S. CEOs said their boards are applying pressure to deliver measurable AI outcomes, up from 61% a year earlier (Dataiku). BCG’s survey of 625 CEOs and board members found 61% of CEOs believe their boards are rushing AI transformation, and 40% of board members who describe themselves as less AI-savvy than their peers worry the company isn’t moving fast enough (BCG). The people least equipped to judge the technology are often pushing the hardest. Three-quarters of executives in Writer’s 2026 survey admitted their company’s AI strategy is more for show than actual internal guidance (Writer), and in KPMG’s Q2 2026 AI Pulse nearly two-thirds of leaders said they’d consider incentives to encourage greater AI usage, including “token-maxxing,” rewarding employees for burning as many AI tokens as possible on an internal leaderboard (KPMG). One thing is clear: when the pressure comes from above without a plan, you get theater, and now the theater has a scoreboard.
The Lockdown. These organizations restrict AI as much as possible. This approach is rooted in fear, and doesn’t work because the usage happens anyway. Reco’s State of Agent Security 2026 found that four out of five AI tools used by employees run with no oversight from IT, and small and mid-size companies average about 414 unsanctioned AI tools per 1,000 employees (SC Media). A 2026 PagerDuty survey of 1,250 office professionals at companies over $500 million in revenue found that 66% had used AI tools at work despite believing it was against company policy, and nearly half said they’d rather use AI without telling anyone than risk being told no (TechTimes). There is a well-intended rationale behind restricting AI, but heavy-handed restrictions don’t stop AI usage, they just stop you from seeing it.
The Frontier. These organizations are interested in the edge of what’s possible. They find the fringe of the technology and ask, “how can we get there?” It’s very ambitious, and the data says ambition is not the problem. Absorption is. In the Dataiku study, 83% of CEOs expect to have AI agents in full production in 2026, even as their confidence in deploying agents at scale dropped from 41% to 31% since last year (Dataiku). KPMG’s global Q2 survey of 2,145 senior leaders shows the share of organizations embedding AI across the whole enterprise almost doubled in one quarter, from 13% to 22%, while the share reporting established ROI didn’t move. It sits at 7% (KPMG). In the U.S. edition, employee resistance to AI agents jumped from 5% to 20% in a single quarter, and resistance driven by added workload or complexity nearly doubled, from 28% to 51% (KPMG US). The frontier is real, but it moves faster than the people inside the building can follow. Ambition pays when an organization can absorb it, and absorbing it is exactly what the fourth approach is designed to do.
The Targeted Effort. These organizations point their talent at specific problems and instruct teams to solve them with AI. It’s a localized approach with a real attempt to track ROI, identify learning, and measure progress. It’s where most companies land and where most of them stall. In KPMG’s Q2 survey, 76% of leaders say AI is delivering meaningful business value, but only 7% describe themselves as having established ROI. Nearly half have delayed, scaled back, or questioned AI agent deployments because the costs outweigh the value, and only about one-third have full visibility into what their AI costs to run (KPMG). Data readiness and access is the top obstacle to deploying agents, cited by 58% of U.S. leaders (KPMG US). The interesting part is who gets through. Organizations with clearly defined accountability for AI outcomes report established ROI at more than three times the rate of those without it, and organizations with full cost visibility are five times more likely to report established ROI, 15% versus 3%. In other words, targeted works when someone owns the number and knows what it costs. The challenge is targeting the right thing for your organization.
Which One Are You?
If you recognize your company in the mandate, your first job is to get one real initiative with a real number attached before the board asks for a second update. If you’re the lockdown, your first job is to find out what people are already using and sanction the best of it. If you’re the frontier, pick one bet you can afford to lose and put the rest on hold until it pays. If you’re the targeted effort, you’re close, and the ten questions below are for you.
The Core Question
Ask 10 people how to introduce AI into an organization and get real value, and you’ll get 10 different answers. I don’t think the 10 people want different things. Most of us want the same things. We want to win, we don’t want to get hurt, we don’t want to get left behind, and we want to stay in control of our own shop. We each weigh those differently and go after them in different ways. Look back at the four approaches and you can see it. The mandate is fear of falling behind. The lockdown is fear of getting burned. The frontier is ambition. The targeted effort is a desire for control. Same wants, different volume, four very different approaches. We ask for things we aren’t sure are possible and we build toward things that may be reality in 3 months or 3 years. Underneath all of it sits one question: how do we identify and select the right AI initiatives to work on?
I’d argue that’s a people question before it’s a technology question, and leaders have been answering it forever. To maintain or grow in a world that never stops changing, we have to keep doing new things, which means we are constantly asking our teams to do things they have never done before. It’s the most well-worn leadership muscle we have.
If you’ve been doing the same job for 30 years, I know how that sounds. But think about what “the same job” has looked like over those 30 years. Someone asked you to move from paper to spreadsheets. Someone asked you to move from spreadsheets to an ERP. Someone asked you to stop faxing and start emailing, then to stop emailing and start using chat. Someone asked you to learn a new compliance regime, a new CRM, a new billing system, a new org chart after the merger. In 2020 someone asked you to run your entire operation from your kitchen table with two weeks’ notice, and you did it. The job title stayed the same. The job did not.
AI is that same problem on steroids. The technology changes month to month, it touches every function at once, and nobody can tell you exactly where it lands. The pace and the fog are a big part of why so many smart leaders grab the first strategy that matches whatever they’re most afraid of or most excited about. Understandable, but it doesn’t change the kind of problem this is. Human problems require human solutions.
So start with yourself. Where are you at? Are you dipping a toe into the AI pool, or have you already done a cannonball into the deep end? Be honest about which of the four wants is turned up loudest for you. Then look at your people. Where are they at? Maturity, sentiment, confidence, aptitude, curiosity. Those are the same things you’d want to know before asking a team to do anything hard for the first time, and they shape what you can realistically take on.
With that in mind, here are ten questions to help guide you in picking the right initiatives.
The 10 Questions
Where is my team technologically?
Pick initiatives that meet your people where they are. You know your industry. A software company is going to be more primed for AI than a regional distributor. You also know your people and your culture. AI is polarizing, and sentiment varies widely for a variety of reasons. Understanding this is the first step.
Your first initiative might simply be getting people comfortable with a new tool or increasing adoption. That won’t automatically move the bottom line, but it could be the unlock for everything that comes after.
What is my strategic roadmap?
Pick initiatives that align with the organization’s mission and roadmap, and avoid ones that collide with other major moving parts, like a platform conversion or a major upgrade. Taking on something today that gets upended in 6 or 12 months by a change you already know is coming is foolish. If it collides with something already in motion, take it off the list.
What are my vendors’ and third parties’ roadmaps?
Pick initiatives that enhance your vendor roadmaps, or at least don’t conflict with them. But know your vendor. Do they deliver on their roadmap promises, or is their roadmap an open-ended sales tactic? You know the confidence you have in them. Don’t get mentally blocked from an initiative just because a vendor says “that’s on the roadmap.” Whether you build it yourself or partner with someone who has already solved it, sometimes you just need to jump in. Stay aware of what muscle you are building as you determine where to build, buy, or parter.
What am I trying to accomplish, and by when?
Pick initiatives that are SMART: specific, measurable, attainable, realistic, time-bound. No surprise here. AI goals are just like every other goal in that they should be well thought out and poised for success. Measure the current state before you start. If you can’t say what success looks like and when, you don’t have an initiative yet. You have an idea. Set a date to review progress towards your goal and determine what it will look like to scale it or kill it before you start. This will mitigate a stalled pilot quietly becoming a permanent one.
What do I need to be successful, and do I have it?
Pick initiatives where you understand the inputs to success: data, people, cost, time, and whatever else the work demands. If you don’t have it, can you get it? If the answer is no, that’s your answer. Data is the input that kills more initiatives than any other. If the data doesn’t exist, isn’t clean, or isn’t yours to use, no amount of enthusiasm fixes that.
Who’s asking for this?
Pick initiatives where curiosity and interest are highest, and prioritize the ones identified by the front line, the people doing the actual work. No one wants an initiative rammed down their throat. Curiosity is gold. Find the curious people, and not only will they take on a new AI initiative, they’ll be grateful for the opportunity. You’ll also get the highest probability of success and the most meaningful learning.
Does the team believe they can be successful?
Pick initiatives in a way that builds confidence in the technology. Start with small wins.
Think about running a marathon. No one does it without preparing, and the interesting thing about training for a marathon is that you never run the full distance before race day. Marathons have an enormous number of first-time runners and the vast majority finish. How? They train. They start small and start slow. Run a mile. Then 2, then 4, then 6, then 8.
Take the same approach with your organization. Don’t ask your team to run a marathon right out of the gates. Ask them to walk a mile first. You’ll be amazed at what you learn along the way.
Can my team actually execute this?
Execution is hard. It always has been and always will be. You know what your team can deliver. A lot of AI initiatives require cross-functional collaboration, change management, and strong program management. Pick initiatives your team can get across the finish line, with clear cross-functional alignment from day one.
What is the organization willing to take on?
Pick initiatives that are in line with your risk tolerance. If your company doesn’t have the appetite to rethink customer-facing workflows right now, don’t take on that initiative. If your appetite is closer to back-office workflows that no one ever sees, start there.
Here’s the cool part. Your risk tolerance might not shift, but your perceived risk of any given initiative will change as you build experience, for better or worse. As confidence in the technology grows, bigger initiatives start to look smaller. That’s one more argument for starting small.
The other half of risk is containment. Before you green-light anything, ask what happens if it’s wrong. Can you turn it off? Does the customer data leave the building? Does a bad output reach a customer or regulator before a human sees it? Pick initiatives where the downside is small and reversible while you’re learning, and save the irreversible ones for when you’ve earned them.
Am I excited about this?
Pick initiatives that you’re genuinely excited about. If you’re not excited by what you’re funding, it’s not going to work. The goal is to start, to learn, to be curious, and to forge a new path forward with technology that is evolving at a rapid pace. It’s not going to be easy. Technological shifts never are. But those who jump in get rewarded. Pick the initiative that makes you look forward to what tomorrow brings.
Start Here
If you’ve never done this before, your first initiative should look like this: back office, so nobody outside the building sees it fail. Small enough to finish in 90 days. Requested by someone who already does the work and wants it gone. Reversible, meaning you can switch it off Friday and nobody notices Monday. Measured against a baseline you captured before you started. It won’t be the most valuable initiative you ever run, but it’s the one that earns you the right to run the valuable ones.
Something actionable you can do today 👣
Check in with yourself. Be realistic about what’s actually driving your AI roadmap. Is it you? The board? Fear of falling behind? A cost target? Curiosity? A competitor’s press release? None of those are wrong on their own, but some of them make you skip gates. Is the roadmap real, or is it theater? If you’ve been chasing, you already know it, and you know which of the four approaches you’ve really been running. You don’t have to tell anyone. Just be honest with yourself, because the ten questions only work if you’re willing to hear the answer.
Send out an AI sentiment survey. Understand how your team is thinking and feeling about AI. It’s an evolving landscape. People love it one week and hate it the next. There are real champions and real dissenters. Get involved in the conversation and understand where your people are, and pulse the team anytime you want a fresh read.
Capture what’s already out there. The AI ideas are floating around your teams right now. If you don’t have an intake form or process, build one today. A simple Microsoft or Google form works to start. Here are the fields I’d recommend:
Idea title
The problem it solves (what task, who does it today, how often)
Who would use it
Estimated time or cost saved per week
Data, systems, or vendors involved
Submitter’s name and team
Are you willing to pilot it?
What would you do with the time saved?
What’s the worst case if it’s wrong?
Your teams are already thinking about this. Start collecting the ideas.
Key Takeaways 🥡🥢
Meet the organization where it is. Maturity, sentiment, confidence, and curiosity are inputs to initiative selection, not afterthoughts. The right initiative for a company on its first AI project is different from the right one for a company on its tenth.
Pull beats push. The initiatives with the highest probability of success come from the front line and are staffed by curious people. Mandates from above without a plan underneath produce theater. Lockdowns produce shadow usage. Neither produces learning.
Small wins compound. Your risk tolerance probably won’t change, but your perceived risk of any given initiative will drop as your team builds experience. Starting small isn’t timid. It’s how you earn the right to go big.
AI doesn’t excuse you from discipline. SMART goals, quality decision making, clear inputs, executable scope, and cross-functional alignment still apply. Most pilots fail on those fundamentals, not on the technology.
In Summary
AI is not magic, and doesn’t absolve your team of discipline. Leaning into AI is a learning process and a bet on the future. Sure, you can get some efficiency today, but the bigger bet is skating to where the puck is going and bringing the entire team with you. That’s a change management effort as much as a technology effort, and it’s been done before. When spreadsheets replaced ledgers and email replaced memos the companies that came out ahead were the ones that picked their spots, built confidence, and kept going. You’re building for the next decade, not for a few bucks next quarter. You got this.
Thanks for reading!
Sincerely,
Bryan 👋
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