If you’re a business leader looking to define or refine an AI strategy, but you’re unsure where the AI hype cycle begins and ends, or you’re wondering how to get your organization ready, I’ve written this for you. Though the landscape is shifting quickly and some variables remain uncertain, the fundamentals that drive meaningful AI adoption are already clear.
TL;DR
If you are seeking to become an AI-enabled organization, these four things must be true:
1. People and data are the most valuable assets in the organization.
2. Distributed empowerment and trust are cornerstones of the culture.
3. The organization regularly and quickly executes complex changes with excellence. (This includes digital transformations, as well as shifts in strategic priorities, reorgs, or other Big Change.)
4. AI priorities and design are driven by a precise, first-hand understanding of the actual work and the people who do it.
Before you commit to an AI strategy, you need to have a clear “Why” that drives your decision-making. However, even if you aren’t fully certain of the direction your AI strategy will take you, there are practical steps you should begin to take now in preparation for the future that lies ahead.
Step 1: Make people and data your most valued assets.
To be clear, I’m not writing this as a data practitioner. Through an Experience Design and Change Management lens, I urge you to keep the human impact of data decisions top of mind as you shape your AI strategy.
Your data governance – stewardship, dictionaries, access, etc. – needs a refresh. Data literacy expectations for the average employee are significantly higher in AI-enabled organizations, and far more teams now require the ability to access data and act on it meaningfully. To support that, your data governance and security structures need to be ready to accommodate rigor at a new scale and speed.
To become an AI-enabled organization, data governance must become more important without becoming more cumbersome.
Traits of a Thriving AI-Enabled Organization (Part 1 of 5):
– Employees throughout the organization can access data generated by other parts of the business, and they know how and why to do so
– Data is trusted and ready for use
– It’s easy to mine and circulate insights
Now let’s talk about people. Maybe this is a hot take, although I hope not: It does not matter how much AI you introduce to your organization; the people who work there will continue to be the most important part of the organization’s success. Even further, if you’re making AI investments with the intention of reducing headcount, you are almost certainly missing out on the maximum ROI. Doing the same work with fewer people in an attempt to optimize for cost alone can become an unfortunate race to the bottom. Leaders who leverage their AI strategy to position for growth will see far better outcomes.
Like specialty power tools in the hands of carpenters, AI solutions should allow people to do their best work, solve bigger and more meaningful problems, and reduce toil.
Assuming that the pace of change and innovation continues, with new digital tools and market disruptions emerging on a regular basis, organizations that are excellent at adapting will be in the strongest position.
Traits of a Thriving AI-Enabled Organization (Part 2 of 5):
– Human talent is amplified and supported
– Adaptability is a basic strength of the organization
In an AI-enabled landscape, “adaptability” will be observable as well-practiced excellence in managing tech debt; experimenting, choosing, and adopting new AI tools; repositioning talent to new areas of the business; and responding quickly to new information. Each of these organizational attributes is distinctly human—ways of working, decision-making and team dynamics, prioritization, alignment. The winners of the AI transition won’t necessarily be those that have the best algorithms or that trim the most costs. The strongest, healthiest AI-enabled organizations will be those that get the human stuff right.
Step 2: Cultivate a culture of trust and distributed empowerment.
In July 2025, MIT’s Project NANDA (Networked Agents and Decentralized AI) published a study, “The GenAI Divide: State of AI in Business 2025,” of over 300 publicly disclosed AI initiatives, including 52 structured interviews and 153 senior leader survey responses. It begins with this startling statement: “Despite $30-40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return.” Later, the report goes on to say, “Behind the disappointing enterprise deployment numbers lies a surprising reality: AI is already transforming work, just not through official channels.” In other words, employees are adopting AI tools at work, just not the ones organizations are paying handsomely for.
There are several potential or attributable causes, but they almost don’t even matter; whether shadow adoption is driven by familiarity with a consumer LLM’s interface or its answer quality, or something else, the fact remains. So, what are you going to do about it? My vote – harness it.
The people doing the work know the work best, and at least some of them have already adopted AI tools. Create a culture of open and purposeful experimentation; allow people to find valuable problems to solve, and vet out different solutions. To do this well requires commitment, strategic clarity, investments of time and money, and empowerment.
Leadership has to be willing to commit to having less control. (Or perhaps just more control over a fewer number of things.) Results may come on unpredictable timelines. Some tools or projects won’t pan out. But most importantly, this kind of empowerment requires clear strategic alignment and pervasive product thinking within the organization—and that’s where you may need to invest in some outside support. Employees can’t be empowered to solve valuable problems unless they know what value looks like, and teams can’t accurately prioritize one problem over another unless those teams share a mental model for what’s important.
(As a side note: If product thinking isn’t a common skill set in your organization, invest now. Create spaces for your people to learn how to spot and solve valuable problems. Give them opportunities to practice product thinking alongside people they can learn from. This is now a table-stakes skill set.)
In tandem with creating your culture of experimentation, begin to elevate your organization’s overall digital and data literacy. AI is not a magic box, and people are better at understanding how to interact with and apply AI tools when they have a better understanding of what those tools are and are not. Introduce them to the wizard behind the curtain.
While these things are non-negotiable, they aren’t enough by themselves to get your organization out of pilot purgatory.
Once you set clear direction and empower people to prioritize problems and experiment with solutions, there must be mechanisms to support decisions of scale. At a minimum, you’ll need:
– Programmatic funding models that reward iterative assessment and measurable outcomes for AI initiatives (and the necessary baseline data from which to measure those outcomes)
– Effective cross-functional collaboration, peer sharing, and reincorporation of learnings into enterprise digital practices
– Enough goodwill and trust in leadership to willingly and promptly support final decisions
Distributed empowerment and experiment-driven decision-making necessitate strong connective tissue across the organization. Otherwise, it’s just chaos.
Traits of a Thriving AI-Enabled Organization (Part 3 of 5):
– People throughout the organization, regardless of their title or business function, know what makes their work valuable to the company’s goals
– People are empowered to test out AI solutions to solve problems in their work
– People close to the work, with budgets and domain expertise, are leading the way forward
– Scalable solutions scale
Step 3: Improve the execution of complex changes.
It is unlikely that the pace of change in business is going to slow down, and part of finding success as an AI-enabled org will require speedy enterprise change as a core competency of the organization. As the landscape continues to shift with ever-increasing speed, the certainty you can stand upon is that things change. So, get good at changing.
An AI-enabled organization will have a lot of decisions to make, announce, and adopt on short timeframes. Basic behaviors and habits will change as foundational workflows are reconsidered with AI at the center. Culture and ways of working between teams may shift. Reskilling and retooling will likely become regular, standard practice. When the scope is large enough, these types of changes are often challenging to manage as one-offs, much less as ongoing rhythms of business.
Expanding your organization’s capacity for handling change is a necessity because whatever dysfunctions your organization faces today in delivering digital excellence or transformation (and everybody’s got some), AI is far more likely to exacerbate them than to fix them.
Traits of a Thriving AI-Enabled Organization (Part 4 of 5):
– Strategic alignment is relatively easy to achieve across the various functions
– Big or small, desired changes tend to permeate the organization without unreasonable effort
– People throughout the organization are generally adaptable and receptive to change
Exercising and improving your organization’s change muscles is a no-regrets focus area to prepare for AI enablement.
Step 4: Go experience the work.
Many employees in your organization are afraid of what AI will mean for their livelihoods, and too many companies are choosing to promote their AI strategies internally with vague messages about “higher value work” or seeking efficiencies, which fails to assuage fears.
Instead of focusing on getting people to buy in, focus on cultivating trust. And trust is most easily built through observable actions.
Go to the gemba. “Before we wrote a single line of code, our data science team did the most important thing any AI team can do: We left the office.” That’s my friend and colleague Adam Silva in a blog post about the development of Bubble Vision, a (very cool – and patented!) digital product that uses AI to accurately measure the volumetric flow rate of a natural gas leak in the field—a meaningful advancement in protecting worker safety, environmental safety, and asset health at an energy utility.
The desire to prioritize the experience and needs of field technicians in their solution design wasn’t just lip service or lines in a slide deck. Before the team ever went to a whiteboard, they donned hard hats and went to the field. They designed the AI solution with respect for the work, which made it a whole lot easier for field technicians to trust the solution, adopt it into their workflows, and feel valued along the way.
In The Progress Principle, Teresa Amabile and Steven Kramer discuss a study they conducted of over 12,000 daily reports from 238 workers across 7 companies in which their central finding was that, “Of all the things that can boost emotions, motivation, and perceptions during a workday, the single most important is making progress in meaningful work.” Perhaps the best way to influence the perception of your AI strategy – to build trust and momentum for the direction you’re headed – is to show people that it works. For them.
Traits of a Thriving AI-Enabled Organization (Part 5 of 5):
– Leaders who prioritize and teams who design AI solutions go to observe the work before commitments are made or code is written
– The rigor involved in defining AI product requirements demonstrates respect and understanding of the work
– The success of AI solutions is defined by operational outcomes
Greenlit projects, design processes, and lived experiences speak louder about an organization’s priorities than any executive email announcement ever could.
To Recap
Even though the AI landscape is laced with uncertainty, there are still things we know for certain are worth doing, which will enable your organization to thrive in this new business context. Strengthening the foundations of your digital and human-centered practices in strategically specific ways will allow your organization to adapt to the changes ahead.
Key Takeaways:
- Your AI strategy is a people strategy. The strongest AI-enabled organizations treat people and data as their most valued assets—using AI to amplify human talent and solve bigger problems, not just cut costs. Build data literacy and product thinking as core competencies across the organization to optimize for growth.
- Distributed empowerment avoids fruitless deployment. MIT research shows 95% of enterprise AI initiatives get zero return, while employees successfully adopt AI through unofficial channels. Harness this. Create the conditions and collaborative infrastructure for great ideas to scale.
- How you manage change today predicts your tomorrow. If your organization struggles to manage complex, matrixed, or simultaneous transformations, the challenges will only increase as the pace of change accelerates. Invest in areas of your business that make change easier to execute.
- Go to the gemba before you build. Experience the work firsthand before designing AI solutions. This demonstrates genuine respect for the work being transformed and creates solutions people actually adopt.
