The State of AI: Building Trust and Aligning Strategy to Drive Adoption and Impact

If you’ve been in a room with technology leaders lately, you’ve probably heard a lot of excitement – and a lot of frustration – about AI. Artificial intelligence has moved rapidly from a conceptual tool to a C-suite priority that offers boundless potential, but implementation remains a messy, human process. 

The truth is, we’re all still figuring this out. AI is here, and it’s powerful. But it’s also new, overwhelming, and often underutilized.

Like with any emerging technology, if people aren’t using it, it doesn’t matter how impressive it is. 

At UDig, we’ve seen it firsthand – with clients and in our own work. Teams launch AI projects with high hopes, but adoption is slow, timelines drag on, and users eventually reject tools they don’t understand or trust.

What’s missing is the connection between the tool and the people it’s supposed to empower. From our experience at UDig, we have identified four best practices that technology leaders can leverage today to bridge the gap from concept to adoption and ensure that AI drives value for an organization. 

In this article, we will cover:

• 1. Drive AI Trust & Adoption
• 2. Specialize Your AI Builds
• 3. Be Strategic in Your Early Investment
• 4. Add AI To Your Workflows
• Making it Happen: From Ideation to Impact

1. Drive AI Trust & Adoption

The most pressing question in AI implementation isn’t “Can it be built?” It’s actually, “Will people use it?”

We have all experienced this before: teams build technologically sound, expertly crafted tools that go underutilized because they don’t align with how people actually work. The AI may function, but the experience for the end user is disjointed and, quite frankly, unfamiliar and uncomfortable. That’s because a vital piece of the pie was overlooked: teams, perhaps innocently distracted by the infinite possibilities, forgot to ask themselves, how will people use what we built? 

The truth is, if someone’s never used AI before, how can they decide whether they’ll use it now? It’s like handing someone a blueprint for a house and expecting them to be able to visualize themselves living in it. Unless they’ve practiced mentally translating a floor plan into a 3D reality, they’ll struggle to envision themselves moving from room to room.

With AI, there’s also a widespread “blank screen problem,” as we often call it. Many employees sit in front of an AI tool and aren’t sure where to start. They likely know the tool can do a lot, but without context or clear entry points, the intimidation is too high. A fruitful AI implementation doesn’t just drop a new model into a workflow; instead, it reshapes the workflow around how people think and decide.

Going back to the blueprint example, you’re not just building a house. You’re helping people visualize what it would be like to live there. That’s why trust and clarity are foundational.

Your AI solution should answer the following: What will this help me do better today? What problem does this solve for me or my team? Until organizations clearly answer those questions, they will struggle to achieve adoption.

2. Specialize Your AI Builds

AI is not one-size-fits-all. Like many other technological advances in their early stages, the tools that drive real results are the ones built for real problems. They operate within a specific context for a particular team.

Successful AI implementation usually comes from focusing on very specific use cases or business functions. Broad, horizontal implementations can become expensive and difficult to manage. When creating a solution for any business challenge, precision is what moves the needle. AI is no different.

You need to think vertically. What are your company’s unique differentiators? What do you do better than anyone else? Where do you have unique data, processes, or industry insight? At UDig, we often come back to a question Richart Rumelt poses in Good Strategy, Bad Strategy: what are your “sources of power”? What are the things that create unique advantages for your organization in the marketplace? That’s where your AI focus should be.

Let’s say you’re in the insurance industry and you’ve built up years of expertise handling claims. That’s your advantage, so design your AI tools to make your differentiator sharper. Build your method into models that understand your policies, identify problems, and provide recommendations for adjusters. This is not about automation. Instead, it’s about amplifying what makes your organization exceptional and using AI to make that process easier, simpler, and faster for your team. 

To put it simply: tangible, impactful results come from focusing on a few clear, high-impact use cases. Start with two or three things that only apply to your business and dive in.

This strategy works because it builds credibility. When your first project shows results, it becomes the proof point that unlocks the next one. You’ll be able to say, “We’ve demonstrated what’s possible. Now let’s apply that momentum where it matters most.”

That’s how you scale and obtain C-suite buy-in for the next AI investment. You don’t get there by boiling the ocean; you get there by solving one real problem at a time.

3. Be Strategic in Your Early Investment

Too often, we have seen organizations take a horizontal, cross-functional approach to AI—i.e., they throw five different things at a wall and hope one of them will stick. While piloting an AI concept gives you the opportunity to explore what’s most useful, what must remain at the core is a clear line of sight to business outcomes. 

We hear this all the time: “We built a great prototype, but no one’s using it.” Or: “We tried AI, but it didn’t deliver the ROI we hoped for.” What we have seen as the recurring missing element in these situations is intentionality. The best solutions use the right tools at the right times. There are plenty of great ideas, but real results happen when there is an intentional strategy and process for choosing the right ideas. 

Your early AI efforts should act like an anchor tenant in a new commercial development. They draw attention and customers, establish value, and set the stage for further growth. Within your organization, look for areas where:

• You have enough data to train a useful model
• The process is repetitive or decision-heavy
• There’s C-Suite buy-in and operational alignment

You shouldn’t try to transform everything at once. Pick one high-impact workflow that can serve as the proof point where AI clearly improves outcomes and drives ROI. Once you can prove that in a focused way, it creates a story that spreads throughout the organization. It shows what’s possible, builds momentum, and positions you as the team doing it right. 

Finally, make sure the investment is tangible. Strategic investment beats scattered pilots every time. Run intentional pilots, test them in real environments, evaluate the results, and clearly demonstrate the ROI. When the C-Suite sees actual business impact, it builds trust – and can lead to more funding. Being relentlessly focused on what will create value now will help grow credibility and capability later.

4. Add AI to Your Workflows

If AI is going to enhance your business, it has to work the way your organization works. That means designing around real workflows and making sure your IT team is a strategic partner from day one.

For years, automation meant writing scripts to make routine tasks move faster and more efficiently while reducing errors. But those scripts were fragile. A single change in a website or system could render those scripts useless. AI offers a more flexible and resilient way forward, but only if you build it around how people actually get things done.

Start by mapping your key workflows. What are employees trying to achieve? Where do they get stuck? What decisions slow them down? Tools like service blueprints help you visualize this clearly. They show where people, systems, and data come together, which is precisely where AI can provide value. 

From there, focus your AI investments on real challenges and how well they fit into a small set of real scenarios (i.e., the use cases we discussed earlier). This keeps development focused and grounded in value and tangible business outcomes. 

Finally, don’t overlook the role of IT. Many AI tools fail because they don’t integrate cleanly with existing systems or because support teams are brought in too late. Your IT team needs to be involved early to manage and scale the infrastructure and plan for long-term sustainability. As demand for AI grows internally, your IT team will be asked to maintain and expand these solutions. The last thing you want is for them to play catch-up.

Going back to the house blueprint concept, a good way to think of this is like building a smart home. You don’t just drop in a bunch of gadgets and hope they work together. You plan the wiring, the layout, and all of the systems underneath—and you bring in the right teams at the early stages to ensure everything works together seamlessly before it all goes behind drywall.

In short, when you bring IT into the planning process and design AI around real people doing real work, you build something that lasts, and most importantly, gets used. 

Making it Happen: From Ideation to Impact

At its core, AI is still technology. It’s new, it’s evolving fast, and it’s incredibly powerful – but it still needs to solve real problems. As a leader who is selling this new technology to the C-Suite, that means your focus should be on impact, rather than on simply “keeping up with the latest technologies.”

From working with successful organizations, we’ve learned that the most impactful moves happen when the conversation shifts from what the tool can do to what the people using it will accomplish. AI is the means, not the outcome.

Remember, start with people: their challenges, workflows, and goals. Build trust by showing them how AI will help them (not replace them, as we all hear too frequently). Specialize in areas where your organization already stands out and has successful differentiators. Invest in one or two high-leverage use cases and grow from there. And finally, make sure your internal systems, especially IT, are built to support what’s coming.

One of the biggest traps we see is building the most impressive “AI house on the block,” only to realize that no one wants to move in. The blueprint was flawless, and the craftsmanship was excellent, but the rooms, the flow of the space, even the light switches, don’t make sense to the people who are supposed to live there.

It’s vital to determine exactly what users want before anything gets built. In other words, it’s crucial to prototype the living spaces before pouring the foundation. 

If you’re navigating the complexity of AI and wondering where to go next, let’s talk.