Blue Cross Blue Shield of Michigan

Lori Dotson

AI Change Authority

Guiding organizational change while building a culture that embraces AI

I’ve been involved with and leading digital transformations for over a decade. The real challenge of driving transformation in a new AI world isn't about building the capability; it's about building the belief. People are always at the heart of change and if they don’t feel a part of it, it rarely takes hold. This doesn’t mean you have to wait for consensus; you just need to be intentional about where and how you start. Earning that willingness is the real work and you don't earn it with a mandate. Find people who feel the pain of the status quo, give them agency in the design and prove it out. Reducing pain and delivering wins are magnets for momentum. One thing I learned the hard way: from the start, make it safe to name what isn't working punishing honest failure just teaches people to report optimism instead of the truth. Culture is the bedrock of everything but can be interpreted as downstream of what you fund, measure and reward, because people read the real incentives, not the announcements. If you praise experimentation but resource only the sure things, people will see through it.

Helping employees adapt while maintaining trust, engagement and collaboration

I've found people adapt to AI best when they help shape the change and trust why it's happening. It's not as simple as rolling out tools and hoping your transformation takes hold. Trust comes from being honest about intent. If a role is changing, I'd rather say so plainly because engagement erodes faster from a suspected hidden agenda than from a hard truth. Encourage a mindset of curiosity so they are still forced to critically think about what they are trying to do with a tool. The real magic comes when you have a subject-matter expert who knows what they're looking for and has the right tools to do it better and faster. Their role shifts from producing the work to judging it, questioning what a tool hands back rather than accepting it. I try to lead as a visible user: if I'm asking thousands to change how they work, the least I can do is change how I work first, fumbles and all.

Balancing AI adoption with business priorities, workforce needs and responsible change management

Most prioritization challenges aren't really about what you build; they're about the sequence you build it in. It's tempting to build the shiny front door first, but a polished front end on a fragmented foundation produces low adoption and lost credibility. So much of responsible change management is actually about pace: build the unglamorous foundation and the highest-leverage internal use cases first, prove the value, then extend outward only as fast as people and guardrails can absorb. I treat measurement as load-bearing, not a reporting afterthought. Every initiative must answer who realizes the value and how we'll know, because funding activity nobody can measure is how portfolios quietly stall. The question of who realizes the value is also where transformations turn into a fight, so I separate the teams that build capability from the teams that consume it and book the value. It settles the two most expensive arguments in any transformation, everyone claiming the same savings and no one owning delivery.

  The real challenge of driving transformation in a new AI world isn't about building the capability, it's about building the belief.  

The most valuable leadership lessons in digital transformation and AI adoption

I think of transformation like a recipe with three key ingredients: the operating model, the reimagination of your core processes and experiences and the technology to bring them to life. The real skill is in the ratio, like any recipe, the ingredients matter, but getting the proportions right is what makes it work. The operating model is the foundation: how work gets funded, how teams form, where decisions sit. Get it right and you point your best people at what matters most. Reimagination means rethinking what a process should be rather than digitizing what exists, because technology aimed at the wrong process only automates the wrong things faster. Technology itself has changed, letting us do more than we could have imagined, but it only pays off when it's pointed at the right work inside an operating model that can sustain it. The most common mistake I've seen is getting that balance wrong, most often an operating model running ahead of the technology to support it. Measurement is what keeps the ratio honest. It shows whether the operating model, the reimagination and the technology are compounding or whether one is quietly holding the others back.

Preparing organizations for continuous AI-driven change

The word "transformation" implies a destination and with AI there isn't one. The models and the expectations keep moving, so the real goal isn't to finish, it's to build an organization that treats adaptation as normal instead of a crisis every time. Two things get you there. First, make change a repeatable engine rather than a heroic event, so every effort leaves behind a reusable model and the next one is faster and less dependent on any single leader. Second, invest in the data foundation ahead of the pain and make it a foundation of meaning. AI gets powerful when you set your company's context against your data, encoding what your terms mean and how they connect in a semantic layer, an ontology, so it answers from your organization's truth instead of a generic guess. It's the least visible investment you'll make and it quietly sets your ceiling.

But the component I'd flag as the hardest isn't technical. As AI makes producing content effortless, the scarce skill becomes interrogating it. I'm already watching generic, over-long output gets passed hand to hand, each handoff quietly pushing the judgment of what's actually true onto the next reader. When no one pushes back, a plausible-but-wrong answer spreads simply because it was easy to generate. So, the culture I want treats AI as a first draft, never a final answer. The scarce, human work is no longer making the thing, it's deciding whether the thing is any good.