The questions that matter: A leader's guide to early-stage AI transformation

kelliabbott · yesterday

Introduction

Welcome back to our series of articles from the AI Center of Excellence (CoE) team at ServiceNow. Through countless advisory and hands-on engagements, we've gathered valuable insights and practical guidance that we're excited to share with the broader ServiceNow community. In our recent conversations with customers navigating their AI journeys, one theme emerges consistently: the leaders who succeed aren't necessarily the ones with the biggest budgets or the most technical expertise—they're the ones asking the right questions.

This article outlines:

The critical questions leaders should be asking their teams during early AI transformation
How to frame AI initiatives in ways that build momentum rather than resistance
Practical guidance for selecting the right projects, setting meaningful KPIs, and earning team buy-in
A framework for developing your own AI leadership capabilities—regardless of your title

Why the right questions change everything

Here's an uncomfortable truth: according to New Vantage Partners, fewer than 20% of executives report having established a data culture—despite 97% investing in data initiatives¹. Many teams are building AI solutions on foundations that can't support them, including inadequate data infrastructure that fails to properly feed AI systems. The difference between organizations that thrive with AI and those that struggle often comes down to whether leaders asked the right questions before writing the first line of code.

AI transformation isn't primarily a technical challenge—it's a change management challenge. The technology is only as good as your team's ability to adopt it, trust it, and integrate it into their daily work. That's why your role as a leader isn't to have all the answers; it's to ask the questions that surface the real barriers, align your team around shared goals, and create the conditions for success.

Frame the change: Questions that build alignment

Before evaluating tool selection or technical architecture, successful AI leaders shape the narrative around why this transformation matters. Framing is about connecting AI initiatives to what your organization already values—whether that's customer experience, operational excellence, or innovation. When people understand why AI matters to them personally, resistance gives way to engagement.

Questions to ask your team

"How does this AI initiative reinforce what we already do best?" - This question grounds AI in your organization's existing strengths rather than positioning it as a disruptive threat.
"What would success look like for your role specifically?" - AI affects different functions differently. Understanding individual perspectives helps you address concerns before they become roadblocks.
"Where in your workflow do you spend time on tasks that feel like they should be easier?" - This surfaces real pain points that AI can address, creating natural advocates for adoption.
"What concerns do you have that we haven't addressed yet?" - Creating psychological safety for honest feedback prevents underground resistance.

The goal of framing isn't to sell AI to your team—it's to co-create a vision where AI enhances rather than threatens their professional identity. People who feel ownership over the transformation become your strongest champions.

Structure for success: Questions that build your foundation

Once you've established the "why," you need to build the operational infrastructure to support execution. This means being deliberate about time, accountability, and collaboration structures—and being honest about where gaps exist.

Questions to ask your team

"Do we have protected time for learning and experimentation, or will this compete with existing priorities?" - Without dedicated time, teams default to old habits under pressure.
"Who owns the outcome of this initiative, and is that clear to everyone?" - Ambiguous accountability kills AI projects.
"What cross-functional collaboration does this require, and have we built those bridges?" - AI initiatives often require data scientists, domain experts, and engineers to work together in new ways.
"What barriers exist to data sharing across departments?" - Data silos are one of the most common (and underestimated) obstacles to AI success.

A note on organizational models

Organizations typically choose between two structural approaches for AI teams. In a service-oriented model, data teams report to functional departments (like marketing or operations)—this works well for incremental improvements but can limit transformative potential. In an executive-led model, data teams report to the C-suite—this enables transformative innovation but requires strong executive sponsorship. Neither is universally "right"; the key is intentionally choosing the model that matches your ambitions and then structuring accordingly.

Select the right projects: Questions that maximize early wins

Not all AI projects are created equal, especially in the early stages. The projects you choose to pursue first will either build momentum and credibility or create skepticism that's hard to overcome. Smart project selection is one of the highest-impact decisions you'll make.

Questions to ask before committing

"Does this project address a real pain point that people actually care about?" - Technical elegance means nothing if the solution doesn't solve a problem people feel.
"Can we demonstrate value within 90 days?" - Early-stage projects should build credibility quickly. Save the multi-year transformations for after you've proven the concept.
"Do we have the data we need, and is it accessible?" - The most common reason AI projects fail isn't the algorithm—it's the data. More on this to come!
"What's the risk if the AI makes a mistake?" - Start with use cases where errors are recoverable. High-stakes decisions should wait until you've built both capability and trust.
"Will this project create advocates or skeptics?" - Choose projects that touch people who will champion success—their enthusiasm is contagious.

Set meaningful KPIs: Questions that measure real progress

Measuring AI success requires new leadership skills. Traditional metrics often miss what matters most in early-stage transformation: learning, adoption, and capability building.

Questions to guide your measurement strategy

"What would convince a skeptic that this is working?" - This forces you to identify evidence that matters to your toughest critics.
"Are we measuring adoption or just availability?" - A tool that exists but isn't used delivers zero value. Track usage, not just deployment.
"What are we learning, and how are we capturing those lessons?" - In early stages, speed to learning matters more than speed to delivery.
"Can we articulate the ROI in terms executives care about?" - Time savings, error reduction, and customer satisfaction translate better than technical metrics.
"What trade-offs are we making, and do all stakeholders understand them?" - Every AI decision involves trade-offs between accuracy, speed, cost, and risk. Transparent discussion prevents later surprises.

Earn buy-in: Questions that address the human side

Resistance to AI often isn't about the technology—it's about fear. Fear of job loss. Fear of losing control. Fear of being made irrelevant. The most effective AI leaders address these human concerns directly rather than dismissing them.

Questions to build trust

"How can we position this as augmentation rather than replacement?" - The most successful AI implementations enhance human capabilities rather than automating humans away.
"What new skills will our team need, and how will we help them develop those skills?" - Investing in reskilling demonstrates commitment to your people's futures.
"How are we involving end users in the development process?" - People support what they help create. Early involvement transforms potential resisters into co-designers.
"Where should humans maintain decision authority?" - Clear boundaries about human oversight build trust and ensure accountability.
"What feedback mechanisms exist for people to raise concerns or suggest improvements?" - Continuous feedback loops signal that you value human judgment alongside AI capabilities.

Putting it all together

Early-stage AI transformation succeeds when leaders focus less on having answers and more on asking questions that surface truth, build alignment, and create conditions for learning. The questions in this article aren't exhaustive—they're starting points designed to spark the conversations that matter.

Key takeaways:

Frame AI as enhancement, not threat. Connect initiatives to existing values and help people see themselves in the transformation.
Structure deliberately. Protected time, clear accountability, and cross-functional collaboration don't happen by accident.
Choose projects strategically. Early wins build momentum; early failures create skepticism that's hard to overcome.
Measure what matters. Adoption, learning, and capability building matter more than technical metrics in early stages.
Address fear directly. Human concerns deserve human responses—not dismissal.
Develop yourself. Leadership at any level means continuously growing your own capabilities.

Conclusion

The organizations that succeed with AI transformation won't be the ones with the most sophisticated algorithms or the largest data sets. They'll be the ones led by people—at every level—who ask the right questions, listen to the answers, and create environments where learning and experimentation can flourish.

Your next step: Choose one section from this article that resonates most with your current challenges. This week, schedule 30 minutes with your team to discuss just those questions. Document what you learn. Small, consistent conversations create the foundation for successful transformation.

If you have questions or thoughts, feel free to drop them in the comments—we'll respond or update the article as needed. If you found this article helpful, please share your feedback or link to it on your preferred platform. This is just one part of our series on AI—stay tuned for more.

For tailored guidance, reach out to your ServiceNow account team.

PS: Views are my own, and do not represent my team, employer, partners, or customers.

https://www.businesswire.com/news/home/20220103005036/en/NewVantage-Partners-Releases-2022-Data-And-...