Every AI vendor is promising transformation. The smart CIOs and COOs know that transformation only sticks when the Target Operating Model evolves with it.
Here’s the truth: most organizations are trying to bolt AI onto operating models designed for a pre-digital world. They’re adding intelligent automation to processes that were already broken. They’re deploying machine learning models into workflows where decision rights are unclear and feedback loops don’t exist. And then they wonder why the ROI isn’t materializing.
AI isn’t a feature. It’s a different way of operating. If your TOM doesn’t reflect that, you’re wasting money on tools that will never deliver their potential.
The Three TOM Dimensions Most Impacted by AI
At Hylaine, we’ve worked with financial services leaders who are getting this right. They’re not just implementing AI. They’re redesigning their operating models around three critical dimensions:
1. Decision Rights: In an AI-augmented world, who decides what? When does the model make the call, and when does a human override? In traditional TOMs, decision rights are mapped to roles and hierarchies. In AI-enabled TOMs, you need clarity on when algorithmic outputs drive action, when they inform human judgment, and when they’re simply not trusted enough to matter. The firms that execute well create explicit decision frameworks. Not as governance theater, but as practical guidance for teams operating under pressure.
2. Workflows: AI changes the sequence and speed of work. Processes that used to require three handoffs and two days can now happen in minutes with minimal human intervention. But only if you redesign the workflow to match. Too many organizations layer AI on top of legacy workflows and then wonder why they don’t see efficiency gains. The answer is simple: you’re still operating as if humans need to touch everything. Smart leaders are reengineering workflows to assume machine assistance, eliminate unnecessary handoffs, and focus human effort where judgment and creativity actually add value.
3. Performance Management: If your KPIs still measure activity instead of outcomes, you’re not ready for AI. Intelligent systems change what matters. Cycle time becomes more critical than headcount. Prediction accuracy becomes more important than processing volume. Customer experience becomes measurable in ways it never was before. The firms winning with AI are redefining performance management to reflect what they’re actually trying to achieve, and then using AI to measure and improve it in real time.
Where We See Leaders Stuck and How to Get Unstuck
The executives we work with face predictable challenges. They’re under pressure to show AI ROI, but their organizations aren’t structured to capture it. Their teams are skeptical about whether AI will replace them. Their governance structures slow down everything. Their data quality is inconsistent. Their vendor landscape is fragmented.
Getting unstuck requires discipline. It means starting with a clear assessment of where AI can actually create leverage in your operating model. It means investing in data quality before you deploy models. It means creating governance frameworks that enable speed, not bureaucracy. And it means bringing your people along. Not with vague change management, but with clear explanations of how their roles will evolve and why that matters.
What Hylaine Is Doing to Help Clients Build the Next-Gen Operating Model
At Hylaine, we don’t believe in one-size-fits-all AI strategies. We work with clients to design TOMs that reflect their specific regulatory environment, technology landscape, and business priorities. That means embedding with leadership teams to map decision rights, redesigning workflows to use intelligent automation where it matters, and building performance management frameworks that measure what actually drives value.
We’ve helped banking clients redesign credit decisioning workflows to incorporate machine learning while maintaining regulatory compliance. We’ve supported insurance firms in building claims operations that use AI to triage and route, freeing adjusters to focus on complex cases. We’ve worked with wealth management teams to create client engagement models that use predictive analytics to personalize at scale.
The common thread? We don’t start with the technology. We start with the operating model. Because AI is only as valuable as the organizational capacity to use it.
The Bottom Line
If you’re investing in AI but not rethinking your TOM, you’re setting yourself up for disappointment. The firms that will win in 2026 and beyond aren’t the ones with the most AI pilots. They’re the ones with the courage to redesign how they operate and the discipline to execute on that vision.
That’s the conversation we’re having with leaders who are serious about making AI work. Not as a science project, but as a core operating capability that changes how value gets created.
