Want to Ensure GenAI Success? First Fix Your Operating Models

GenAI has created a reckoning decades in the making, forcing data leaders to face some hard truths. The unaddressed problems of the data world – silos, quality, governance, and more – have surfaced with a vengeance during GenAI pilots and POCs.

GenAI efforts will languish without tackling these core issues. While we are seeing increased technology investment to address many of the data foundation gaps, one area that’s being severely neglected is the operating model.?

Operating Model – The “People” Part of the Equation?

The operating model is the most complex issue because there's no magic wand. It's about culture. It's about agility. It's about alignment on what is most valuable to the business.?

Filling operating model gaps requires a willingness and commitment to asking difficult questions that lack easy answers. How do we prioritize demand? How do we measure and report on value? How do we plan capacity??

Most organizations do have an operating model and overall OKRs (objectives and key results) in place. However, while Line of Business (LOB) and functional leaders certainly understand and acknowledge OKRs, they also have discrete priorities with finite budgets and resources. This reality has always created some misalignment between IT and the business.

That misalignment is only getting larger with GenAI. IT is usually funding GenAI projects out of existing budgets, so they focus on efficiency and cost savings. The business wants GenAI to help drive growth and innovation, and thinks IT is not listening to their needs.?

If leaders want their lofty expectations around GenAI to become reality, they need to get IT and the business on the same side of the table.

Breaking the IT/Business Gridlock??

This situation is like anything in life. A football club operating on separate game plans; an orchestra working from multiple music sheets; or a family vacation without a clear discussion of planned activities. The result is always conflict, frustration and missed opportunities.?

The IT/business misalignment in today’s organizations comes down to a few key issues: IT’s technology delivery vs. service mindset, a lack of shared responsibility/plan between IT and the business, and differing priorities that don’t ladder up to organizational OKRs.???

A reimagined/refined operating model can bridge each of these gaps. The OKRs help define the priority, potential value and the shared support/funding needed for a larger universe of GenAI user journeys & use cases. Use cases are prioritized based on value and within the overall context of user journeys and personas, not on any one LOB or IT’s discrete needs. Leaders from all LOBs and functions sign up for and are measured on the success of the implementation, knowing that their contribution to solving the business's biggest pain points is what's being measured. Here’s what it looks like in action:?

• Priorities and plans are revisited monthly to ensure alignment and rapid?response?to new/shifting market and business conditions as needed. The full range of stakeholders must commit to these meetings, including IT, Sales Ops, Product Development, Marketing Ops and HR.
• Leaders should be prepared to evolve the operating model over time. Most organizations that start with a centralized model will benefit from evaluating structure and processes for continued alignment with OKRs. This ensures agreement around ways of working and how the group defines change management needs.?This operating model over time may mature from centralized to federated models.
• It's important to continually evaluate funding models. Many organizations get bogged down in budget tug of wars due to misalignment on goals, OKRs and each department’s role in creating mutual success.

This approach also has an important and strategic byproduct. By focusing on prioritized use cases and user journeys, IT can more effectively design and create data products that fuel GenAI experiences for organization-wide impact.

A revised operating model focused on GenAI enables the entire organization to strategically and confidently invest in the right areas to move projects more quickly from pilot and experimentation into real-world deployments that drive value.