AI has moved faster than any enterprise transformation I’ve experienced in more than 30 years in technology. Across industries, the conversation has shifted quickly from what AI can do to what it is delivering.

That shift matters. Many organizations have invested heavily in AI but remain caught in a cycle of pilots, proofs of concept and disconnected use cases. The technology is advancing rapidly, but leaders are still working through how to convert experimentation into outcomes that show up at the business level.

That is why the new playbook from the World Economic Forum (WEF), “The AI-First Operating System: A Blueprint for Operating and Business Model Innovation,” is so timely. I was proud to contribute to WEF’s AI-First Enterprise workstream, alongside leaders from across industries and academia, as we explored what it really means to become AI-first. The value of the workstream is that it is grounded in real operational experience — what companies are learning as they scale AI across workflows, teams and business models.

At Genesys, that focus aligns closely with our own AI-first transformation journey. We are not approaching AI as a set of disconnected tools, but instead using it to reimagine how work gets done, how decisions are made and how we create value for employees, customers and the business.

 

Moving Beyond AI-Enabled to AI-First

One distinction from my work with WEF that resonates strongly with me is the difference between being AI-enabled and being AI-first.

An AI-enabled organization applies AI to specific tasks within existing workflows. That can create meaningful productivity gains, but the operating model itself often remains the same.

But an AI-first organization goes further. It redesigns workflows, roles and decision-making around intelligence and automation. AI becomes embedded in the way the enterprise runs — not as an add-on, but as a core capability that shapes how the business creates value.

At Genesys, we did not rush into AI. That restraint was intentional. We treated the early phase as a laboratory: testing use cases, defining governance and building employee confidence before scaling. The goal was not to move slowly. It was to learn how to move fast without breaking trust.

We started with a simple question: Where can AI create measurable impact?

That meant identifying priority workflows where AI could improve outcomes for the business, for our employees and for our customers. This approach is also highlighted in the WEF playbook, which features Genesys as a case study in operationalizing AI through distributed execution — including how we inventoried more than 200 use cases and developed a standardized playbook to help teams identify and prioritize workflows, redesign work, evaluate solutions and measure value. Success would not come from launching hundreds of disconnected experiments. It would come from giving teams a shared model for redesigning high-value workflows from end to end.

 

Start with Outcomes, Not Tools

It is tempting to begin with the technology. What model should we use? Which platform should we deploy? Where can we add AI?

Those questions matter, but they should not come first. The better starting point is: What problem are we trying to solve, what outcome are we trying to improve and where does work create friction today?

Once the problem is clear, AI becomes a means to redesign work rather than another layer on top of it.

We are seeing this across several areas of Genesys. In pre-sales, creating product demos used to take significant time and coordination. With AI-powered tools, our solution consultants can now build personalized demos about 30% faster. That frees up more time for customer-facing work and allows teams to tailor each interaction more closely to a customer’s needs.

In our RFP process, teams previously spent hours sorting through complex customer requests to identify goals, requirements and questions. Now, an AI-powered assistant helps extract the most important details in minutes. That has accelerated time to a usable plan by about 35% and reduced repetitive work by as much as 90%, allowing teams to focus more on strategy and differentiation.

These examples are not just about efficiency. They show how AI can change the shape of work. When repetitive tasks are reduced, people can spend more time applying judgment, creativity and expertise.

 

Build Confidence Before Scale

Early in our transformation, we learned that governance, skills and data cannot be treated as downstream considerations. They are part of the operating model.

We also learned that good governance should create confidence, not paralysis. Early on, employees had understandable questions about what was permitted and how to experiment responsibly. Progress accelerated when we made the guidance simpler and clearer while preserving the foundational guardrails. The message became clearer: Responsible experimentation was not only allowed; it was essential to learning where AI could improve the way we work.

We have taken a layered approach to AI across the enterprise. We use major enterprise platforms as part of a broader strategy that balances buy, partner and build. Many solutions are role-specific — in customer support, finance, HR, sales and other workflows. At the same time, we wanted every employee to have the opportunity to benefit from generative AI as a shared capability.

That required practical scaffolding: education, governance, clear guidance and an ongoing focus on trust.

It also required us to use our own technology in the way our customers do. As our own “customer zero,” Genesys Product Support uses the same AI-powered CX platform we offer customers. By integrating capabilities like virtual agents and Genesys CloudTM Agent Copilot into everyday workflows, the team has found smarter and faster ways to support customers.

The impact has been significant: approximately 25% productivity savings and 157,000 cumulative working hours saved over three years.

 

Treat AI As a Teammate

The cultural shift is just as important as the operational shift. AI fundamentally changes how people think, work and make decisions. If organizations do not address the human side of change, AI can remain trapped in pockets of adoption. Tools may be available, but the culture does not shift.

At Genesys, we are focused on helping employees think about AI not simply as a tool, but as a teammate that can amplify human judgment. That does not mean replacing expertise. It means giving people intelligent support so they can work with more context, speed and confidence.

That confidence is already changing how people work. In one internal survey, 85% of employees said using generative AI improved the quality of their work.

This becomes even more important as agentic AI evolves. As AI systems take on more responsibility across workflows, people need trust in how those systems operate, clarity on where human oversight belongs and confidence that AI is being introduced responsibly.

This is why AI transformation requires close partnership between technology and people leaders. The CIO and CPO need to work in lockstep as joint architects of an AI-first enterprise, pairing platform readiness with workforce readiness. Technology leaders help ensure platforms, data and governance are ready. People leaders help redesign skills, roles, career paths and culture.

When those strategies move together, AI can scale responsibly with people at the center.

 

The Journey Is Only Beginning

The early proof points are encouraging. Across Genesys, we are seeing measurable impact in priority workflows, from faster demo creation and RFP planning to significant productivity savings in product support.

But the real value is not only in these individual results. It is in the repeatable model they represent: identify the workflow, redesign it around intelligence, measure the impact and scale what works.

AI has no finish line. Models will keep changing. Capabilities will keep advancing. Expectations will keep rising. But the foundations of successful transformation are becoming clearer: Organizations need to align around outcomes, build operating models where governance, skills and data are non-negotiable, and create cultures where people feel confident shaping AI, not simply reacting to it.

The World Economic Forum’s new publication offers a valuable blueprint for leaders navigating this shift. At Genesys, we are proud to have contributed to that work — and even more focused on applying these principles inside our own business.

The real opportunity is not to adopt AI for its own sake. It is to use AI to create better work for employees, better experiences for customers and better outcomes for the business.

 

Read the World Economic Forum white paper, “The AI-First Operating System: A Blueprint for Operating and Business Model Innovation,” to learn more about what it takes to move from AI experimentation to AI-first transformation.