From experimentation to execution: Bridging the GenAI Divide

Over the past two years, organisations have poured billions into generative AI (GenAI), according to IDC.¹ Adoption is high. Most enterprises have experimented with AI tools or run pilots. According to the ServiceNow Enterprise AI Maturity Index, AI has increased the gross margin of 67% of organisations worldwide. Yet transformation is still elusive. Many pilots don’t make it into production—or fail to deliver measurable business impact.²

This growing gap between GenAI experimentation and GenAI at scale is often referred to as the GenAI Divide. On one side are proofs of concept and isolated use cases. On the other side, sustainable transformation is embedded into daily business operations.

Why do so many organisations get stuck? Not because of technology, infrastructure, or regulation. Many GenAI tools don’t integrate into workflows, don’t adapt to context, and don’t improve over time. Closing this divide requires a different mindset: workflow-first design, agentic systems that learn, and governance as the foundation for trust.

From pilots to workflow-driven transformation

GenAI tools can help individuals by drafting text, summarising content, and analysing data. But these productivity gains rarely translate into outcomes that impact the bottom line. The organisations that achieve real value focus on embedding GenAI into day-to-day workflows by:

• Processing procurement approvals faster
• Routing and resolving customer service requests end to end
• Accelerating HR case handling or IT operations

In these examples, GenAI is not a separate tool. It’s woven into the flow of work. That’s the critical difference between pilots that stall and solutions that scale.

Hypothesis-driven adoption

A common trap is running technology-first pilots, effectively testing what GenAI can do without linking it to a business challenge. A better approach is hypothesis-driven:

1. Define the challenge: What business outcome are you trying to improve?
2. Formulate a hypothesis: If AI is applied to this workflow, what is the measurable impact (e.g., fewer errors, reduced cycle time, better customer retention)?
3. Test in a contained environment: Keep the scope clear and manageable. Don’t make it too big and complex.
4. Learn from every experiment: Each test delivers information. Even when results fall short, you’ll gain insights to refine the next step, always linked back to the business goal.
5. Scale when proven: A successful proof of concept can evolve into production-ready workflows.

By repeating this cycle step by step, you’ll move closer to your business objectives. In this way, pilots can become building blocks for scaling AI responsibly. Think big, start small, act now, and learn fast.

Agentic AI: Addressing the learning gap

Non-agentic AI workflows are prone to challenges because they lack memory, context, and adaptability. Tools that require repeated setup or that fail in complex workflows rarely survive beyond pilots.

Agentic AI changes that. It remembers, adapts, and orchestrates workflows end to end. Instead of producing static outputs, AI agents learn over time and act with autonomy. Early adopters are already applying this in:

• Customer service, where AI agents resolve inquiries without escalation
• Finance, where routine approvals and reconciliations are automated
• Sales, where AI agents track engagement and trigger follow-ups automatically

Governance as the foundation for scale

Scaling AI is not just a technical challenge. It’s also a governance challenge. Executives consistently cite trust, data boundaries, and accountability as critical for adoption.² Without governance, AI risks becoming a “shadow economy” of unsanctioned tools used by employees without oversight. With governance, AI becomes a strategic asset: trusted, compliant, and aligned with business risk appetite.

Strong governance provides guardrails for:

• Monitoring model performance
• Helping to ensure ethical and responsible use
• Keeping data boundaries clear
• Enabling transparency and accountability

This is what allows leaders to scale AI responsibly.

The path forward

Bridging the GenAI Divide requires discipline, design, and vision. The organisations that succeed will:

• Stop investing in static pilots and focus on embedding AI into workflows
• Adopt agentic AI that learns, remembers, and adapts
• Apply a hypothesis-driven approach to experiment, learn, and scale step by step
• Make governance non-negotiable as the foundation for trust and scale

The front-runners will be those that combine ambition with discipline: thinking big, starting small, acting now, and learning fast.

Find out how ServiceNow can help you put AI to work for people.

¹ IDC, March 2025, Successfully Moving Enterprise GenAI from Proof of Concept to Production

² MIT, July 2025, The GenAI Divide, State of AI in Business 2025