The next edge in AI performance is action

Three people are sitting at a table with a laptop and paperwork

Every quarter, it feels like a new “best” AI model emerges, offering the next phase of capabilities in reasoning, writing, and coding. But something else is happening too.

The AI performance disparity between the top models is shrinking fast. As benchmarks converge, prices are dropping. According to Gartner®, “LLMs in 2030 will be up to 100 times more cost-efficient than the earliest models of similar size developed in 2022.” 1

When intelligence is easily accessible, an AI model’s sophistication stops being the differentiator.

True AI value lies in the enterprise context that grounds AI in reality, the governance that keeps it safe, and the execution infrastructure that turns insight into action. AI without workflows is just expensive advice. The competitive advantage belongs to organisations that put AI to work inside workflows.

Fragmentation puts a ceiling on AI potential

Businesses often use technology to solve separate, localised problems. AI is scaled as a patchwork, built by different teams across disparate systems with different rules and no single control.

Data platforms, for example, help teams understand what they have. But any action based on those insights is typically executed through another disconnected system.

While standalone LLMs can suggest complex actions, they can’t orchestrate them across multiple systems. They lack persistent memory and any connection to the systems where work happens.

AI-assisted prototyping, or vibe coding, makes development fast and accessible to the non-technical workforce. But prototypes don’t create enterprise value. This comes from repeated iteration built on decades of accumulated business intelligence, from approvals to service-level agreements to embedded regulatory controls.

Even AI agents have limits. Ungoverned execution creates risk, so organisations tend to restrict deployment to trusted, siloed environments. Meanwhile, AI is bolted onto existing applications as shallow intelligence layered over disconnected data.

New technologies like these can deliver productivity gains, but in isolation, they won’t solve complex problems. Even the most powerful conversational AI can’t fix a cross-system payroll discrepancy or provision an employee across five systems, each with a unique approval chain.

A platform approach is the new differentiator

Most organisations are scaling AI on infrastructure that wasn’t built for it. According to the ServiceNow Enterprise AI Maturity Index 2026, just 16% of organisations worldwide have replaced fragmented systems with an integrated platform.

To move to the next chapter of enterprise AI, leaders must consider how to turn insights into action safely and at scale. This requires:

An AI platform brings together data, AI, workflows, and security in one place. This enables organisations to efficiently deploy and manage AI applications rather than relying on disjointed point solutions. It’s the difference between general-purpose AI that assists individuals and embedded, governed AI agents that run an enterprise.

The potential is massive. When AI agents are applied securely in workflows, they can handle the mundane tasks your people don’t want to do and give them back the autonomy to do the work they signed up for.

Doctors can rapidly generate clinical notes and dedicate more time to patient care. Security analysts can automate alert triage and focus on upskilling employees in cyber security. AI helps deliver greater value and work becomes more human.

Quote: "Even the most powerful conversational AI can't fix a cross-system payroll discrepancy or provision an employee across five systems, each with a unique approval chain"

Unlocking value with an AI control tower

Scaling autonomy requires a new level of control. You can't just give an autonomous workforce free rein over your systems. You need a way to manage performance and risk from a single control plane. That’s the value of ServiceNow as the AI control tower for business reinvention.

Its architecture comprises four interconnected capabilities: sense, decide, act, and secure. Together, they form the foundation to put AI to work at scale. Here’s an overview, based on the ServiceNow Blueprint for Agentic Business.

Sense: Most LLMs are trained on data from the internet, but AI needs enterprise context to understand your business. The ServiceNow® AI Platform connects to more than 350 systems, from enterprise resource planning (ERP) to customer relationship management (CRM), to contextualise your data in real time. This helps give AI agents a more complete view of clouds and assets to know what exists, how it connects, and what it means to the business.

Decide: Enterprise-level decision-making can’t be based on probabilistic guesswork. To safely deliver value, AI models must align decision-making with specific business rules, policies, and knowledge. Context Engine is designed to deliver that organisational intelligence, mapping the relationships between your people, policies, systems, assets, and past decisions. That allows it to surface what’s relevant at the moment an AI agent applies judgement and acts. The result is auditable, more predictable behaviour that can emulate the quality of decisions made by leaders.

Act: An Autonomous Workforce of AI specialists can execute end-to-end processes across IT, HR, and customer service, from automatic IT resolution to CRM record updates. When an out-of-the-box AI specialist or agentic workflow doesn’t exist, teams can develop new AI agents tailored for specific business functions.

Secure: Guardrails must be applied from the moment of action to help every AI system comply with both internal policies and external regulations. If an AI agent does something unexpected, you can pause, redirect, or stop it mid-action.

The AI landscape is moving fast, and intelligence is abundant. Next, organisations must surround that intelligence with business context, execution infrastructure, and governance that’s built in, not bolted on.

Find out how ServiceNow can help you take control of your AI.

1 Gartner Press Release, Gartner Predicts That by 2030, Performing Inference on an LLM With 1 Trillion Parameters Will Cost GenAI Providers Over 90% Less Than in 2025, 25 March 2026.

GARTNER is a trademark of Gartner, Inc. and/or its affiliates.