How wide is your firm’s AI Velocity Gap?

Agentic AI will revolutionize individual productivity within 18 months, but your enterprise transformation is about to hit a wall. Tools like ChatGPT Agent, Google’s Project Mariner, and Perplexity’s Comet promise AI that can book reservations, manage workflows, and act autonomously. Yet the gap between what works for individuals and what scales across the enterprise is widening fast.

Enterprises’ ability to close their AI Velocity Gaps is reshaping how work gets done

Individuals are racing ahead, seamlessly weaving AI into their daily routines, while enterprises remain bogged down in bureaucracy, silos, and security debates. The result is what we call the “AI Velocity Gap,” a widening divide between AI-empowered employees who move fast and experiment freely while their organizations are still forming committees to decide what’s safe. In this AI-first era, the real productivity revolution is happening at the individual level, not the institutional one, as we highlight in Exhibit 1.

Exhibit 1: Individuals are becoming AI-empowered while enterprises fall behind

Source: HFS Research, 2026

Individuals will adopt agentic AI 10x faster than enterprises because the barriers are nonexistent

For knowledge workers, deploying agentic AI is trivial. You connect the agent to your Gmail, calendar, and OpenTable account, and you’re done. If it screws up your dinner reservation, you fix it. If it sends a weird email, you apologize. The experimentation is cheap, the stakes are manageable, and there’s no IT department blocking your access.

This is why productivity-obsessed individuals are already automating routine tasks like booking travel, summarizing documents, and managing follow-ups. The technology doesn’t need to be perfect; it just needs to save more time than it costs. And for workers drowning in administrative overhead, that bar is surprisingly low.

Your best employees are already using these tools. They’re becoming AI-augmented superhumans while your enterprise is still forming committees to discuss governance frameworks.

Your enterprise isn’t ready because it was never designed for autonomous AI

Deploying agentic AI in an enterprise isn’t a software implementation. It’s a complete business reinvention. There’s an extensive undertaking that requires enterprise leaders to consider the following:

• Unified data access across siloed systems. Your customer data lives in Salesforce, your financial data in SAP, and your operational data in ServiceNow. An AI agent needs to see what a human employee sees across all these systems simultaneously. Most enterprises don’t have this foundation.
• Standardized workflows that can be automated. Your business processes are tribal knowledge passed down through email chains and hallway conversations. Before an AI can handle expense reports or customer inquiries autonomously, you need to document, standardize, and make machine-readable every workflow. That’s a multi-year project.
• Security protocols that allow AI to act on behalf of employees. Your compliance team is still debating whether employees should paste confidential information into ChatGPT. Now imagine explaining that an AI agent will log into enterprise systems, move money, sign contracts, and communicate with customers. The organizational resistance isn’t technical. It’s cultural and political.
• Governance frameworks for when things go wrong. Who’s liable when the agent makes a mistake? How do you audit AI decisions? What happens when an agent acts on outdated information? You need a trust infrastructure that doesn’t exist yet.

This is admitting the uncomfortable truth: “Everybody is trying to build them, but right now they don’t actually work that well.” Individual users will tolerate imperfection and iterate quickly. Enterprises won’t and can’t.

The AI Velocity Gap will create winners and losers faster than you think

We’re entering a period in which individual and enterprise productivity diverge sharply. Small teams of AI-literate workers will compete with larger organizations still stuck in pilot purgatory.

AI-augmented individuals will outperform traditional teams. A consultant with agentic AI handling research, scheduling, and follow-ups can serve 3x as many clients as competitors still doing everything manually. They’ll win deals on speed alone. Meanwhile, enterprises will move cautiously while competitors move fast. You’ll run pilots, wait for vendor roadmaps, and debate liability questions while nimbler competitors figure it out through experimentation. By the time you’ve solved governance, the market will have moved.

The reinvention cost is higher than the technology cost. You’re not just buying software. You’re rebuilding workflows, retraining employees, and changing incentive structures that reward being busy rather than being productive. This is a transformation on par with moving from paper to digital or from on-premises to the cloud. It takes years, not quarters.

As our recent research across the Global 2000 reveals, the issue with agentic transformation isn’t the tech; it’s the archaic processes that are failing to produce better data to support decision making. It’s also the failure of leadership to train their people to rethink processes and be aware of the real business problems they are trying to solve. While so many stakeholders obsess over technical debt, the real change mandate is to address process, data, and people debt to exploit these wonderful technologies, as shown in Exhibit 2.

Exhibit 2: Enterprises must re-architect how people, data, and platforms connect to build a frictionless engine for growth

Sample: 305 major enterprise decision makers
Source: HFS Research Pulse, 2025

The irony? Enterprises have far more to gain from agentic AI than individuals do. The efficiency gains from automating routine processes at scale could be enormous, but you also have far more to lose if you get it wrong.

Enterprises must treat agentic AI as a business model transformation, and we’ve got the roadmap

Too many enterprises are treating agentic AI as just another technology deployment. In reality, it requires a complete business model transformation, including the following steps:

• Identify your AI-ready individuals and give them air cover. Your best people are already experimenting with these tools. Instead of blocking them with policy, learn from them. They’re your early warning system for what works.
• Pick one workflow and rebuild it from scratch. Don’t try to AI-enable everything. Choose one high-volume, standardized process, such as customer onboarding, vendor management, or expense processing, and redesign it, assuming AI agents will handle 80% of it. Learn what breaks.
• Build your trust infrastructure. You need audit trails, liability frameworks, and governance policies before you can scale. Start building them around small pilots rather than waiting for the perfect enterprise-wide solution.
• Accept that your competitors are making this bet too. The question isn’t whether agentic AI will transform work. It will. The question is whether you’ll lead the transformation or react to it after you’ve lost market share to faster-moving competitors.

The Bottom Line: Individuals will sprint, enterprises will stumble, and the AI Velocity Gap between them will define who wins.

The agentic AI revolution is real, but it’s going to roll out in two completely different timelines. Individual knowledge workers will see massive productivity gains in the next 12–18 months. Enterprises will spend the next 3–5 years figuring out governance, rebuilding workflows, and managing change.

The winners will be the individuals who adopt early and the enterprises that stop pretending this is another technology project. The losers will be the organizations that form yet another committee while their competitors reinvent their business models to take full advantage of AI.

Your employees are already becoming AI augmented. The question is whether your enterprise is willing to make the changes needed to catch up.