Last quarter, we finally completed our six-month marathon to deploy GitHub Copilot. The very next day, Claude Code launched. As I watched our ‘cutting-edge’ implementation instantly become yesterday’s news, a senior engineer asked me when we could start using the new tool. My painful answer? ‘We’ll need another six months to evaluate it.’ That’s when it hit me: our organizations aren’t just moving too slowly—they’re structurally incapable of keeping pace with AI advancement. And it’s costing us more than we realize.

The Acceleration Reality

The pace of AI advancement has broken free from the traditional technology adoption curve. Consider:

From GPT-3 to GPT-4 represented a 12-month leap that transformed the landscape. While companies were still evaluating basic use cases for the former, the latter arrived with capabilities that rendered those evaluations largely irrelevant. The same pattern is repeating with specialized AI coding assistants.

Meanwhile, our adoption processes remain anchored in a different era:

  • Security reviews: 3-4 weeks
  • Legal approval: 2-3 weeks
  • Procurement negotiation: 4-6 weeks
  • VP budget approval: 2-4 weeks
  • Pilot program: 4-6 weeks
  • Enterprise rollout: 4-8 weeks

These sequential processes add up to a minimum four-month cycle time—and that’s with constant pressure to prioritize. In our recent Windsurf evaluation, despite weekly status check-ins and escalations, we’re still months into the process without developer access. During this time, Claude Code emerged as potentially a better solution, but we’re organizationally committed to completing the Windsurf evaluation first.

The Hidden Tax of Process Inertia

The cost of this misalignment extends beyond mere timing issues. It creates:

1. A widening capability gap: While our developers are still working with GitHub Copilot, their counterparts at more nimble organizations are leveraging significantly more advanced tools, creating a multiplier effect on their productivity.

2. Engineering frustration: High-performing developers are acutely aware of what they’re missing. One senior developer recently told me, “I feel like I’m coding with one hand tied behind my back compared to what my friends at startups are using.”

3. Shadow AI adoption: The organizational impulse to restrict creates an equal and opposite reaction. Developers find workarounds, using personal accounts or unsanctioned tools, creating precisely the security risks our processes aim to prevent.

4. Decision paralysis: The speed of change creates a “why bother” mentality. “If we start evaluating Claude Code now, won’t something better come along before we finish?” This reasoning becomes a self-fulfilling prophecy of perpetual technology lag.

Reimagining Risk Management for the AI Era

The fundamental issue isn’t that our risk management frameworks are wrong—it’s that they were built for a different pace of change. Our current approach optimizes for managing known risks in a relatively stable environment. But we’re now operating in a landscape where:

  • The technical capabilities change monthly, not yearly
  • The competitive disadvantage of delay compounds exponentially
  • The “safest” option (waiting for maturity) becomes the riskiest strategy

This requires a fundamental shift in how we think about technology risk. Instead of our traditional “assess everything upfront” model, we need frameworks that:

1. Assume continuous evolution: Rather than approving a specific version of a tool, we need to approve categories of tools within guardrails, allowing for version upgrades without restarting the entire evaluation process.

2. Create parallel rather than sequential evaluations: Security, legal, and procurement teams need to work simultaneously rather than serially, with cross-functional working sessions replacing handoffs.

3. Implement tiered risk frameworks: Not all AI tools pose the same risk. Coding assistants that don’t transmit proprietary code externally carry different implications than tools processing sensitive customer data. Our processes should reflect these distinctions.

4. Shift from prevention to monitoring: Instead of trying to predict every possible risk scenario upfront, we need to create strong monitoring capabilities that can detect problems as they emerge, allowing for faster initial adoption with guardrails.

A New Operating Model

What might this look like in practice? Here’s a model we’re beginning to implement:

1. The 30-day fast track: For certain categories of tools (particularly developer productivity tools), we’ve assembled a cross-functional SWAT team that can complete initial evaluations in 30 days or less, with security, legal, and procurement working in parallel.

2. Continuous evaluation cycles: Rather than point-in-time approvals, we’re moving to quarterly re-evaluations of adopted AI tools, allowing us to both incorporate new capabilities and address emergent risks.

3. Capability-based approvals: Instead of approving specific vendors, we’re defining approved capability categories (e.g., “code generation tools that don’t transmit proprietary code externally”), allowing teams to adopt new tools that fit within established guardrails.

4. Risk-weighted adoption: Critical projects with high potential impact receive expedited evaluation paths, acknowledging that the cost of delay may outweigh the incremental security benefit of extended review.

When we implemented this framework for our recent Claude Code evaluation, we were able to compress our timeline from months to weeks. The key was reframing the conversation from “Is this tool perfect?” to “Is this tool within our acceptable risk parameters, and what monitoring do we need?”

Leading the Transition

Making this shift requires more than just process changes—it requires leadership at multiple levels:

With security and legal teams: The conversation can’t be adversarial. We need to acknowledge their legitimate concerns while helping them understand that in an accelerating environment, perfect security through delay creates larger organizational risks.

With executive leadership: The value proposition needs to be concrete. When we presented data showing a 26% productivity boost from upgraded AI coding tools, alongside evidence of our competitors’ adoption, the conversation shifted from “Why rush?” to “Why wait?”

With engineering teams: Setting expectations that with accelerated adoption comes increased responsibility. Teams that receive early access to new AI tools must commit to rigorous monitoring and reporting.

The most successful transitions happen when we recognize that everyone shares the same goal: enabling our organization to leverage powerful new capabilities while managing legitimate risks. The disagreement is rarely about the destination—it’s about the acceptable pace of the journey.

The Adaptation Imperative

Organizations that thrive in this new environment will be those that develop adaptation as a core capability. This means:

  • Hiring for adaptability alongside technical depth
  • Building flexible, parameter-based security frameworks rather than binary approvals
  • Creating organizational muscle memory for rapid evaluation and deployment
  • Developing leaders who can balance opportunity and risk in conditions of uncertainty

The choice we face isn’t between security and speed. It’s between security models designed for yesterday’s pace of change and security models designed for tomorrow’s. The organizations that figure this out first will gain a compounding advantage that becomes increasingly difficult to overcome.

The AI revolution won’t wait for our processes to catch up. It’s up to us to bring our processes in line with the new reality.

Our developers are watching what we do now. They’re forming impressions about whether we’re the kind of organization that can compete in this new landscape. We need to show them that we can adapt at the speed of AI—not because it’s convenient, but because it’s necessary.

The time to start is now. The gap is only widening.