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ADAPT - Future-Proofing

Can it evolve?

The AI landscape changes monthly. New models, new capabilities, new risks. A system that can't adapt will be obsolete before your contract expires. Future-proofing isn't optional—it's survival.

What "ADAPT" Means​

The ADAPT criterion evaluates whether a system can:

  1. Support New Models: Adopt newer, better AI models as they become available
  2. Enable Autonomy: Support agents, tool-calling, and advanced AI capabilities
  3. Use Standard Integrations: Work with common enterprise tools without custom dev
  4. Evolve Architecturally: Adapt to new AI patterns without complete rebuilds
  5. Scale with Usage: Handle growth in users, queries, and data volume
  6. Incorporate Feedback: Improve based on actual usage patterns

Why Future-Proofing Matters​

AI Evolution is Exponential, Not Linear​

Direct Version: GPT-3 to GPT-4 took 17 months. GPT-4 to Claude 3.5 took less. Models are improving faster than enterprise procurement cycles. If your vendor can't adopt new models easily, you'll be stuck running 2024 technology in 2026 while competitors use 2026 technology. That's not a competitive disadvantage—it's a death sentence.

Suitable for Work Version: The pace of AI model improvement exceeds traditional enterprise software evolution. Organizations require:

  • Ability to adopt superior models as they become available
  • Protection from vendor or model provider strategic changes
  • Architectural flexibility to incorporate new AI capabilities
  • Systems that improve rather than ossify over time

Autonomy is the Next Frontier​

Direct Version: Today's AI answers questions. Tomorrow's AI takes actions. If your vendor's architecture can't support agents, tool-calling, and autonomy, you're buying technology that's already being obsoleted. Don't invest in the past.

Suitable for Work Version: AI capabilities are rapidly expanding from retrieval and generation to autonomous action. Systems must support:

  • Agent-based architectures for multi-step tasks
  • Tool-calling and function execution
  • Safe autonomy with appropriate guardrails
  • Integration of AI decision-making into workflows

Lock-In Accelerates as Technology Changes​

Direct Version: Every year you stay on old technology while better options exist, switching costs increase. Your users learn the old way. Your processes adapt to old limitations. Your data accumulates in old formats. Future-proofing isn't about the future—it's about not being trapped by the past.

Suitable for Work Version: Technology debt compounds over time:

  • User training on outdated interfaces becomes embedded practice
  • Business processes optimize for system limitations rather than best practices
  • Data and configurations accumulate in vendor-specific formats
  • Competitive disadvantages widen as alternatives improve

What Good Adaptability Looks Like​

Excellent (Green)​

A vendor with strong adaptability provides:

✅ Multi-Model Support: Easy switching between models (Claude, GPT-4, Gemini, open-source)

✅ Rapid Model Updates: New models available within weeks of release

✅ Autonomy Ready: Supports agents, tool-calling, function execution

✅ Standard Integrations: Pre-built connectors for common enterprise systems (Slack, Salesforce, etc.)

✅ API-First Architecture: All functionality accessible programmatically

✅ Scalable Infrastructure: Handles 10x user growth without re-architecture

✅ Feedback Loops: Systematic improvement based on usage data

✅ Version Management: Smooth upgrades without breaking existing workflows

Example: "New models typically available 2-4 weeks after release. Support for Claude 3.5, GPT-4o, Gemini 1.5, and Llama 3. Agent framework supports multi-step tasks and tool-calling. 50+ pre-built integrations. API-first design. Infrastructure auto-scales to demand."

Acceptable with Caveats (Yellow)​

A vendor with partial adaptability:

⚠️ Supports 2-3 models but update cadence is slow (3-6 months)

⚠️ Basic agent support but limited tool-calling capabilities

⚠️ Some integrations available but may require custom development

⚠️ Architecture handles moderate growth but may need upgrades

⚠️ Feedback incorporated but improvement cycle is slow

Example: "We support GPT-4 and Claude, adding new models quarterly. Basic workflow automation available. 10 common integrations pre-built. Custom integrations via professional services. System scales to 10K users with current architecture."

Unacceptable (Red)​

A vendor with poor adaptability:

❌ Locked to single model or very slow to adopt new models

❌ No support for agents, tools, or autonomy

❌ Few integrations; most require expensive custom development

❌ Architecture can't scale or requires rebuilds for new capabilities

❌ No feedback mechanisms or improvement cycles

❌ Updates break existing workflows or require retraining

Example: "We use our proprietary model optimized for our system. We evaluate new models annually. Custom integrations available via services engagement. Current architecture supports up to 5K users. Major updates deployed annually."

Evaluation Questions​

When evaluating adaptability, ask:

Model Evolution​

  • Q: Which models do you currently support?
  • Q: How quickly do you adopt new models after release?
  • Q: Can I test new models before full rollout?
  • Q: What's your roadmap for model support?

Autonomy Capabilities​

  • Q: Do you support AI agents and multi-step workflows?
  • Q: Can AI call tools or execute functions?
  • Q: What guardrails exist for autonomous actions?
  • Q: Can I build custom tools or actions?

Integration Ecosystem​

  • Q: Which systems have pre-built integrations?
  • Q: How easy is it to build custom integrations?
  • Q: Do you support standard protocols (OAuth, SAML, webhooks)?
  • Q: What's your integration marketplace or partner ecosystem?

Architectural Flexibility​

  • Q: How does your architecture support new AI capabilities?
  • Q: What happens when you add major new features?
  • Q: Can you support capabilities that don't exist yet?
  • Q: Do updates require system downtime or retraining?

Scalability​

  • Q: What's your largest customer deployment (users, queries)?
  • Q: How does performance change with scale?
  • Q: What triggers need for infrastructure upgrades?
  • Q: Do you support multi-region or hybrid deployment?

Improvement Cycles​

  • Q: How do you incorporate user feedback into product development?
  • Q: What's your product release cadence?
  • Q: Can I influence your roadmap?
  • Q: Do you conduct quality improvement based on usage data?

Red Flags​

Watch out for vendors who:

🚩 Lock you to their "proprietary" model with no alternatives

🚩 Take 6+ months to support new models

🚩 Have no agent or autonomy capabilities and no roadmap

🚩 Require expensive professional services for basic integrations

🚩 Can't explain how their architecture will support future AI capabilities

🚩 Have scaling limitations that require re-architecture

🚩 Don't incorporate customer feedback into product development

🚩 Break existing workflows with every major update

Why Vendors Resist Adaptability​

What they say: "Our integrated solution is optimized for stability and performance."

What it often means:

  • Their architecture is brittle and can't easily adopt new capabilities
  • They're dependent on a specific model provider and can't switch easily
  • They want lock-in to prevent you from comparing to newer alternatives
  • They lack engineering resources to keep pace with AI evolution
  • Their business model depends on selling expensive migrations

The truth: "Stability" often means "we can't move fast." Fast-moving AI landscape requires adaptable systems, not frozen ones.

Best Practices for Procurement​

During Evaluation​

  1. Test Model Switching: Verify you can actually switch models easily
  2. Review Roadmap: Evaluate vendor's plans for new AI capabilities
  3. Check Integration Options: Test key integrations you'll need
  4. Assess Architecture: Have technical team evaluate system architecture
  5. Review Customer History: Ask references about vendor's pace of innovation

In Contracts​

  1. Guarantee Model Access: Contractual commitment to support major new models within X months
  2. Roadmap Commitments: Binding commitments for key capabilities on roadmap
  3. Integration SLAs: Performance standards for critical integrations
  4. Scale Guarantees: Defined performance at 2x, 5x, 10x current usage
  5. Update Policies: Limits on breaking changes, guarantee of backward compatibility

Post-Deployment​

  1. Monitor Model Updates: Track when new models become available vs. when vendor supports them
  2. Test New Capabilities: Pilot new features as they're released
  3. Review Roadmap Progress: Quarterly check on vendor's delivery vs. promises
  4. Plan for Scale: Model growth and verify infrastructure can support it
  5. Evaluate Alternatives: Annual review of competitive landscape

Real-World Impact​

Case Study: Model Lock-In​

Scenario: 2023 - Vendor built on GPT-3.5. 2024 - GPT-4, Claude 3, Gemini released with far superior capabilities. Vendor still only supports GPT-3.5.

With Adaptability: Customer switched to Claude 3.5 Sonnet in 2 days. Quality improved dramatically. Cost per query dropped 40%.

Without Adaptability: Customer stuck with 2023 technology while competitors used 2024 models. Quality gap widened. Contract renegotiation took 8 months. Lost market position.

Case Study: Autonomy Gap​

Scenario: Company needs AI to not just find information but take actions (create tickets, update CRM, schedule meetings).

With Autonomy Support: System already supported tool-calling. Added 6 custom tools in 2 weeks. AI assistants could complete full workflows autonomously.

Without Autonomy Support: Vendor's system only retrieved information. Required complete replacement with agentic system. Migration took 9 months. $500K investment lost.

Case Study: Integration Limitations​

Scenario: Company used 15 enterprise tools (Slack, Salesforce, Jira, Confluence, etc.). Needed AI to work across all of them.

With Standard Integrations: 12/15 tools had pre-built integrations. Built 3 custom integrations using standard webhooks in 1 week.

Without Standard Integrations: Vendor required professional services engagement for each integration. $50K and 6 months per integration. Project became economically unfeasible.

The Adaptability Spectrum​

Future-Proof (Best)​

  • Multi-model by design
  • Agent-native architecture
  • API-first with standard protocols
  • Infrastructure scales automatically
  • Continuous integration of new capabilities
  • Investment Protection: Maximum

Adaptable (Enterprise-Grade)​

  • 3-4 model options, updated quarterly
  • Basic autonomy support
  • Common integrations pre-built
  • Can scale to large deployments
  • Regular feature releases
  • Investment Protection: Strong

Limited Adaptability (Risky)​

  • 1-2 models, slow update cycle
  • No autonomy, limited workflow support
  • Few integrations, services-dependent
  • Scale limitations exist
  • Investment Protection: Moderate

Brittle (Avoid)​

  • Single model, no alternatives
  • No autonomy capabilities
  • Minimal integrations
  • Can't adapt to new AI patterns
  • Investment Protection: None

Key Takeaway​

AI moves fast. Your vendor must move faster.

In 2024, we got:

  • GPT-4o, GPT-4o mini
  • Claude 3.5 Sonnet
  • Gemini 1.5 Pro with 2M context
  • Llama 3, 3.1, 3.2
  • Widespread agent capabilities

That's 5+ generations of meaningful improvement in one year.

If your vendor takes 6 months to adopt new models, you're always running 6-month-old technology.

If they can't support agents, you're buying last year's AI architecture.

If they can't scale or integrate, you'll outgrow them faster than you think.

Don't buy systems that can't evolve. Buy systems built to adapt.

Next Steps​