- Building AI isn’t the hard part. Most companies still struggle to get it trusted, adopted, and embedded into daily work.
- “AI” is now taking two tracks inside organizations: GenAI for knowledge work and agentic AI that takes actions across systems, which raises the bar for governance and integration.
- To get real ROI, companies need to pick the right problems and establish clear goals early.
What has changed since last March is the speed at which “AI” has become two very different things inside companies:
- GenAI that drafts, summarizes, and accelerates knowledge work
- Agentic AI that acts by using tools, touching systems, and chaining decisions together
These two distinct tracks, along with my work at PivotX Advisors, offer a unique opportunity to reexamine how companies can get real value from AI. And if you want real value, not just impressive MVPs, you need a strategy that moves from Explore to Value. That means choosing the right problems, designing work differently, and instrumenting outcomes so you can prove what changed.
Here are the updated best practices I’d stand behind in 2026.
1) Build a Dedicated AI Team with CXO Commitment
AI adoption needs executive sponsorship. A dedicated team with clear leadership support keeps efforts aligned to business goals and prevents “random acts of AI.”
2) Create a Clear Roadmap for AI Adoption
A roadmap should show how you move from pilots to production. Without a graduation path, MVPs pile up and value never compounds. In procurement work we’ve completed, the demo wasn’t the challenge. The hard part was mapping the steps needed to embed the agent into the actual process so it became part of how work gets done.
3) Start with a Value-Led Approach
Rather than starting with AI technology, map the real workflow and user journey first. From there, pressure-test it against the data you have. We use what we call the SPARK framework at PivotX (Scrutinize processes, Personalize journeys, Analyze data, Recognize opportunities, and Katalog/prioritize use cases) to use the discovery process to home in on the right use cases to invest in before building anything. This was instrumental in setting us up for success as we began working with a highly regulated life sciences workflow that involves approvals, documentation, stakeholder engagement, and fee-for-service processes. Here, mapping the real operational journey was far more valuable than forcing AI into the wrong problem first.
4) Assess AI Readiness
Before implementation, check:
- Business Awareness: Do teams understand where AI helps vs. hurts?
- Business Value Clarity: Do you know what outcome you’re improving?
- Data Readiness: Do you have the right data and access?
- Talent Availability: Do you have people who can build and run it?
5) Ensure Business Value Before Investing in AI
Prioritize projects with clear ROI. If ROI is hard to calculate, proceed only when qualitative benefits are compelling—and you can still define leading indicators. Don’t force fake ROI math.
6) Use a System-Thinking Approach Aligned with Strategy
AI should support the broader business strategy. Treating it as a stand-alone tool creates point solutions that don’t scale.
7) Communicate AI Success Internally
Trust spreads through proof. Share specific wins (cycle time, cost, quality, risk reduction) so employees see what AI is actually doing.
8) Senior Leaders Should Drive AI Adoption
When leaders promote AI and use it themselves, adoption rises. Leadership behavior signals whether AI is real or optional.
9) Train Employees with Role-Based AI Courses
Train by role and workflow. Generic AI training builds awareness, but role-based training drives correct usage.
10) Embed AI into Business Processes
AI needs to live inside the tools and workflows people already use. If it sits beside the work, usage fades after the novelty wears off. In a recent procurement workflow we worked on, value didn’t show up in the demo. It showed up once agents were embedded directly into the flow, managing intake, policy checks, due diligence, and payment terms, rather than living in a separate “AI tool” someone had to remember to open.
11) Continuously Improve AI Solutions
Monitor performance, collect feedback, and iterate. This matters even more for agentic AI, where reliability depends on how it behaves across edge cases.
12) Build Trust in AI Among Employees
Skepticism is normal. Clear policies, transparency, and training help employees understand reliability, boundaries, and accountability.
13) Create a Compelling Change Story for AI Adoption
Explain why AI matters in practical terms: what changes in day-to-day work, what gets easier, and what stays human-owned.
14) Track Key AI Performance Indicators (KPIs)
Define KPIs tied to outcomes: productivity, cost, speed, quality, customer impact, risk reduction. If you can’t measure it, you can’t prove value. In procurement workflows we’ve supported, AI either earns the right to scale or gets stuck based on the basics like cycle time, exception rates, policy compliance, and time saved per request.
15) Establish an Operating Model for AI
An operating model is the bridge from strategy to execution. Centralized, decentralized, or hybrid can all work—what matters is clear ownership for governance, deployment, risk, and ongoing performance.
Final Thoughts
AI creates impact when it’s planned, adopted, and operationalized—not just built. In 2026, the differentiator is whether you can move from Explore to Value by embedding AI into real work and proving outcomes with clarity.
