We’ve already become accustomed to the cycle: A new “game-changing” AI model is announced. Anticipation (or dread, depending on your position) mounts. The model launches, and… it’s fine. Some features are improved. Others are lackluster. If the game has been changed, it hasn’t changed much.
Your org does not need another AI model hype cycle. It needs a dependable way to turn AI into better preparation, decisions, and coaching inside real workflows. When teams chase releases, they ship demos. When they build a stack, they ship results.
Map value to a stack
Keep your program focused on goals, not tools. Start at the top: Problems → Processes → People → Knowledge layer → Tools. Define the job to be done, the handoffs, and who reviews what. Only then, pick where AI helps. This shifts the discussion from “What can the model do?” to “What will our system reliably produce?”
Choose two use cases
This doesn’t mean you should ignore new iterations of AI tools. But don’t go “all in” on every new tool update. Instead, pick two high-frequency, low-risk workflows where better information and faster cycles matter and test new tools there. For most learning teams, that looks like:
- Manager 1:1 prep and follow-ups
- Onboarding paths for a key role
- Policy or benefits Q&A
- Sales call prep and debrief
Draft the input and output for each. Example: “Given last week’s 1:1 notes and team goals, produce a coaching plan with three questions and one follow-up task.”
Make knowledge a first-class layer
Outputs mirror inputs. Establish a trusted retrieval set that blends internal content with vetted external material. Set simple rules for source approval, freshness, and citation. If your assistant defaults to open-web search, you will ship speed without trust. If it defaults to curated sources, you will ship consistency. Tools like Microsoft Copilot integrate more cleanly when the knowledge layer is clear.
Tools like getAbstract’s Copilot Connector integrate directly into Microsoft 365 to ensure AI assistants retrieve from trusted, curated knowledge sets. That’s the shift from generic speed to specific, repeatable quality – especially in workflows like coaching, onboarding, or policy navigation.
Ship workflows, not prompts
Prompts are ingredients. Workflows are recipes. Capture the steps that turn a request into a reviewed output: retrieve, draft, show sources, review, revise, publish, and learn. Package that flow where people already work, whether that’s Slack, Teams, or your LMS. Reuse prompt chains only after they pass a review checklist.
Measure what leaders value
Swap novelty metrics for accountable ones. Track adoption, cycle time, output quality, cost per successful task, and rework avoided. Show a before and after for each pilot. LinkedIn’s Workplace Learning themes around business alignment are useful here; leaders back what they can see in the numbers.
A quick pilot plan
Week 1: Lock the two use cases and the retrieval set.
Weeks 2-3: Build a minimum viable workflow with a review checklist.
Weeks 4-5: Pilot with ten users, log issues, and fix the system.
Week 6: Report outcomes plainly and decide what to scale.
Close the model gap
New releases will keep coming. Treat them as accelerants, not strategies. L&D creates durable value by owning the stack: the problems, the processes, the knowledge layer, and the way work actually gets done. Build that, then let each model slot in and make it better.
Learn how getAbstract’s Copilot Connector can help turn your AI stack into a system that learns and scales by bringing curated knowledge directly into Microsoft 365.
