1 AI doesn’t tend to fall apart because the technology can’t deliver.
It falls apart when enthusiasm runs ahead of clarity. Someone hears a great pitch, spots a clever feature, and moves forward before stopping to ask what actually needs fixing.
The five ideas that follow are about slowing that rush down. They focus on keeping AI practical, purposeful, and tied to the everyday realities of how work actually gets done – so it adds value where it matters, not just where it looks impressive.
2 Start With The Problem
Start with the problem, not the technology
Starting with the problem keeps AI grounded and useful, instead of something that just looks impressive on paper.
When businesses ask what’s actually slowing them down, annoying customers, or quietly costing money, AI suddenly has a job to do. It becomes a practical tool, not a shiny, trendy experiment hunting for a purpose.
3 Measurable Outcomes
Measurable outcomes are what stop AI from living in the “sounds good” category. Without clear benchmarks, it’s hard to tell whether a project is actually helping or just adding another layer of complexity to everyone’s day.
When success is defined upfront – time saved, costs reduced, business growth, happier customers – AI knows what it’s there for.
4 Clear metrics keep expectations grounded.
Teams can see progress, spot issues early, and adjust before small problems grow. Just as importantly, measurable outcomes make conversations easier with leadership. Results become visible, value is easier to justify, and decisions feel more confident.
5 Map AI Projects To Business Priorities
Mapping AI projects to real business priorities is what keeps them helpful instead of noisy. When AI is clearly linked to things the business already cares about – happier customers, lower costs, smoother operations, fewer risks – it becomes much easier to see whether it’s actually earning its keep.
Platforms like the ServiceNow AI platform really shine when they’re used with a bit of intention. Instead of switching everything on at once and hoping for the best, they work better when they’re aimed at a clear outcome – one real problem, one practical win at a time.
6 Involve Decision-Makers Early
Bringing decision-makers in early gives AI projects a sense of direction before they even get moving.
When leaders help shape the conversation from the outset, the focus naturally shifts to real outcomes instead of impressive features. The business problem comes first, not the technology.
It also removes a lot of friction. Decisions happen faster, teams don’t stall waiting for sign-off, and no one has to awkwardly retrofit an AI tool into plans it was never meant to support.
Most importantly, early leadership involvement builds confidence. When people see leaders engaged and clear on the purpose, buy-in feels natural, ownership is clearer, and results tend to stick.
7 Focus On Augmentation
Focusing on augmentation is about handling the dull, energy-draining jobs to AI so people can get back to the parts of work they’re actually good at. When repetitive tasks quietly fade in the background, mental space opens up.
Augmentation also builds trust in a very human way. People are far more comfortable with AI that feels like a helpful sidekick, not a quiet replacement plan. It keeps things consistent, flags small mistakes, and surfaces useful insights, while humans stay firmly in charge.
8 Final Thoughts
AI delivers value when it’s guided by purpose, not hype. Follow these five tips above and start with real problems, set clear goals, and focus on practical outcomes.
