AI adoption
Hire for discernment when everyone has the same AI tools
Trehan Stenton argues that judgment, curiosity and emotional intelligence now separate the advisers, hires and leaders who all use the same AI tools.
AI Adoption Is a Process Problem, Not a Tooling Problem (feat. Trehan Stenton)
Video from Snowpal, Inc..
Generated from the canonical interview transcript and validated against source data by Guests on Air.
What skill matters most when everyone has the same AI tools?
Discernment matters most. When clients, consultants and new hires all use the same AI tools, the advantage moves to the person who questions the output, checks the data and senses when something is wrong. Trehan Stenton would pay more for a curious, questioning mind, and expects emotional intelligence to count for more than raw intelligence as AI takes on routine work.
Trehan Stenton, founder of AI Integration Partner, helps growing and multi-location businesses turn strategy into process, then use AI and change management to make it stick. On the Snowpal podcast, the conversation turned to a question many leaders now face: if everyone can use the same AI tools, where does a person, an adviser or a new hire still add value?
The answer Stenton keeps returning to is discernment. Tools can now take a draft, a deck or a plan most of the way, but someone still has to judge whether the result is right, whether the data behind it is clean and whether it serves the direction the business has chosen. That judgment, Stenton argues, is becoming scarcer and more valuable.
This article covers three parts of that argument: why advisers must move up the value chain when clients hold the same tools, why the best hire may be the person who asks better questions, and why emotional intelligence, rather than raw intelligence, will separate good leaders as AI takes on more of the routine work.
Key takeaways
- When clients and advisers share the same AI tools, the value shifts to time, discipline and the discernment to judge the output.
- Only about a third of organizations adopting AI rework the underlying process; most just try to make the old one faster.
- Pay more for a curious, questioning mind that validates what the tools produce instead of assuming it is correct.
- As AI handles routine output, intuition, empathy and clear communication become the skills that set leaders apart.
1. Same tools, different judgment
Consulting used to rest on a simple gap. Clients had business skills but lacked engineering or IT expertise, so they paid outsiders to fill it. Now both sides use the same chat assistants and coding tools. Trehan Stenton accepts that a consultant who simply pushes out a document has a short shelf life, because clients can increasingly produce that document themselves.
What remains, in Stenton's view, is time, discipline and perspective. Many clients have the creativity to change but get pulled back into running the business. An adviser who agrees a timeline, measures progress and owns outcomes can see the work through. Just as important is a view of what is happening across the industry, rather than only how the client has always worked.
That outside view matters because, by Stenton's account, only about 35 percent of organizations implementing AI actually rework the baseline process. The rest keep the process they have used for years and ask AI to make it a little quicker. Stenton would rather teach clients to map their own processes and find their own bottlenecks, and suggests consulting may come to be paid for outcomes rather than hours.
2. Hire the person who asks better questions
The same logic applies to hiring. A newcomer with little experience may lean heavily on AI tools, while a seasoned specialist costs more but needs less help from them. Asked where the value sits on that spectrum, Stenton starts with the process the role supports: what needs automating, where AI genuinely helps and what skills the work really demands.
Wherever the role sits, the person must not assume that whatever the tools produce is correct. Stenton would pay more for someone with a curious, questioning mind who knows the data well enough to validate an answer and add to it. For a manufacturing client building agents, the priority is a strong analyst who can read the numbers and turn them into recommendations.
Governance has to arrive with that capability. Stenton cited one large technology company that let every employee loose on generative AI and used three months of tokens in a single week without producing the right outcome. The lesson is to pick the right people, make sure they understand the limits of the job and be clear about what the work should deliver.
3. Why emotional intelligence will count for more
Discernment is also about pace. Decisions that once took weeks to think through now arrive faster, because a chat assistant can get an answer most of the way there in minutes. Stenton notes that people still have to add their own reasoning to that last stretch, and warns that faster decision cycles leave less time for the reflection good judgment needs.
For that reason, Stenton expects the most valuable skills to sit in emotional intelligence rather than raw intelligence. An experienced leader can often sense that a profit and loss statement looks wrong just by reading it. Sensing problems inside an organization, explaining a vision clearly and bringing a team through change are things a generative tool cannot do for anyone.
Stenton also sees specialists becoming more like generalists. People who understand the whole customer journey, and how a change in one area lands in another, will add more than those working in a single lane. For growing businesses adopting AI, that means investing in curiosity, judgment and soft skills alongside the tools, because the tools alone will not set anyone apart.
