Before you hire for transformation, read this

Every organisation wants to move faster on AI. Most are stuck. So the job ad goes up. Here's what we would do first.

The pattern is familiar. Leadership sets the direction: we need to move on AI, and we need to move fast. Six months pass. The strategy deck still lives in SharePoint. The workflows haven't changed. The teams are busy. The moment feels urgent but nothing has actually shifted.

So comes the hire. Sometimes fixed-term. One to two years — there is an understanding that once the work is done, we should need fewer people, not more. So it's all a bit uncertain. It comes with a title that sits somewhere between "AI Lead," "Agile Delivery," "IT Innovation," "Digital Projects," "Transformation Manager," and "Change Analyst". The brief? Transform how we work.

Hiring to solve a problem is what we all know and do. But in this era, in practice, you're asking a lot, maybe too much, from one person. And you're asking it when executives no doubt have a belief that we should not need to add to headcount before we have understood what is possible.

The person you want is unlikely to apply

The unicorn job description goes something like this: AI-native, technically capable of building end-to-end solutions, able to sit with senior stakeholders and extract the messy reality of how work actually happens, skilled at educating others, fast-moving and commercially astute enough to prioritise what matters with exceptional analytical skills. Great with people — this is change management 101 after all. Explain complex things to people who aren't across it. And humble enough to give away everything they know to upskill the rest of the team.

To be brutally honest, there are very few engineers or analysts who have been exposed to what is possible with AI, with the responsibility to build it, and even fewer who are exceptional at supporting a melting pot of stakeholders who are navigating a state of uncertainty and change we haven't seen since the industrial revolution. Where they do exist, most likely, your fixed-term contract at $140k isn't what they're looking for.

This era demands the best talent to empathetically and technically drive change. Your team deserves that.

But let's say you find someone strong. Here are the risks you may run into.

The four risks nobody puts in the brief

Risk 1 / The experience gap

Most candidates can talk about AI transformation. Far fewer have actually rethought how work is done from the ground up and built the solutions to match. What you often get instead is a Power Automate flow and some new Power BI dashboards. Neither is wrong, but neither unlocks AI for your teams in the way you need. It's not their fault. Almost nobody has done this work at scale yet. The field is new.

Risk 2 / The lego problem

The best transformation happens when knowledge spreads, when the whole team understands how to think and work differently, not just the person you hired. But here's the uncomfortable truth: if your transformation hire is good at their job, they're building toward making themselves redundant. Not many people are genuinely excited by that prospect. Hoarding expertise, even unconsciously, is a natural response when your job security depends on being the one who knows things.

Risk 3 / The EQ requirement

If your processes were perfectly documented, clearly followed and up to date, this role would still be hard. But they're not, because no organisation's are. So before a single thing gets built, your transformation hire needs to surface the reality: the fears, the workarounds, the informal processes, the politics. They need to do that with enough EQ that people feel heard and enough discipline that they don't get stuck in stakeholder therapy for six months. This is a genuinely rare combination of skills in a technically capable person.

Risk 4 / Teaching at pace

For transformation to stick, this person needs to be an exceptional teacher. Not a trainer who runs workshops and disappears, but someone who can actively transfer capability to non-technical people while simultaneously doing the work and checking theirs. That's a skill set most technical people haven't been asked to develop and it's the thing that determines whether the organisation is different in 12 months, or just has some new tools one team uses.

If you can pull someone who genuinely has all of this — the technical depth, the EQ, the teaching instinct, the willingness to give away their lego — then go. They will do more in twelve months than you can imagine. Don't hesitate.

But if you can't, then hiring first is the wrong sequence.

The case for getting your house in order first

The more effective path is to maximise AI deployment and adoption within your existing team before adding headcount. Not because headcount is bad, but because the hire lands better and delivers faster when the groundwork is done.

This is where Sumday comes in. We sit with your team and do the work: surface what's actually happening, scope and prioritise the opportunities, build or implement the solutions, deploy them and actively upskill your people in the process. We give away the lego by design.

We do this without the knowledge-hoarding problem, without the EQ risk and without the twelve-month ramp. We're incentivised to leave your team more capable than we found them, not to make ourselves indispensable.

Until this is done, we caution organisations against hiring for the unicorn. Not because the role isn't valuable but because the conditions for that person to succeed aren't yet in place. That is not a people-first approach to hiring in the AI era. The pressure on them to demonstrate significant change that hits the bottom line is real; giving them the best chance to win is the right place to start.

Still want to hire?

If you still want to bring someone in, we still recommend undertaking the scoping, business cases for each AI use case and early experimenting before they land. Then they're stepping into a very different situation. There's a well-scoped list of priorities. There's a clear business case with evidence behind it that speaks to a CFO as much as the tech team. There's guidance on how to execute that's been tested in your actual environment. Three months of onboarding and ramping becomes a week. They're ready to run from day one.

In this era, we want to give existing teams the opportunity to understand, use and shape AI adoption before adding to headcount. That's the people-first approach to transformation.

Chat with us to learn more about our approach to AI transformation that unlocks better, faster decisions.