Your Team Is More AI-Ready Than You Think
In the Superagency in the Workplace research, McKinsey found that employees are about three times more likely than their leaders assume to already be using AI for a meaningful part of their daily work. And while many leaders look to their people as the ones who need to catch up, employees themselves report feeling ready [1]. Across organisations, people are already experimenting, learning and finding their own uses for these new tools, often ahead of the formal strategies meant to guide them.
The report makes the interesting claim that employees are not the biggest barrier to scaling AI and fast changing technologies, but leaders might be the ones “not steering fast enough”. The most useful question becomes a simple one: how are your people already engaging, and what does each of them need to flourish within the organisation’s safety guardrails for an uplift in productivity?
Everyone embraces AI in their own way
Sit two capable colleagues in front of the same new tool and you will often see two different responses. One might immediately try a dozen things, while the other could be curious about what happens to data and accuracy. Each person in a team will notice a different facet their colleague will be glad of.
This is where knowing your leadership team’s approach to uncertainty, change, decision making and risk is important. Our QO2 profile helps surface this by looking at the balance between how readily someone sees opportunities and how readily they see obstacles, using five underlying scales. Each person sits on a spectrum of those scales where both ends bring something a team needs.
Moving Towards Goals energy is about drive. Give a team a new AI writing assistant and the colleague high in it will keep refining their prompts until they unlock a genuinely useful way to draft reports, opening up a method the whole team can borrow. The colleague at the other end reads sooner which tasks the tool is suited to and directs their energy there, so their effort goes to the work that gains the most from it. One persists, the other keeps the effort well placed.
Multi-Pathways is about options. Handed the same chatbot, the colleague high in it will discover a dozen uses, summarising meeting notes, drafting replies and shaping first ideas, with an inventive workaround whenever it stumbles, then pass the best of them on to everyone. The colleague lower in it brings focus instead, settling on the use that matters most to their role, perhaps turning rough notes into clear records, and becoming the person others learn it from. One brings breadth, the other depth.
Optimism shapes expectation. When the team takes on a new AI tool, the more optimistic will build the belief and energy that get people trying it in the first place, while the more measured person sets expectations everyone can rely on, so the team keeps checking the early outputs and feels the gains are real when they arrive. Hope and groundedness balance each other.
Fault-Finding is the ability to see potential obstacles. Before a document goes into an AI tool, the colleague strong in it will make sure everyone knows where the information goes, what has been assumed and how far the output can be trusted, which is exactly what lets the team use it with confidence. The colleague lighter in it keeps the momentum, drawing quick value from what the tool already does well today. One protects the plan, the other propels it.
Time Focus is about vision. Asked how AI fits the team, the future-minded will paint a picture of where it could take the department, giving everyone something to aim for, while the experience-minded ground that ambition in what reliably works now and what past rollouts have taught, so the path there is a sound one. Vision and experience need each other.
The opportunity-leaning end keeps things moving, the obstacle-leaning end keeps them sound and the richest leadership teams are aware of the importance of each end of the spectrum and deliberately cultivate them so there are no gaps.
Where leadership alignment makes the difference
McKinsey’s research surfaces a telling pattern. C-suite leaders are more than twice as likely to name their people’s readiness as a barrier to AI adoption as they are to look at their own leadership alignment. McKinsey’s own conclusion is that the biggest factor in successful adoption, for the highest organisational benefit, is leadership and that alignment among senior leaders cannot be assumed [1]. This is where executive team self-awareness and development has an important role to play.
The same rich variety of perspectives and approaches we see across a workforce lives within leadership teams. QO2‘s worldwide norm data puts the median score at 2.3 to 1, so the typical person sees a little over twice as many opportunities as obstacles [2]. Leadership teams often lean towards the opportunity end, which means they are in general more excited than the rest of the team about the possibilities that AI offers. The subtlety is that a shared sense of optimism can still mean very different things to different people.
For many senior leaders it shows up as a strategic, future-focused enthusiasm for where AI could take the organisation, when the wider organisation also needs an immediate, present-focused view of how to adopt it well right now.
Alignment is not about everyone feeling the same way. It is about seeing these differences clearly and drawing on the full range on purpose, so the team carries both the long view and the near one, both ambition and grounding.
A leadership team that understands its own QO2 opportunity and obstacle balance can align faster, speak to the organisation with one coherent voice and set a pace the whole business can follow for quicker, smoother and better regulated adoption right across the organisation.
From the leadership team to the whole organisation
A leadership team that understands its own balance already does much of the heavy lifting. When senior leaders can see where their collective enthusiasm and caution sit, they set a clearer pace, agree on the guardrails together and speak to the organisation with one voice. That alignment cascades and a great deal of healthy adoption follows from it.
The fuller picture comes from understanding the whole organisation, because AI adoption is really won or lost team by team and person by person. The support that helps people is the support that fits them and the evidence is clear. McKinsey found that 48 percent of employees would use AI tools more if they were given formal training, and 45 percent would use them more if the tools were built into their daily workflow. In its own rollout McKinsey went further, tailoring training to each group rather than offering one program to everyone [1].
Getting this wrong has a cost. A 2026 study found that productivity rose while people used three or fewer AI tools but fell away once they were juggling four or more. Oversight-heavy AI work brings measurably more mental fatigue and information overload and a third of those experiencing this actively looked to leave their position [3]. Knowing how different parts of the organisation sit lets you match the pace and the support to each team, so people get the gains without the overload.
This is where QO2 can be of further use. Its results can be brought together and anonymised, so opportunity/obstacle perspectives can be mapped by teams or departments. It shows the specific type of support that is needed and turns a one-size-fits-all rollout into something tailored, paced and safe. This supports AI adoption sustainability: people are supported at a pace that does not tax them, they get the most from the tools, and the rollout stays safe for the organisation.
The QO2 has been used to ensure the success of many transformation projects. The NGM Group (see the full case study here), took this whole-organisation approach to manage a significant transition. Group-level insights showed the People and Culture team exactly where to focus, so support was given where it was needed rather than spread thin.
Leading AI Adoption That Lasts
The picture McKinsey paints is encouraging once you read it to mean your people are not a blocker, they are a capability already in motion. The work of leadership is not to push them to catch up but to understand how each of them is already engaging, then set the pace and the guardrails that let that energy turn into productivity the whole organisation can rely on.
QO2 gives you a way to see that clearly. At the leadership level it shows whether your shared enthusiasm is the strategic, future-facing kind or the practical, present-facing kind. Identify where the gaps sit, so the executive team can align faster and speak to the business with one voice.
The next best step is understanding the specific support each part of the business needs. This allows a single uniform rollout to becomes something tailored, paced and safe. This is what protects people from overload and what keeps adoption sustainable rather than rushed.
The opportunity-leaning end keeps things moving and the obstacle-leaning end keeps them sound. Adoption succeeds when leaders draw on both on purpose, starting with their own balance and extending the same understanding team by team and person by person. That is how an organisation that is already more AI-ready than you think becomes an organisation that adopts at a pace it can actually carry.
References
[1] McKinsey & Company (2025). Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential at Work https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
[2] Team Management Systems. QO2 Profile Questionnaire: Worldwide Database Percentile Norms, Team Management Systems Research Manual (5th edition).
[3] Fortune (2026). AI “brain fry” is real, and it’s making workers more exhausted, not more productive. https://fortune.com/2026/03/10/ai-brain-fry-workplace-productivity-bcg-study/