Market Signal

85% of researchers say automated tools have already improved their workflow — primarily by saving time and enabling faster delivery. Automation now supports every stage of the research process, from brief sharpening to survey design, fieldwork, and synthesis. The execution layer is largely solved.

What remains unsolved — and increasingly urgent — is interpretation.

McKinsey’s 2026 Skill Change Index makes the argument directly: AI will not render human judgment obsolete. It will reshape how it is applied. The highest-value work in 2026 is no longer generating data. It is framing the right questions, interpreting ambiguous findings, communicating insight to stakeholders, and validating AI outputs against organisational reality.

Meanwhile, the industry is naming the shift itself. Brands are now expecting research partners who can operate in the blended space of strategy and insight — comfortable with data, fluent in commercial context, able to translate findings into decisions. Insight teams are building capabilities in narrative framing, behavioural interpretation, and strategic translation. The research project manager and the strategic consultant are no longer interchangeable roles. The premium is moving decisively toward the second.

Frontline

A head of insight at a consumer brand, reflecting on how her team’s remit has changed in the past eighteen months:

“We used to spend most of our time on the mechanics — designing studies, managing suppliers, quality-checking outputs. Now the platforms do most of that. What I need from my team is something harder. I need them to sit in a room with the commercial director and tell him what the data actually means for his decision. Not present it. Interpret it. That’s a completely different skill — and not everyone has made the transition.”

This is the gap the edition is about. Not between those who use AI and those who don’t. Between those who can tell you what the data shows and those who can tell you what to do about it.

Sharp Skill: Interpretive Courage

Interpretation is not just an analytical capability. It is a professional posture — and it requires something that no tool can supply: the willingness to take a position.

The research and strategy profession has long rewarded neutrality. Present the findings. Let the data speak. Offer options rather than recommendations. This posture made sense when the risk of being wrong was career-limiting and the value of the researcher lay in their methodological rigour. It makes less sense when AI can produce methodologically rigorous outputs at scale and what organisations actually need is someone willing to say what those outputs mean.

There is a documented pressure working against this. When AI produces a confident, well-structured output, the professional who disagrees with it faces a specific career risk: being seen to override the algorithm is uncomfortable, and deferring to it feels safer. The result is that interpretation — genuine, situated, commercially-aware interpretation — gets quietly replaced by AI-endorsed summary. The findings look sharp. The thinking behind them has been outsourced.

Three moves that build interpretive capability:

1. Separate what the data shows from what it means. Make it a discipline, not an assumption. “The data shows X” and “this means Y for your situation” are two distinct statements. Train yourself to make them explicitly, in that order, every time. The second statement is yours. Own it.

2. Know the commercial context before you know the findings. Interpretation that lands is always situated. What pressure is the commercial director under? What decision is actually on the table? What would change the outcome? The strategist who walks into the debrief knowing the answers to those questions interprets differently — and more usefully — than one who arrives with only the data.

3. Make a recommendation, not a menu. “Here are three possible interpretations” is a research deliverable. “Here is what I think this means, and here is what I would do” is a consulting one. The second is harder, more exposed, and significantly more valuable. If you are consistently offering options rather than recommendations, ask yourself honestly whether that is intellectual rigour or professional self-protection.

Case in Point

The split your audience is living through is not theoretical. It is showing up in how roles are being structured and what is commanding a premium in the market right now.

The research project management layer — scoping, commissioning, supplier management, delivery — is being absorbed into platforms and automated workflows. The organisations investing in headcount are doing so for a different capability: the ability to sit with a client before the brief is written, understand the commercial context, interpret findings in light of organisational reality, and take a clear position on what should happen next.

This is not a new distinction. The difference between a research project manager and a strategic consultant has always existed. What is new is the speed at which the first role is being automated and the premium being placed on the second. The strategists and researchers who thrive in this environment will not be those who executed the most studies. They will be those who learned to interpret — and had the courage to say so out loud.

Closing Thought

AI tells you what happened. Interpretation tells you what to do about it.

For most of the profession’s history, the first part was the hard part — gathering the data, running the study, producing the output. The second part was assumed to follow naturally from sufficient rigour and experience.

That assumption no longer holds. Execution is fast, cheap, and increasingly automated. Interpretation is scarce, situated, and irreducibly human. The gap between those two things is where the next generation of strategic value will be built.

The question is not whether you can produce the findings. It is whether you are willing to stand behind what they mean.

That is the sharper edge.

The Sharp End is a monthly field guide for strategists, researchers, and insight leaders. If this edition resonated, share it with someone who is ready to move from presenting findings to owning what they mean.

Not yet subscribed?

Market Signal

85% of researchers say automated tools have already improved their workflow — primarily by saving time and enabling faster delivery. Automation now supports every stage of the research process, from brief sharpening to survey design, fieldwork, and synthesis. The execution layer is largely solved.

What remains unsolved — and increasingly urgent — is interpretation.

McKinsey’s 2026 Skill Change Index makes the argument directly: AI will not render human judgment obsolete. It will reshape how it is applied. The highest-value work in 2026 is no longer generating data. It is framing the right questions, interpreting ambiguous findings, communicating insight to stakeholders, and validating AI outputs against organisational reality.

Meanwhile, the industry is naming the shift itself. Brands are now expecting research partners who can operate in the blended space of strategy and insight — comfortable with data, fluent in commercial context, able to translate findings into decisions. Insight teams are building capabilities in narrative framing, behavioural interpretation, and strategic translation. The research project manager and the strategic consultant are no longer interchangeable roles. The premium is moving decisively toward the second.

Frontline

A head of insight at a consumer brand, reflecting on how her team’s remit has changed in the past eighteen months:

“We used to spend most of our time on the mechanics — designing studies, managing suppliers, quality-checking outputs. Now the platforms do most of that. What I need from my team is something harder. I need them to sit in a room with the commercial director and tell him what the data actually means for his decision. Not present it. Interpret it. That’s a completely different skill — and not everyone has made the transition.”

This is the gap the edition is about. Not between those who use AI and those who don’t. Between those who can tell you what the data shows and those who can tell you what to do about it.

Sharp Skill: Interpretive Courage

Interpretation is not just an analytical capability. It is a professional posture — and it requires something that no tool can supply: the willingness to take a position.

The research and strategy profession has long rewarded neutrality. Present the findings. Let the data speak. Offer options rather than recommendations. This posture made sense when the risk of being wrong was career-limiting and the value of the researcher lay in their methodological rigour. It makes less sense when AI can produce methodologically rigorous outputs at scale and what organisations actually need is someone willing to say what those outputs mean.

There is a documented pressure working against this. When AI produces a confident, well-structured output, the professional who disagrees with it faces a specific career risk: being seen to override the algorithm is uncomfortable, and deferring to it feels safer. The result is that interpretation — genuine, situated, commercially-aware interpretation — gets quietly replaced by AI-endorsed summary. The findings look sharp. The thinking behind them has been outsourced.

Three moves that build interpretive capability:

1. Separate what the data shows from what it means. Make it a discipline, not an assumption. “The data shows X” and “this means Y for your situation” are two distinct statements. Train yourself to make them explicitly, in that order, every time. The second statement is yours. Own it.

2. Know the commercial context before you know the findings. Interpretation that lands is always situated. What pressure is the commercial director under? What decision is actually on the table? What would change the outcome? The strategist who walks into the debrief knowing the answers to those questions interprets differently — and more usefully — than one who arrives with only the data.

3. Make a recommendation, not a menu. “Here are three possible interpretations” is a research deliverable. “Here is what I think this means, and here is what I would do” is a consulting one. The second is harder, more exposed, and significantly more valuable. If you are consistently offering options rather than recommendations, ask yourself honestly whether that is intellectual rigour or professional self-protection.

Case in Point

The split your audience is living through is not theoretical. It is showing up in how roles are being structured and what is commanding a premium in the market right now.

The research project management layer — scoping, commissioning, supplier management, delivery — is being absorbed into platforms and automated workflows. The organisations investing in headcount are doing so for a different capability: the ability to sit with a client before the brief is written, understand the commercial context, interpret findings in light of organisational reality, and take a clear position on what should happen next.

This is not a new distinction. The difference between a research project manager and a strategic consultant has always existed. What is new is the speed at which the first role is being automated and the premium being placed on the second. The strategists and researchers who thrive in this environment will not be those who executed the most studies. They will be those who learned to interpret — and had the courage to say so out loud.

Closing Thought

AI tells you what happened. Interpretation tells you what to do about it.

For most of the profession’s history, the first part was the hard part — gathering the data, running the study, producing the output. The second part was assumed to follow naturally from sufficient rigour and experience.

That assumption no longer holds. Execution is fast, cheap, and increasingly automated. Interpretation is scarce, situated, and irreducibly human. The gap between those two things is where the next generation of strategic value will be built.

The question is not whether you can produce the findings. It is whether you are willing to stand behind what they mean.

That is the sharper edge.

The Sharp End is a monthly field guide for strategists, researchers, and insight leaders. If this edition resonated, share it with someone who is ready to move from presenting findings to owning what they mean.

Not yet subscribed?

By Francis Nicholson – Expert in hiring Data, Insight and Strategy talent for the Age of AI

Editor’s Note

Over the past eight editions, we have covered a lot of ground. Retrainability. AI literacy. The power shift. Integration. Non-linear careers. Each one has been about adapting — becoming more visible, more connected, more legible to a market in motion.

This month, a different question. Not what you need to become. But what it takes to last.

Because the strategists who fade are rarely the ones who stopped trying. They are the ones who kept trying — furiously, visibly, permanently — in the wrong direction.

Market Signal

The data on this is striking and a little uncomfortable.

DHR Global’s 2025 Workforce Trends Report surveyed 1,500 knowledge workers and found that 88% reported feeling highly engaged — while 82% were simultaneously experiencing burnout. Not disengaged people burning out. Engaged ones.

Deloitte’s research sharpens the point further. A third of workers say they prioritise work that is most visible, regardless of whether it actually creates value. Forty-one percent of daily working time is spent on activity that doesn’t contribute to meaningful organisational outcomes.

And Deloitte’s 2025 Human Capital Trends report identifies AI as quietly making this worse — accelerating the pressure to stay current, adding to workloads, and creating burnout as a silent byproduct of what looks, from the outside, like engagement.

The pattern that emerges: people are busy, active, visibly on — and hollowing out. High signal. Declining substance. This is what performed relevance looks like at scale.

Frontline

A senior insight professional, reflecting on a period she now describes as her least productive despite looking like her most active:

“I was posting, attending, upskilling, presenting. I had opinions on everything. I could talk fluently about AI, about brand, about commercial strategy. But when I’m honest, I couldn’t do any of it deeply. I was performing currency I hadn’t actually earned yet. It caught up with me.”

Relevance performed is relevance borrowed. It has to be repaid.

The repayment usually arrives when something real is asked of you — a project that requires genuine depth, a room that requires actual authority, a moment where fluency in the vocabulary is no longer enough.

Sharp Skill: Building from a Stable Centre

The alternative to performing relevance is not stepping back. It is building from a stable centre — a clear point of view, a defined type of problem you solve well, a reputation that doesn’t require constant maintenance to survive a quiet month.

McKinsey’s research on expertise development draws on psychologist Anders Ericsson’s work across multiple fields — medicine, music, athletics — and finds that it is deliberate practice, not repetition, that compounds real capability. Doing more of the same thing more visibly does not build expertise. Intentional, effortful engagement with the right problems does.

For strategists and researchers, a stable centre usually has three components:

1. A type of problem you are known for solving. Not a job title. Not a methodology. A specific kind of challenge that recurs across industries, sectors, and contexts — and that you have genuinely developed judgment about over time. This is the thing that makes you the first call, not one of several options.

2. A point of view that is genuinely yours. Not an aggregation of other people’s frameworks. A perspective — on how insight creates value, on what strategy actually requires, on where organisations consistently get things wrong — that you have earned through repeated exposure and honest reflection. This is what makes a conversation with you worth having.

3. An energy model that compounds rather than depletes. Research on career longevity is clear: burnout-based productivity cycles cannot sustain a long career. The capabilities most relevant to complex strategic work — judgment, pattern recognition, influence — continue to improve well into midlife, but only if the energy model is sustainable. The strategists who last are not those with the highest output. They are the ones who have learned which work builds them and which merely maintains the appearance of motion.

Case in Point

Stanford’s Centre on Longevity published research in early 2026 noting something counterintuitive about knowledge-work careers: while processing speed does decline after early adulthood, the capabilities most central to complex strategic work improve with age. Judgment. Pattern recognition across contexts. The ability to read a room, hold ambiguity, and move toward a decision without full information.

These are not skills that trend-chasing builds. They are skills that accumulate through depth — through repeated, deliberate engagement with hard problems over time.

The strategists who remain genuinely valuable at 45, 50, 55 are not the ones who successfully performed relevance across every passing cycle. They are the ones who built something real underneath it.

Closing Thought

There is a version of staying relevant that is exhausting and ultimately unsustainable. It requires constant attention, constant output, constant signal. It performs currency that has to be repaid when real demand arrives.

There is another version that is quieter and harder. It requires knowing what you actually stand for, which problems you are genuinely equipped to solve, and which trends you can afford to watch without chasing.

The second version does not look as busy. But it compounds in ways the first one never can.

Stay sharp. Not just current.

The Sharp End is a monthly field guide for strategists, researchers, and insight leaders. If this edition resonated, share it with someone navigating exactly this moment — or forward it to a colleague who might be performing more than they’re building.

Subscribe on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7377303978340143104

Nicholson Glover is a London-based specialist recruitment consultancy, founded in 2002. We place mid-to-senior professionals across four disciplines: Customer Research & Insight, Strategy & Innovation, Data, Analytics & AI, and Product & Technology. We recruit qualitative and quantitative researchers, behavioural scientists, data strategists, econometricians, foresight specialists, product managers, and senior strategy leads — with agencies, consultancies, corporate insight teams, and venture-backed businesses across the UK and globally. To speak to us about a role or a hire, contact Francis at francis@nicholsonglover.co.uk or visit nicholsonglover.co.uk.

By Francis Nicholson – Expert in hiring Data, Insight and Strategy talent for the Age of AI

Editor’s Note

Last month we looked at the integrator: the strategist who creates value by connecting functions that weren’t designed to speak to each other. This month, a harder question. If that kind of work is increasingly where the value sits — lateral, relational, cross-functional — why does it so rarely appear on a job title, a pay band, or a performance review?

Because most organisations still reward the ladder. And most strategy careers are no longer shaped like one.

Market Signal

The data is starting to catch up with what many of us already sense.

Among professionals who have been in their roles for five or more years, 38% are no longer considering management positions as their next move. Even among current managers, 19% are actively seeking non-supervisory roles next. Progression without a title upgrade is becoming a deliberate choice, not a fallback.

McKinsey’s research on career mobility tells a sharper story still. The professionals with the most upwardly mobile trajectories — moving one, two, or three income brackets higher — were not the ones who stayed longest in a lane. They were the ones who made what McKinsey calls “bold moves”: roles that were adjacent but contained up to 40% genuinely new skills. Lateral stretch, not linear tenure, drove the biggest career gains.

Meanwhile, Deloitte finds that organisations adopting skills-based talent models — where range and adaptability are assessed alongside depth — are 63% more likely to achieve their desired business outcomes than those using traditional role-based frameworks. The logic is shifting. Most organisations just haven’t updated their reward systems to match.

Frontline

A strategy director at a professional services firm, reflecting on her last two career moves:

“Both times, I took what looked like a sideways step. Different sector, slightly smaller team. Both times, people asked me if I was sure. Both times, I came out the other side with a sharper point of view, a broader network, and frankly more interesting work. The ladder would have had me managing more people and attending more governance meetings.”

The pattern is consistent. The moves that look lateral from the outside often compound fastest on the inside.

Sharp Skill: Narrating the Non-Linear

The risk of a non-linear career is not that it limits your options. It is that others can’t read it.

Hiring managers, sponsors, and senior stakeholders are still pattern-matching against a ladder. A varied career looks like indecision to someone who has only ever seen one kind of progression. The strategist’s task is to make the arc legible — to give the range a narrative.

Three practical moves:

1. Name the thread, not the titles. The through-line of a non-linear career is rarely a job function. It is a kind of problem you solve, a lens you bring, a type of situation you thrive in. “I work at the intersection of data and commercial decision-making” is more compelling — and more accurate — than a list of lateral moves that require explanation.

2. Make range look intentional. Every move that felt exploratory at the time can be reframed as deliberate in retrospect. Not dishonestly — but accurately. The skills you built in each role were real. The question is whether you have articulated why they compound.

3. Publish your thinking, not just your work. In a lattice career, reputation travels ahead of you in ways a CV cannot. The strategists building durable visibility — as we covered in Edition 5 — are the ones whose thinking is legible before they enter a room. A newsletter. A point of view. A consistent voice on a specific tension. These are not personal branding exercises. They are how range becomes recognised as expertise.

Case in Point

McKinsey’s internal mobility research found that employees who took on rotational assignments — moving across functions, sectors, or problem types — were 20% more likely to be promoted than those who stayed within a single track. The moves that looked sideways were, in aggregate, the faster route up.

But here’s the friction: the same research shows that over 80% of role movements still involve people changing companies rather than moving internally. Most organisations structurally resist the lateral moves they claim to value. Which means that for many strategists, the non-linear career is largely self-managed — and self-narrated.

That is not a disadvantage. It is leverage, for those who know how to use it.

Closing Thought

The ladder was always a simplification. It assumed a stable hierarchy, a predictable market, and a single definition of seniority. None of those hold in the way they once did.

What’s replacing it isn’t chaos. It’s a lattice — and a lattice rewards different things: range, relationships, the ability to operate in unfamiliar terrain without losing your bearings.

The strategists who thrive in this environment are not the ones with the most impressive vertical climb. They are the ones who can make their journey make sense to someone hearing it for the first time.

Narrative is the new CV.

The Sharp End is a monthly field guide for strategists, researchers, and insight leaders. If this edition resonated, share it with someone who would find it sharp rather than safe — or forward it to a colleague who is navigating exactly this kind of career moment.

Not yet subscribed?

By Francis Nicholson – Expert in hiring Data, Insight and Strategy talent for the Age of AI

Editor’s Note:
Last month we looked at where power is moving in AI-accelerated organisations: away from information holders, toward those who can align functions around a shared direction. This month, a natural question follows. If alignment is the new currency, who actually does that work — and what does it take?

The answer is increasingly: the strategist as integrator.

Market Signal

Most AI transformation programmes are stalling not because the technology isn’t good enough, but because the organisation isn’t connected enough. McKinsey’s State of AI research finds that 88% of companies now use AI in at least one function — yet only 39% see any measurable EBIT impact. The gap is not capability. It is coherence.

Meanwhile, demand for professionals who can bridge that gap is accelerating. Robert Half’s 2026 Salary Guide shows that marketing analytics managers and digital strategists are among the fastest-growing roles in the sector — specifically because employers are seeking people who connect data, insight, brand, and commercial outcomes in one fluent motion.

The integrator is not a new job title. It is an emerging professional identity.

Frontline

A senior insight leader at a mid-size FMCG company described her last twelve months like this:

“My job title hasn’t changed. But what I actually do has. I spend more time getting the data science team and the brand team to agree on what a question even means than I do running research. The methodology is the easy part.”

This pattern is showing up everywhere. The technical work is increasingly delegated — to AI, to junior staff, to automated platforms. What remains is the connective tissue: understanding what different functions need, translating between them, and holding the quality of the question.

Sharp Skill: Integration Without Authority

Most strategists who do integration work do not have formal authority over the teams they connect. They influence without hierarchy. That is a distinct skill, and it is learnable.

Three practical moves:

1. Own the question, not the answer. When different functions argue about conclusions, the integrator reframes to the upstream question. “Before we debate the output, are we aligned on what we’re actually trying to decide?” This is not facilitation. It is intellectual leadership.

2. Build a shared vocabulary early. The most common failure in cross-functional work is that each team uses the same words to mean different things. “Brand,” “performance,” “audience” — all contested terms. Name the ambiguity before it becomes a conflict.

3. Make the connections visible. When research has a direct line to a commercial decision, say so explicitly. When data science findings contradict a brand assumption, surface it as a productive tension rather than a problem. Integrators create legibility between functions — which is exactly what visibility-minded strategists learned to do for their own thinking in Edition 5.

Case in Point

In industries where AI is generating more insight faster, the bottleneck has shifted decisively. It is no longer “do we have the data?” It is “can we agree on what it means and what to do next?” Businesses that invested consistently in insight reported faster, more confident decisions and stronger capacity to adapt — not because their research was more sophisticated, but because their insight function had learned to operate across commercial, brand, and data teams simultaneously.

The strategists who thrived were not the ones with the most technical skill. They were the ones who could make insight actionable across functions that had different languages, different incentives, and different definitions of success.

Closing Thought

There is a version of the strategist that waits to be consulted. They produce excellent work, present it clearly, and hope it lands.

There is another version that operates differently. They are present earlier, in the room where the question is being formed. They connect the data scientist to the brand director before the brief is written. They know which commercial pressure is driving the urgency. They shape the context in which their own work will be received.

The second version is not smarter. They are better integrated.

That is the sharper edge.

The Sharp End is published monthly. If this was useful, share it with someone who would find it sharp rather than safe.

✍️ Editor’s Note — Power Didn’t Disappear. It Moved.

Over the past two months, we’ve talked about leverage and visibility.

But visibility alone doesn’t guarantee influence.

To understand what 2026 demands of strategists, researchers and insight leaders, we need to look at something bigger:

Where is power actually moving?

AI hasn’t flattened organisations.

It has redistributed influence.

And the shift is subtle.

📈 Market Signal — Influence Is Moving to the Integrators

The major consulting firms are already documenting this shift.

Research from McKinsey into corporate transformations found that 70% of successful transformation programmes prioritised cross-functional teams early, compared with only around 20% of unsuccessful ones.

The same body of work shows that cross-functional transformations outperform single-function initiatives by 30–40% in terms of value delivered.

And yet collaboration remains difficult.

McKinsey also reports that three out of four cross-functional teams underperform on key metrics, largely because organisations struggle to align expertise across functions.

The implication is important.

The scarce capability isn’t intelligence or technical skill.

It’s integration.

Power is increasingly concentrating around people who can:

• connect insight to commercial decisions
• translate between technical and non-technical teams
• reduce friction between functions

In a world of abundant information, alignment becomes the real bottleneck.

🗣 Frontline — “I’m Not the Expert. I’m the Bridge.”

A strategy director at a global brand described their evolving role like this:

“I’m not the person who knows the most about AI, and I’m not the commercial lead either. But I’m the one who makes the two talk to each other. That’s where the influence is now.”

This is becoming a familiar pattern.

The most valuable people in many organisations are no longer the deepest specialists.

They are the connectors — the people who can move ideas across boundaries.

🔧 Sharp Skill — Influence Without Ownership

March’s Sharp Skill is uncomfortable but increasingly essential:

Influence without formal authority.

In cross-functional environments, few people own the full problem.

But influence often belongs to the person who can:

1. Frame the problem before it becomes political
2. Make trade-offs visible across teams
3. Sequence work so others can move confidently

When organisations struggle to align functions, the people who reduce that friction become disproportionately influential.

🌟 Case in Point — The Translator Advantage

One insight lead we spoke to didn’t increase their influence by becoming more technical.

Instead, they positioned themselves between data science and marketing.

They translated modelling outputs into commercial decisions.

They surfaced trade-offs between experimentation and brand risk.

They didn’t own the budget.

They didn’t own the roadmap.

But they owned the narrative that connected them.

In a year of AI experimentation, that narrative ownership became decisive.

✂️ Closing Thought

Information is no longer scarce.

Alignment is.

And power tends to follow scarcity.

The strategist of 2020 controlled insight. The strategist of 2026 connects insight, technology and decisions.

That’s where the influence now lives.

Editor’s Note — Capability Isn’t the Problem
One of the most common frustrations I hear from strategists, researchers, and insight leaders isn’t about skill. It’s about stalling. They’re delivering strong work. They’re trusted. They’re often told they’re “doing really well.” And yet — opportunities pass them by. This isn’t a confidence issue. And it isn’t a performance issue. It’s a visibility gap.

Market Signal — When Good Work Disappears

Research backs this up.

• Harvard Business Review shows employees who actively communicate their reasoning and progress are 23% more likely to be rated as high performers, even when output is comparable.

• McKinsey research consistently finds that visibility with decision-makers outweighs technical excellence as a predictor of advancement in knowledge roles.

• Microsoft’s Work Trend Index reports that nearly 60% of leaders feel they lack visibility into how work actually gets done — especially in hybrid teams. At the same time, Gartner predicts that by 2026, over 80% of knowledge work outputs will involve AI assistance, creating what it calls a “contribution blur.” When output is easy to generate, only visible thinking gets recognised.

Frontline — “My Work Was Valued. My Thinking Wasn’t Visible.”

“I kept being told I was doing well — but the stretch roles went elsewhere. When I asked why, the feedback was vague. That’s when I realised something uncomfortable: my work was valued, but my thinking wasn’t visible.” This story is increasingly common. AI accelerates delivery. Collaboration diffuses ownership. And unless reasoning is surfaced deliberately, judgment disappears behind the artefact.

Sharp Skill — Making Thinking Visible

Visibility isn’t about being louder. It’s about making your thinking legible. In AI-accelerated environments, thinking that isn’t visible is assumed not to exist. Practically, this means:

1. Narrating intent; what problem are we solving, and why?

2. Surfacing trade-offs; what options were rejected, and on what basis?

3. Closing the loop; what changed because of this work?

This isn’t self-promotion. It’s strategic transparency.

Case in Point — Quiet Capability, Amplified

One insight lead didn’t change role or employer. Instead, she changed how her work showed up. She framed insight as decision support, documented judgment not just conclusions, and made trade-offs explicit in senior forums. Within months, her influence grew. Not because she became louder, but because her thinking became easier to trust.

Closing Thought

Capability still matters. But capability without visibility now carries a cost. In a market full of output, influence flows to those whose thinking can be seen.

Written by Francis Nicholson – Expert in recruiting for Insight and Strategy roles.

Written by Francis Nicholson – Expert in hiring Data, Insight and Strategy talent for the Age of AI

New Year, New Leverage Skills, stories & signals shaping tomorrow’s teams

Editor’s Note — A Different Kind of Optimism

January often arrives carrying an expectation of clarity. Clear goals. Clear plans. Clear answers about what comes next. But after three months exploring retrainability, AI literacy, and human advantage, one thing feels increasingly clear precisely because the noise has settled: the market hasn’t become easier…but it has become more legible.

2026 doesn’t offer certainty, but it does offer leverage. Not leverage in the sense of control, but leverage in the ability to move forward without waiting for perfect information. For strategists, that leverage shows up in quieter ways: clearer framing, faster synthesis, and the confidence to shape decisions earlier rather than simply respond to them. AI has normalised experimentation.

Organisations now understand where automation helps and where it doesn’t.

And human judgment (influence, interpretation, credibility) is being re-evaluated not as a nice to have, but as a differentiator. The people who will gain ground this year aren’t waiting for confidence to arrive. They’re building strategic momentum.

Market Signal — The Fog Is Thinning

Employers are clearer about what they don’t need: endless deck production, generic analysis, output without ownership. And more explicit about what they do need: people who can frame problems, connect insight to action, and move decisions forward under uncertainty. AI hasn’t removed ambiguity but it has shortened the distance between question and answer.

Signal: the premium is moving from information to interpretation.

Frontline: “I Stopped Waiting for Clarity”

One senior strategist explained: “I realised I was waiting for the market to tell me what version of my role would survive. The moment I stopped waiting and started shaping it myself, things moved.” Instead of chasing certainty, she began making small, visible moves, owning ambiguous briefs, reframing insights into clear choices, and stepping into conversations earlier. Momentum followed not because the environment changed, but because her position within it did.

Sharp Skill: Strategic Momentum

Strategic momentum isn’t about speed or confidence. It’s the ability to move forward without full certainty while increasing future options. It means making directional moves, showing learning in progress, and positioning yourself where thinking is shaped, not just delivered. In 2026, momentum isn’t loud…it compounds quietly.

Case in Point: The Quiet Repositioning

A long-tenured insight lead didn’t change role, title, or employer. Instead, she reframed how her value showed up — shifting from insight delivery to decision framing and using AI outputs as conversation starters, not endpoints. No reinvention. Just leverage.

Closing Thought

2026 won’t reward certainty. It will reward those willing to move before certainty arrives. Strategic momentum isn’t about confidence.

It’s about creating options before you need them.

Written by Francis Nicholson – Expert in hiring Data, Insight and Strategy talent for the Age of AI

Theme: Human Advantage — The Skills AI Still Can’t Touch

Skills, stories & signals shaping tomorrow’s teams

✍️ Editor’s Note: Human Advantage in the Age of AI

After two months exploring retrainability and AI literacy, one truth keeps surfacing in conversations with strategists, researchers, and insight leaders:

Everyone is experimenting with AI. BUT confidence, influence, judgment and human connection are stealing the spotlight again.

As more teams adopt AI tools, the differentiators are shifting back to the timeless skills that have always made people great at this work. Not the data. Not the decks. But the human advantage: how we influence, interpret, challenge, empathise and persuade.

This month, we’re putting the spotlight firmly on the skills AI still can’t touch and why they matter more than ever.

📈 Market Signal — The Human Premium Is Rising

Across strategy, insight and data, job descriptions are quietly evolving.

Not with louder demands for technical expertise — but with stronger emphasis on:

• Stakeholder influence
• Judgment under uncertainty
• Commercial intuition
• Cultural insight
• Emotional intelligence and facilitation

Why? Because AI is excellent at generating options…..but it’s terrible at deciding which one matters.

Companies are learning (quickly) that AI can accelerate thinking, but only humans can:

• Read a political room
• Land a narrative
• Challenge a client
• Sense when something “looks right” but is wrong

Signals in the market:

• Senior hires are increasingly being assessed on cross-functional credibility and influence.
• Early-career roles are favouring candidates who show “learning agility” and communication impact over specific tools.
• Leadership teams are describing “judgment” as their biggest hiring gap not technical ability.

Takeaway: In 2026, the most valuable skills won’t be the ones AI replaces…they’ll be the ones AI amplifies.

🗣 Frontline — “My job isn’t insight anymore… it’s interpretation.”

A senior insight lead at a global tech company put it simply:

“AI gave us more answers than we know what to do with. My team’s value is now deciding which answers actually matter.”

She described how AI has sped up early-stage synthesis so much that her team’s role has shifted upstream:

• Framing the strategic question
• Connecting insight to business realities
• Coaching stakeholders out of the wrong rabbit holes

The job isn’t collecting or even analysing anymore….it’s guiding decisions through complexity.

And that requires honesty, confidence, diplomacy, and narrative skill. Not an algorithm.

🔧 Sharp Skill — Judgment Under Uncertainty

If Edition 2 was about “thinking with AI,” Edition 3 is about the human supplement; the things only you can do.

This month’s Sharp Skill: Judgment.

AI can tell you what might be true. Only humans can tell you what’s useful.

To strengthen judgment in an AI-heavy workflow:

1. Interrogate the edges — where does the model’s logic break?
2. Sense-check with context — what would a real customer actually say?
3. Pressure test assumptions — what’s the commercial trade-off?
4. Read the politics — what is the organisation ready to hear?

Takeaway: Judgment is becoming the new strategy superpower.

🌟 Case in Point — The Strategist Who Became “The Translator”

One brand strategist we spoke to was initially worried that AI tools were “doing her job.”

Six months later, she’s in a bigger role.

Why? Because she became the person who could:

• Challenge AI outputs
• Spot patterns AI missed
• Land a narrative senior leaders could act on
• Build confidence in recommendations

Her director described her new value perfectly:

“The machines gave us speed. She gave us clarity.”

The human advantage isn’t disappearing, it’s being revalued.

✂️ Closing Thought

AI is getting faster. Teams are getting leaner. And the work is getting louder.

The people who will rise next aren’t the most technical — they’re the ones who bring the human edge: influence, intuition, honesty, and courage.

👉 The future belongs to those who combine AI acceleration with human advantage.

Welcome to the second edition of The Sharp End. Each month we’ll cut through the noise to bring you the signals, stories, and shifts that matter most for strategists, researchers, and insight professionals.

Written by Francis Nicholson an expert in Recruiting Insight & Strategy Leaders & Helping Brands Hire Better & Talent Find Purpose.

✍️ Editor’s Note — The AI-Literate Strategist

Last month we talked about retrainability — who companies choose to invest in when AI starts reshaping roles. This month, we look at what happens next: how strategists, researchers, and insight leaders are becoming AI-literate. Not “AI experts.” Not coders. But professionals who can use AI to think faster, frame sharper, and deliver insight that still feels human. Because the edge isn’t in the tools themselves, it’s in knowing how to make them work for you, not instead of you.

📈 Market Signal — From Tools to Thinking

We’re now seeing the second wave of AI adoption in the strategy and insight world:

• Early adopters used AI for speed — transcribing, summarising, automating.

• The next wave is using it for thinking — exploring scenarios, framing hypotheses, testing narratives. According to LinkedIn data, job postings mentioning “AI literacy” in marketing, strategy, and research roles are up 42% year-on-year. Yet few employers can define what that actually means. The firms getting it right see AI literacy as mindset over mastery:

• Curiosity to experiment.

• Judgment to challenge machine output. • Storytelling to turn data into direction.

Takeaway: “AI literacy” is emerging as the new differentiator — not as a technical skill, but as a way of thinking.

🗣 Frontline Story — “It’s Like Having a Junior Strategist Who Never Sleeps”

How often do you use AI in your day job? The answer;”90% of my day, when is the last time you ran a Google Search?”. “I started using AI to speed up desk research but now it’s in every stage of my process. I test hypotheses, summarise transcripts, even draft narrative frames to push my thinking. It’s not perfect, sometimes it’s way off, but it’s made me sharper. It’s like having a junior strategist who never sleeps. The trick is knowing when to trust it, and when to throw its ideas out completely.” That’s how one Innovation Director described their evolving relationship with generative AI. Others echo the same sentiment: AI is becoming the new thinking partner, not a threat. Those who use it well are learning to structure briefs faster, prototype insights earlier, and move their clients from analysis paralysis to action faster.

🔧 Sharp Skill — Framing with AI

If you want to show AI literacy, don’t start by listing tools — show how you think with them. Try this three-step approach:

1. Prompt for patterns — use AI to reveal what’s missing, not just what’s there.

2. Interrogate the logic — push back on its assumptions; make the invisible visible.

3. Rebuild the narrative — turn raw AI output into a point of view that moves people.

Takeaway: The best strategists aren’t being replaced by AI. They’re being augmented by it — faster thinkers, sharper framers, more decisive storytellers.

🌟 Case in Point — From Insight Manager to “AI Translator”

One insight manager at a global FMCG brand described how she began experimenting with AI to synthesise open-ended survey data. Instead of waiting days for coding, she could test hypotheses in hours — freeing up time to focus on the story and recommendations.

When she shared her results with leadership, her manager asked her to train the wider team. Three months later, her title changed to AI Translator, leading internal pilots on how to integrate tools responsibly. The lesson? AI literacy isn’t about learning to code — it’s about learning to communicate.

✂️ Closing Thought

AI is changing what it means to be “strategic.” The best people in our field won’t be the ones with the most tools — they’ll be the ones who use tools to think differently.

👉 The future belongs to the AI-literate strategist.