From the rise of AI explainability to the return of ‘old school qual,’ here’s what’s been shaping the insight space this month.

🔁 Quick Pulse: What We’re Hearing

💬 “Senior stakeholders are buying into AI… but still want to see the workings.”

💬 “There’s fatigue around dashboards, people want decisions, not data dumps.”

💬 “Clients are coming back to qual, they miss the ‘why’ behind the numbers.”

After two years of tech hype, there’s a pivot back toward insight with narrative… not just metrics.

🧠 One Big Trend: AI Explainability Hits the Insight Agenda

2025 saw mass adoption of AI for concept testing, coding qual, and predictive modelling. But now in 2026, trust and transparency are the key concerns, especially when insights are used to shape customer experience or comms.

🧪 Example: Samsung worked with a research agency to pilot an “AI explainability layer” – a human-readable narrative that outlines how and why the model reached its recommendation. This helped align insight with compliance, brand tone, and stakeholder buy-in.

“Black box” AI won’t cut it anymore… clients want to see behind the curtain.

🔍 Method Spotlight: Vox Pop Reels for Instant Storytelling

Short-form video isn’t just for TikTok anymore. Researchers are embracing 60-second vox pops as fast, rich, human-centred data capture.

🎤 Brands are using:

• Selfie-style responses via mobile
• On-street intercepts with social-style editing
• Quick-turn showreels for stakeholder buy-in

👟 Example: New Balance used vox pop reels to test cultural relevance of a brand refresh across Gen Z consumers in the UK and US. The results? Stakeholders described the output as “more convincing than a slide deck”.

It’s qual, but creator-style… human, raw, and powerful.

🚀 Brand to Watch: Pret A Manger

Pret has quietly become a testing powerhouse, using CRM and loyalty data to trial new product lines, formats, and messaging at speed.

In Feb, it launched a micro-test of new vegan breakfast options via app-exclusive offers with instant feedback loops via 2-click polls and purchase behaviour tracking. 4 variations, 1 week, and a decision made.

It’s a lesson in using what you already have to test smarter, not bigger.

📊 Smart Stat

📈 63% of insight teams say they’re “over-reliant” on past data, but only 27% have a proactive plan for live testing in 2026. (Source: Insight Leaders Barometer, February 2026)

The insight edge this year? Being braver in the present, not just smarter about the past.

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.

New year, new insights! With hiring kicking off, budgets locked in, and strategic ambition running high, here’s what we’re seeing across the research and insight world as 2026 gets underway.

🧭 Quick Pulse: What We’re Hearing

💬 “Our 2026 planning is all about moving faster, even if it has to be with fewer resources.”

💬 “We’re under pressure to use existing data before commissioning more.”

💬 “AI is everywhere – we need senior people who are excited about it and can envision how AI can help evolve our proposition.”

Insight leaders are walking the tightrope between automation and authenticity, with speed and stakeholder trust top of mind.

🔍 The Big Trend: Agile Insight Goes “Always-On”

The ‘project-based’ model of insight is being replaced by rolling feedback loops. Instead of standalone surveys or communities, brands are investing in “always-on” listening across social, CRM, product reviews and micro-surveys.

🛒 Example:Superdrug launched a continuous feedback engine across its app, triggering 1–2 question check-ins at key interaction points. This data is visualised weekly, feeding directly into stock, promo and comms decisions.

2026 will reward teams who treat insight not as a moment, but as a muscle.

🛠 Method Spotlight: Mobile-First Qual

With screen fatigue setting in and diary studies losing steam, mobile-native qual is seeing a resurgence… but smarter this time.

📱 Short-form video tasks  🎤 Voice note prompts  🧠 “In-the-moment” reflections over traditional recall

📦 Example:Gü Desserts used voice diaries via WhatsApp to capture how consumers felt about indulgence and guilt post-holidays. The real, unscripted tone uncovered emotional drivers that traditional surveys missed, feeding into packaging updates launching this quarter.

👀 Brand to Watch: IKEA

IKEA launched a co-ideation lab within its loyalty app where customers help design new storage solutions. Members submit photos of real-life space problems, vote on ideas, and get early product drops.

It’s research, community, loyalty and co-creation all in one.

📈 Results:

– 20K+ submissions in first 6 weeks

– 3 product ideas now in prototype

– 15% increase in app engagement

📊 Smart Stat

🧠 Only 42% of brand teams say they regularly activate insights within 10 days of collection.(Source: GreenBook Q4 Report)

In 2026, speed to action will be vital to businesses more than ever.

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.

Q4 pressure is peaking, 2026 plans are crystallising, and insight teams are being asked to do more – faster. From AI guardrails to attitudinal segmentation 2.0, here are four big themes shaping the insight space this November.

🤖 AI Guardrails Become a Strategic Priority

As AI tools become embedded in research workflows, brands are setting clearer boundaries, not just around data privacy, but around decision-making accountability.

Heineken introduced internal AI usage guidelines for research, setting out when AI should support, augment, or be left out entirely. One key rule: AI can summarise but never generate final recommendations without human review. This move followed a misinterpretation of sentiment in early AI-led social listening, which skewed tone-of-voice guidance.

It appears that we are now moving past the AI hype phase, and now many are looking to build smart, ethical infrastructure around it.

🛒 Segmentation Gets Real-Time and Behaviour-Led

Static attitudinal segments are being challenged by live behavioural signals…especially in e-commerce, where consumer needs shift hour by hour.

M&S moved beyond traditional personas to trial “real-time segment activation,” combining first-party data, page journeys, and session recency to adapt homepage content and email subject lines dynamically. A single user could shift between three need states in a day, and personalisation kept up. The result? A 12% uplift in click-through and stronger basket builds.

The new segmentation mindset is less “who are you?” and more “what do you need right now?”

📱 Gen Alpha Enters the Research Chat

Brands are starting to treat Gen Alpha (those born after 2010) as a research-worthy audience in their own right, not just as “kids of millennials.”

LEGO launched a co-creation community for 10–13-year-olds, with fully COPPA-compliant guardrails, moderated online spaces, and a hybrid of drawing, voice, and build-based tasks. Insights from the community fed into 2026 packaging and digital gameplay strategies, showing this age group’s strong lean toward environmental storytelling and mixed-reality play.

Gen Alpha doesn’t want to be marketed to… they want to shape what’s being built.

🧾 ‘Value’ Gets Redefined… Again!

As economic anxiety drags on, the consumer definition of valuecontinues to evolve, and brands are updating how they measure it.

ASDA conducted a mixed-methods study combining digital receipts, time-use diaries, and emotional check-ins during weekly shops. Findings showed that value isn’t just about price, it’s about reducing mental load (“can I get everything here without having to think?”). This insight has informed store layout updates, pre-bundled meal solutions, and smarter basket-building prompts.

The value equation in 2025 = price + time + headspace. Are you tracking all three?

The Big Shift: Automation Arrives Quietly… Then All at Once

What began as “experimentation” in 2023 has become a full-scale reset by Q4 2025. AI isn’t just augmenting work. It’s absorbing it. And suddenly, everyone from HR leaders to brand strategists is being asked to rethink what roles, teams, and capabilities look like in the next three years.

• Amazon is preparing up to 30,000 corporate job cuts. The automation wave is targeting ops, HR, AWS and devices, especially non-technical middle-tier roles.

• Microsoft announced 9,000 job losses (4% of workforce) to align operations with its AI-first future.

• Accenture, even as a key AI transformation vendor, cut 11,000 jobs this year.

• Layoffs across the tech sector now exceed 180,000 in 2025, according to OpenTools.

But here’s the twist: many of these companies are still hiring. Just not for what they used to.

1. AI engineers, prompt strategists, automation leads and talent designers are in.

2. Traditional generalist roles are being thinned out or merged with AI-enabled systems.

3. Brand, innovation, and people teams must now build strategy around capability gaps, not just market gaps.

📌 Takeaway: It’s no longer “AI vs jobs.” It’s “AI vs stagnation.” The talent strategies that win will be the ones that evolve fastest, not just react loudest.

Brand in Focus: Amazon’s Talent Rebuild

Amazon‘s layoffs aren’t just a cost-cutting play. They’re a talent reset. Internal sources and analysts suggest the company is moving from “people-heavy” operations to “platform-heavy” logistics, powered by its own AI tooling and internal LLMs.

Over 14,000 roles across devices, HR and AWS have already been confirmed for redundancy. Yet Amazon is actively hiring for thousands of automation specialists, robotics leads and prompt engineers.

According to a recent Forbes piece, Amazon’s “automation imperative” is now core to its cost-of-delivery model and future logistics dominance.

Why it Matters

• Internal capabilities are being rebuilt to match external strategy. Fast.

• Employer brand is being tested. Messaging now has to juggle “AI ambition” with “human responsibility.”

• Teams who once ran day-to-day ops are being retrained (or replaced) to manage the platforms instead.

📌 Takeaway: Brand trust now extends to your own people. If your automation play doesn’t feel like a talent strategy, it might just become a PR problem.

Consulting Corner: Deloitte Builds a Digital Workforce

While some consulting giants are pulling back, Deloitte is doubling down. Their Global Agentic Network, launched this year, aims to help clients not just “use” AI, but restructure their entire operating models around it.

In August, Deloitte released a framework for workforce evolution, centred on AI‑human collaboration, internal reskilling, and agile org design. Their services now include embedded AI strategy teams focused on people architecture, not just tech integration. Notably, Deloitte is hiring AI leads and workforce architects at a faster rate than traditional strategy consultants.

Why it Matters

• Consulting is becoming embedded, not external.

• Clients want speed, integration and transformation. “Advisory decks” alone won’t cut it.

• Talent agility is being sold as a service.

📌 Takeaway: Strategy firms are no longer just fixers. They’re builders. And the product is often the org chart itself.

🔔 Final Thought

The layoffs are real, but so is the opportunity. This month proves that “strategy” isn’t just about goals or markets anymore. It’s about how your teams are designed, what skills you’re investing in, and whether your internal narrative can match your external promise.

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.

Welcome to the first 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.

Our opening theme is retrainability. Accenture’s recent cuts show how companies are already sorting their people into those they will invest in — and those they won’t. For strategists and researchers, this question is no longer abstract: AI is beginning to nibble at the very edges of our craft.

This issue looks at how that line is being drawn, what it feels like on the frontline, and the tools you can use to prove your value. Because the sharp end of the AI transition isn’t about technology alone — it’s about who gets carried forward, and why.

📈 Market Signal: Who Can’t Be Retrained?

Accenture has just cut more than 11,000 jobs in three months and told staff more departures are coming — not because business is collapsing, but because the firm has decided some employees simply can’t be retrained for the AI era.

The consulting giant, which employs nearly 800,000 people worldwide, is spending $865mn on restructuring as it races to align its workforce with client demand for AI and data-driven projects.

In the past two years, Accenture’s AI and data workforce has grown from 40,000 to 77,000 people — a doubling that shows exactly where investment is flowing.

Why it matters for strategists and researchers:

• Skill adjacency: If your role touches data, insight, or digital delivery, you’re more likely to be reskilled than replaced.
• Learning velocity: Firms invest in people who’ve adapted before and can prove they’ll adapt again.
• Value horizon: Reskilling that doesn’t produce results within 12 months rarely makes the cut.

Takeaway: Retrainability is becoming a new professional currency. If you can’t show it, don’t expect employers to bet on you.

🗣 Frontline: “If AI Can Do the Decks, What’s Left for Me?”

“I’ve built my career on turning consumer research into clear strategy. Now clients are asking if they really need a team to do that — or if AI can churn out the insights faster and cheaper. I can see the tools getting better by the month. Part of me is excited to use them, but another part is worried: if AI can write the deck, what’s left for me?”

That’s how one strategy director at a global brand described the tension. The core craft of research and strategy — synthesising data, pulling threads, telling the story — is exactly what generative AI is beginning to nibble at.

And it’s not just candidates feeling the pressure. One CMO we spoke to put it bluntly:

“We don’t need strategists who tell us what the data says. We need strategists who tell us what to do about it.”

Why it matters:

• For strategists and researchers, the retrainability question is sharper than ever: what new value can you bring that AI can’t?
• For employers, the risk is assuming that “research” or “deck writing” is the whole job. The human edge lies in judgment, provocation, and credibility with decision-makers — areas where AI still lags.

The anxiety is real, but so is the opportunity: those who integrate AI into their toolkit, while doubling down on influence and storytelling, are the ones most likely to be deemed worth retraining.

🔧 Sharp Tools: 3 Ways Strategists & Researchers Can Prove They’re Retrainable

1. Frame Better Questions AI is good at answers, weak at defining problems. Show that you can ask sharper, more commercial questions that AI won’t.
2. Blend Machine Output with Human Judgment Don’t just use AI — challenge it. Turn outputs into compelling arguments that persuade decision-makers.
3. Make the Story Stick A model can write a deck, but it can’t read a room. The ability to land a message with executives or clients is what sets retrainable strategists apart.

Takeaway: Retrainability in this field isn’t about coding skills. It’s about proving you can sit at the intersection of AI outputs, human judgment, and organisational influence.

🌟 Case in Point: From Planner to AI-Literate Insight Lead

Not every story is one of anxiety. One senior brand planner we spoke to recently has already pivoted successfully. Faced with shrinking budgets and rising expectations, she took the lead in experimenting with generative AI tools to accelerate early research synthesis.

Instead of fearing replacement, she positioned herself as the translator: testing outputs, spotting blind spots, and shaping them into compelling recommendations for senior leadership.

Her reward? A newly created role as Insight Innovation Lead, where she now guides how the business blends AI with traditional research.

The lesson: Retrainability isn’t just about technical skills. It’s about curiosity, experimentation, and making yourself the person who shows others how to use new tools wisely.

👤 People Move: Retrainability at the Top

EY has promoted Nicola Morini Bianzino, its global Chief Technology Officer, into the role of Global Managing Partner for Client Technology.

What makes the move interesting isn’t just the title change, but what it signals: the firm’s most retrainable leaders are those who can pivot from technical depth into client-facing influence.

Why it matters: Retrainability isn’t just for analysts. At the top level, the people rising fastest are those who can bridge worlds: from technologist to strategist, from data to story, from back office to boardroom.

✂️ Closing Thought

AI isn’t just another tool in the strategist’s kit. It’s a sorting mechanism. The winners won’t be those who know the most — but those who prove, day in and day out, that they can be retrained, reinvented, and still matter.

👉 In the AI era, retrainability is your strategy.

By Cameron Maskell

Halloween has come and gone, campaign season is peaking, and insight teams are digging into end-of-year trends and 2026 planning. So, what’s been brewing across the research landscape this October? Here are four developments that stood out.

💬 AI Moderators Take on Qual at Scale

As online qual grows, AI is stepping in… not to replace researchers, but to support smarter, more scalable moderation and follow-up.

Unilever recently piloted an AI moderator for a week-long online community exploring teen skincare habits across five markets. The AI tool used natural language prompts to probe participants in real time, flag contradictions, and tailor follow-up questions. Human moderators oversaw and refined key threads, cutting analysis time by 40% and improving depth on niche topics.

Takeaway? The human touch still matters, but with AI co-pilots, qual is getting faster, deeper, and more flexible.

🧭 “Second Screen” Ethnography Captures Real-Life Context

Forget scheduled diaries! Second-screen ethnography is giving researchers real-world behavioural insight, in real time.

Just Eat worked with a mobile ethnography partner to capture how people really decide what to order, not just what they say they do. Participants screen-recorded their scroll behaviour on food apps, paired with voice memos explaining their thought process (e.g. “I was craving Chinese, but then the delivery time made me change my mind…”). The data led to UX tweaks and new delivery-time filters now rolling out across the app.

Takeaway? When you see the decision unfold live, the ‘why’ behind the ‘what’ becomes clearer than ever.

📦 Research Supports the Rise of Re-commerce

As resale, rental, and return models surge, insight teams are guiding how brands tap into the growing re-commerce economy.

John Lewis launched a second-hand furniture pilot and partnered with an insight consultancy to explore perceptions of pre-owned value. Through on-site pop-ups and digital ethnography, they found that product condition mattered less than storytelling. Consumers valued transparency (e.g. “tell me who used this before”) and sustainability framing more than price alone. The findings led to a test of branded storytelling tags on pre-loved listings.

Takeaway? Research and insights are showing that resale value is about reputation, relevance, and re-framing what ‘used’ really means.

📊 Insight Teams Embrace “Test-and-Learn” Budgeting

As agility becomes a C-suite mantra, insight teams are rethinking how they use and justify budget.

Sky ran a “test-and-learn” budget structure across its research road map this quarter, allocating 20% of spend for fast-turn experiments tied to live business questions. Quick audience pulses and message tests fed directly into weekly creative decisions for an entertainment campaign. The result? Shorter time-to-insight, fewer bottlenecks, and more buy-in from commercial leads.

Takeaway? Insight is becoming a testing engine for smarter business decisions.