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.