BOI (Board of Innovation)

3 archetypes: how companies approach AI adoption

Signe Damgaard — Mon, 30 Jun 2025 14:59:55 +0000

3 archetypes: how companies approach AI adoption

Laura Stevens

Managing Director of Data & AI

As organizations navigate the AI revolution, we see three distinct AI adoption archetypes emerging. Each represents a different approach to leveraging AI, whether as a growth engine, an enterprise-wide transformation enabler, or a deep functional enhancement.

1. Outward AI: AI as a revenue generator (AI as a product/service)

These companies place AI front & center in their business or operating model, using it as a core differentiator/growth driver. AI isn’t just a supporting tool, it’s a core product or a revenue enabler.

Characteristics

AI-powered products, services, or business models.
AI as a monetizable asset—from AI-powered subscriptions to new data-driven services.
Heavy investment in proprietary AI capabilities and R&D.

Examples of AI as a revenue generator

Spotify

AI-driven personalized playlists and audio discovery models directly impact engagement and subscription revenue.

Tesla

AI-powered autonomous driving as a central value proposition.

OpenAI

AI-based GPT models offered as a core product.

Netflix

AI-powered content recommendation drives customer retention and revenue.

2. Holistic AI: AI as an enterprise-wide transformation engine

Organizations in this category view AI as a cross-functional enabler, embedding it across all business units and operations to drive efficiency, resilience, and decision-making.

Characteristics

AI-driven automation and efficiency across multiple domains.
Focus on real-time data, predictive analytics, and proactive decision-making.
AI used to enhance resilience, adapting dynamically to changing business conditions.

Examples of AI as an enterprise-wide transformation engine

Walmart

AI-driven supply chain optimization, inventory management, and dynamic pricing.

Amazon

AI integrated across logistics, personalization, fraud detection, fulfillment centers.

Shell

AI-powered predictive maintenance, drilling optimizations, energy efficiency initiatives.

Unilever

AI adoption across marketing, R&D, and sustainable sourcing.

3. Deep AI: AI embedded in a specific domain

Rather than applying AI broadly, these companies go deep, embedding AI into one key function or business area where it can drive maximum impact.

Characteristics

AI is highly specialized within a key operational area.
Strong integration with industry-specific workflows or customer touchpoints.
AI-driven transformation focused on single domain vs enterprise-wide implementation.

Examples of AI embedded in a specific domain

Klarna

AI-powered customer service automation, using AI chatbots and predictive insights to enhance user experience.

John Deere

AI-driven precision agriculture, optimizing crop yields with machine learning.

Starbucks

AI-powered loyalty program personalization and real-time menu adjustments.

Lufthansa

AI in customer support and flight disruption management.

Why archetypes matters (and the relevance of different AI approaches)

As AI adoption accelerates, organizations face a critical choice: How will AI shape their future business model and operations? The three AI archetypes – Outward, Holistic, and Deep – provide a strategic lens to help companies clarify their AI ambitions, align investments, and make informed decisions about where and how AI should drive value.

1. Strategic clarity: Matching AI ambition to business goals

Understanding these AI archetypes helps companies answer key strategic questions:

Is AI a core revenue driver (Outward AI)?
Should AI be embedded across all functions to enhance decision-making and resilience (Holistic AI)?
Is the best approach to specialize AI in a specific domain for maximum impact (Deep AI)?

By defining an AI archetype early, organizations can ensure that AI adoption is purpose-driven, not just exploratory.

2. Investment & prioritization: Where to focus resources

Outward AI companies should prioritize AI R&D, build proprietary models, and monetize AI services.
Holistic AI companies need to invest in AI infrastructure, cross-functional data sharing, and real-time decision systems.
Deep AI adopters must optimize AI in a focused domain, ensuring deep integration with business processes.

Without a clear AI strategy, companies risk spreading resources too thin or investing in AI without clear ROI expectations.

3. Operating model alignment: Structuring AI for impact

Each archetype requires different organizational capabilities, governance structures, and AI talent strategies:

Outward AI needs strong AI innovation teams, product managers, and scalable AI infrastructure.
Holistic AI requires a company-wide AI governance framework and cross-functional collaboration.
Deep AI demands deep expertise in one domain, ensuring seamless AI integration into workflows.

Without a structured AI operating model, even the best AI strategies may fail to scale.

4. Competitive differentiation: AI as a market advantage

AI is becoming a key differentiator in nearly every industry.

Outward AI companies gain a competitive edge by launching AI-powered products/services before competitors.
Holistic AI adopters win through faster, data-driven decision-making and operational resilience.
Deep AI organizations create best-in-class AI-driven experiences in their focus area (e.g., Klarna in customer service).

A misaligned AI strategy could mean falling behind industry leaders who use AI more effectively.

5. AI scalability & long-term success

Some companies start with Deep AI and later evolve into a Holistic AI approach.
Others start with Outward AI innovation, then expand AI across internal functions.

Having a clear AI archetype helps organizations plan for future AI expansion, ensuring that today’s investments align with long-term AI maturity.

Key takeaways

AI is not a one-size-fits-all journey. The three AI archetypes help companies structure AI adoption strategically.
Clarity in AI ambition prevents wasted investments and accelerates ROI.
Aligning AI with business strategy ensures scalability and long-term competitive advantage.
Companies should evolve their AI roadmap based on their chosen archetype – Outward, Holistic, or Deep.

Let’s talk about how you structure your AI adoption.

Laura Stevens

Managing Director of Data & AI

Laura Stevens, PhD, is the Managing Director of Data & AI, bringing a unique blend of strategic vision, analytical expertise, and leadership acumen. With a background in neuropsychology, business consulting and organizational transformation, she has successfully navigated a career spanning academia, consulting, and industry leadership. As a former VP Data & AI in an international organization, Laura has led large-scale Data & AI teams covering data science, machine learning, data engineering, data governance, and visualization. She is passionate about leading organizations through their data & AI transformation.

laura.stevens@boardofinnovation.com

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The post 3 archetypes: how companies approach AI adoption appeared first on BOI (Board of Innovation).

The AI-powered marketing funnel – what marketers can do

Signe Damgaard — Mon, 23 Jun 2025 14:16:09 +0000

The AI-powered marketing funnel

What growth marketers can do as AI owns discovery, comparison, and purchase.

Jo McKinney

Growth Officer Americas

When the journey happens in a sentence

Ask ChatGPT a question like “what’s the best running shoe for flat feet under $120” and AI returns a top pick, explains why, checks inventory, and places the order. In 2025, a single prompt can answer, narrow options, and complete payment without opening a browser. The familiar sequence of touchpoints collapses, but at the same time, opportunity expands for brands that focus on content depth, structured data, and instant fulfilment.

Read on for a hands-on roadmap: where to rewrite, what to re-structure, and which metrics help you know if you’re winning when a single prompt can now decide the sale.

Awareness – a zero-click reality

At the awareness stage, consumers used to find brands via search engines, social media, or ads. Now, generative AI is changing that up. A Bain & Company survey found that 80% of consumers use zero-click AI answers for at least 40% of their searches, meaning they get what they need without visiting a site. That’s led to organic traffic drops in the double digits.

Profound analyzed 30 million AI responses and found ChatGPT cited Wikipedia nearly 50% of the time, while Google AI Overviews leaned on Reddit and YouTube. Perplexity also favored Reddit. Every time a paragraph replaces a link, a visit disappears. WARC reports publishers in Google’s AI Overviews beta lost 18% to 64% of organic clicks. Semrush data on HubSpot’s traffic confirms that by December 2024, blog traffic accounted for just 42% of their organic sessions, down from 77% in January 2024.

Still, Google’s May 2025 guidance confirms that valuable, fast-loading, technically sound pages with supporting media continue to surface. When users do click, they stay longer and convert better.

What to do for awareness: Aim to be the source AI cites in its answer

Create genuinely helpful top-of-funnel assets that are relevant, comprehensive, and directly answer common user questions:
- Educational articles
- Buying guides
- How-to tutorials
Skip tons of keywords and thin content
Produce clear, authoritative resources that an AI can lift and summarize (which is what it does)

Consideration – from research to recommendation in one chat

In the consideration stage, buyers evaluate options (reading reviews, comparing features, seeking recommendations in various forums). Now, users can ask, “Is Brand A or B better for me?” and get a strong answer synthesized from lots of different sources. AI favors pages written like answers, not ads. Pages with schema markup, subheads, and plainspoken language are easier for AIs to extract. If your product pages read like Q&A (What problem does it solve? Who shouldn’t buy it? How does it compare?), the AI can quote you outright.

For brands, this means the traditional mid-funnel touchpoints (comparison pages, customer review platforms, lengthy spec sheets) might get bypassed. A consumer who might have spent an evening reading blog comparisons or watching YouTube reviews could now have their options narrowed down by ChatGPT in seconds. Traffic that used to flow to third-party review sites or your own product comparison pages is diminished. If your product isn’t among AI’s curated picks, you may not even be in the running for consideration.

But being included in AI recommendations builds trust quickly. If a user values sustainability and your skincare brand is eco-friendly, the AI may surface that contextually in its recommendation. This is effectively hyper-personalized, context-aware positioning that would be hard to replicate with a traditionally broad marketing message.

What to do for consideration: Hyper-personalized, context-aware positioning

Make your product data rich and AI-readable.
- Maintain detailed specs, FAQs, and descriptions (keep them updated)
- Use clear headers and apply schema markup
Leverage public sentiment
- Encourage strong, authentic reviews
- Surface review snippets for AI to quote
Format product pages as if answering a question (helps people and machines)
- Comparison tables
- Real review snippets
- Problem-solution narratives (I normally hate these but AI likes them)

In the messy middle of consideration, depth wins over brand size or clout. Brands offering clear pros and cons, verified reviews, and honest comparisons are cited even when bigger players are not. Yes, fewer users reach your page, but those who do are ready to act because they’ve already done their homework (or their AI has).

Conversion – the rise of one-click (or no-click) commerce

Perhaps the biggest change is happening at the bottom of the funnel: conversion. AI is not just guiding decisions, it’s beginning to execute them. We are entering the age of conversational commerce, where a customer can go from “I think I’ll buy this” to completing a purchase without ever leaving a chat interface or search results page.

Adobe Analytics reports that generative AI traffic to retail sites grew by over 1200% between mid-2024 and early 2025. Orders initiated through AI chat are converting at significantly higher rates than standard organic sessions. Fewer steps mean fewer drop-offs. In Shopify’s ChatGPT sales-channel pilot (pilot launched in April), users could say, “Order two more bags of the light roast,” get a confirmation, and complete the purchase, entirely in dialogue. Copilot can build an Instacart cart from a recipe card. The funnel stages blur into one.

This has two major implications. First, web analytics may show fewer checkout visits, not because of lost sales, but because they happen elsewhere. PYMNTS describes this shift as compressing browsing, selection, and checkout into one conversation.

Second, brands may lose visibility into the customer journey. If the AI agent owns the transaction, brands may miss chances to upsell or connect. You may see the order but not know where it came from. That said, smoother checkout raises conversion rates fast. Adobe shows that AI-driven search is rapidly outperforming traditional search in final-stage conversion.

What to do for conversion: Ready your e-commerce stack for AI-initiated orders

Shopify merchants
- Test the ChatGPT sales-channel beta as soon as it is offered
Broaden beyond Shopify
- Enable chat-commerce in WhatsApp or Facebook Messenger
- Activate social commerce checkouts on Instagram or TikTok
- Pilot voice buying through Alexa or Google Assistant
Minimize friction, and you’ll see gains.

Why fewer clicks can signal a stronger engine

By now it’s clear that traffic patterns are shifting. You may experience drops in website visitors at various funnel stages (i.e. fewer people reading your top-of-funnel blog posts, or fewer reading your product pages) and a gut reaction might be to panic. But it’s important to interpret this change properly: some of that traffic wasn’t converting anyway, and some of it is being replaced by better traffic. When discovery, evaluation, and purchase merge into one interaction, there are fewer opportunities for drop-off, and stronger signals of purchase intent. This shift often means less traffic overall, but more meaningful engagement from those who do make it to your site.

Building for the agentic era

The most effective answer is product-led SEO, or content rooted in genuine user tasks, not keywords. Product-led SEO means creating content that draws in your target customers by solving their problems and integrating your product as part of the solution. It’s a shift from writing generic articles for clicks, to making content that is deeply tied to your product’s value proposition and the user’s needs. Map the real questions, publish complete solutions, structure information so a parser can lift it and surround claims with proof. Google’s own checklist still applies, the difference is that assistants now enforce it ruthlessly.

Marketing KPIs are changing too

Measurement must evolve as well. Page views and sessions matter less than three new signals:

Citations inside AI answers
Traffic that originates from those answers and
Sales made without a visible cart page (i.e. ai facilitated checkouts).

Add these to the dashboard or risk misreading performance.

Adapt one stage at a time to build a fast funnel

When AI guides each step in the consumer journey or marketing funnel, marketing’s job shifts from driving clicks to supplying perfect answers and seamless fulfilment. Brands that master both will own the fastest funnel. Here are a few key takeaways for marketers:

AI is the new middleman. Prepare for journeys with no traditional clicks.
Authority plus depth wins. One great page beats ten shallow ones.
Conversational checkout doubles speed. Integrate catalogues with chat-friendly endpoints.
Revise dashboards. Add share of AI citations and agent-driven revenue next to classic SEO metrics.
Act now. Rewrite one foundational guide so that it deserves an AI citation, turn one comparison page into an answer and activate one chat-based order path. Each small step tightens the link between question and purchase.

Getting started with your AI optimized marketing funnel

Complete these five moves and you’ll know which answers AI trusts, how answer-style content converts, and how much revenue already flows through AI channels.

1. Do an AI visibility audit

Ask ChatGPT, Bing AI, and Google AI the ten beginner and comparison questions you care about. Note which brands they cite and where you’re missing.

2. Publish three definitive answers

Fill the gaps from the audit with three deep resources (FAQ + schema + visuals). Aim to become the paragraph an assistant quotes.

3. Rewrite one product page into “answer format”

Add a pros/cons table, a competitor column and real review snippets (with Review schema) so the page reads like the response an AI would give.

4. Make your store assistant-ready (for ecomm brands)

Conversational commerce is here, and Shopify has begun rolling out its ChatGPT integration. While some users now see in-chat “Buy Now” buttons, most purchases still redirect to the merchant’s Shopify checkout page (this will transition over time). Once available, there’s no setup required. Just make sure your product titles, images, pricing, and reviews are clean and complete in your Shopify backend. For broader reach, enable a WhatsApp Shop by uploading your product catalog through the WhatsApp Business App or API. With new channels active, your storefront becomes shoppable within conversations.

5. Add new KPIs to the dashboard

Track share of AI citations, traffic from AI, and AI-driven sales alongside sessions and conversion rate. Seeing these numbers weekly builds momentum.

Let’s talk about how you can use AI to drive real business impact.

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The post The AI-powered marketing funnel – what marketers can do appeared first on BOI (Board of Innovation).

How to use AI to surface evolving trends (even before they arise)

Mikulas — Tue, 17 Jun 2025 14:53:42 +0000

How to use AI to surface evolving trends (even before they arise)

Silvia Segura

Strategist Lead

Leo Velásquez

Strategist

Consumer behavior is not static.

With increased access to information, social media and social movements, our behavior shifts more rapidly than ever, making it difficult to keep up with consumer segmentation.

That’s where AI comes into play:

AI-powered clustering can help uncover new micro-segments and surface evolving trends and consumer preferences that traditional methods miss.

The problem with traditional segmentation

People don’t fit into neat boxes anymore. Static segments like “Gen Z” or “Millennials” miss the nuance.

Today’s consumers are fluid and interests shift with each scroll, like, or trend. Relying on traditional and static segmentation can create missed opportunities for businesses.

Enter AI-powered clustering

AI helps us move beyond basic demographics. Clustering algorithms like k-means and hierarchical clustering group people based on what actually matters:

their attitudes,
actual behaviors,
and preferences on specific issues.

Not just age or buying power.

From

To

Manual and time-consuming sifting through large data sets; looking for evident (surface-level) patterns

AI-driven synthesis and uncovering of deep consumer insights from large data sets

Traditional segmentation based on static, historical demographic and behavioral data

Dynamic micro segments; continuously updated with new behavioral and reactionary data

Brands reacting to consumers’ past behavior, expressed needs and current trends

Brands predicting consumer needs and desired, including unexpressed preferences

Consumer insights team interpreting data reactively; relying on outdated frameworks

Proactively using incoming, real-time data to continually update and evolve segmentation frameworks

Marketing strategies designed for static consumer segments

Dynamic marketing that adapts to evolving and emerging micro segments

Real-world example of AI-powered research

AI-powered clustering in traditional research

In a recent client project on hygiene products, we used clustering techniques to uncover unique consumer groups we’d never see with standard filters: Low Concern Minimalists

They weren’t defined by gender or income, but by a shared mindset and interested in unconventional benefits like advanced cleansing formats or unconventional wellness claims.

Without clustering, this valuable insight would have likely slipped through the cracks. By letting the data guide us, we uncovered a micro-segment with a unique combination of characteristics, behaviors, needs, and preferences—and revealed entirely new opportunities to connect with them through messaging that truly speaks their language.

Visual representation of hygiene products consumer segments identified via K-means clustering.

Each dot represents a respondent; colors indicate cluster assignment.

A new type of AI-powered research: Agentic Social Listening

The potential of AI-powered clustering goes far beyond traditional research.

In a pioneering project with a fintech brand preparing to launch a credit card for Gen Z consumers, we used advanced clustering methods to gain a deep understanding of the “under-25” audience (their attitudes, cultural cues, and content preferences) through the lens of their organic online behavior.

Using Agentic Social Listening, we gathered over 150,000 social media mentions from platforms like TikTok, Reddit, and Instagram, and extracted rich signals from video transcripts, comments, and visuals, enabling us to apply visual clue clustering. That method organizes content based on shared aesthetic and contextual patterns.

Through this approach, we identified 10+ distinct Gen Z sub-segments, each built around a unique cultural theme—from sports and anime to sustainable fashion and high-adrenaline interests like motorcycles and speed sports.

Dive further with this session from AUTONOMOUS: Using AI to capture signals of change

From cluster to create: How to actually use them?

Identifying clusters is just step one. The real power comes when you activate them. Fast.

Here’s how we do it

Once micro-segments are mapped, we plug their behavioral and cultural data directly into creative agents.

These AI models generate tailored campaign assets on the fly: everything from messaging and visuals to packaging ideas and content formats, aligned with each segment’s emotional and aesthetic codes.

The agents we built autonomously created visual mockups and messaging tailored to this group’s tone, culture, and content style. These outputs weren’t static; they evolved across iterations with different clusters, constantly optimizing communication to better engage each micro-segment.

One of the biggest opportunities this method unlocks is the shift from insight to immediate execution. With these models, you’re not just discovering who your audience is. You’re acting on that intelligence, instantly.

Creative agents take the cluster-specific data (like visual preferences, tone, or behavioral pain points) and use it to generate ready-to-use brand assets, campaign ideas, packaging mockups, and product messaging that feels hyper-personalized.

That means no lag between insight and execution.

This is especially powerful in fast-moving categories like lifestyle, consumer goods or youth finance. This setup lets you:

Skip the middle step: go from segment to campaign-ready creative instantly
Tailor design, tone, and storytelling to match each group’s vibe
Refresh content dynamically as clusters evolve

It’s not about “understanding” your audience anymore but about creating for them in real time.

Why this matters: 10 opportunities this brings to businesses

1. Understanding evolving behavior

Group people or entities based on real-world behavior—what they do, not just who they are.

Use it for

Spotting changing user habits, lifestyle shifts, or usage trends.

Example

Detect clusters of people who suddenly start cooking at home more, or those reducing digital screen time—regardless of their demographics.

2. Responding to shifts in real time

Continuously update clusters as new data flows in—capturing emerging needs or patterns.

Use it for

Adaptive systems that adjust on the fly (like services, products, experiences).

Example

Re-cluster users weekly to reflect current preferences or environmental conditions—like shifting from “travel planning” to “budget anxiety.”

3. Finding the hidden common denominator

Group people/things based on deep similarities—often hidden in complex data.

Use it for

Uncovering surprising connections that wouldn’t appear in top-level analytics.

Example

Grouping users across different platforms who respond to the same type of humor or visual format.

4. Building better prototypes, faster

Inform prototyping by showing the diversity within your audience or system.

Use it for

Testing concepts across real-life behavioral clusters—not arbitrary segments.

Example

Create 3–5 concept variations matched to real-world clusters (e.g. “convenience-maximizers” vs. “value-seekers”) for rapid iteration.

5. Compressing noisy data into actionable insight

Summarize messy inputs (qualitative surveys, usage logs, open text) into coherent clusters.

Use it for

Making sense of diverse feedback or unpredictable systems.

Example

Analyze thousands of data points across sources (like social media) to condense and form distinct groups.

6. Revealing identity beyond labels

Build fluid personas based on behavior and belief systems rather than static attributes.

Use it for

Creating more nuanced profiles that reflect lived experience.

Example

Identify people who make eco-conscious choices but reject “green” branding, revealing tensions between action and identity.

7. Guiding personalization without overfitting

Create flexible groups that allow for meaningful personalization, without assuming you know exactly what each individual wants.

Use it for

Balancing personalization with scalability.

Example

Recommend solutions based on flexible clusters of intent (e.g., “explorers” vs. “optimizers”) rather than overly-specific personal data.

8. Identifying early signals

Detect early signals forming into emerging behaviors or patterns.

Use it for

Foresight, innovation scouting, trend monitoring.

Example

Spotting a new type of decision-making logic emerging among users before it goes mainstream.

9. Localizing decision-making

Apply clustering dynamically within a specific geography, culture, or community.

Use it for

Designing interventions, policies, or solutions tailored to real-life contexts.

Example

Instead of applying a global persona, cluster by actual on-the-ground realities (e.g., “urban heat avoiders” vs. “resilient commuters”).

10. AI-assisted strategic foresight

Use dynamic clustering to simulate how audiences may evolve under different future scenarios.

Use it for

Planning resilient, future-ready strategies (for product lines, policies, or services).

Example

See how today’s niche segments (e.g., “tech-cautious eco-maximalists”) might grow or shrink under different tech or economic trends.

Do I need to be a programmer?

No! That’s the beauty of it.

You can use no-code tools like Conjointly Clustering Demo or OpinionX Cluster Tab, AI-powered platforms that automatically analyze and group survey responses based on similar responses or unique, strong opinions.

This allows you to identify emerging micro-segments quickly and with greater precision without having to sift through data manually.

Opinion X view of the clustering feature

Or, if you’re feeling adventurous, use simple Python code to run your own clustering models.

Here’s a quick example:

				
					from sklearn.cluster import KMeans
import pandas as pd

# Example: Load or prepare your cleaned and relevant dataset
# Ensure the DataFrame `df` is preprocessed and ready for clustering

# Step 1: Select features for clustering
features = df[["feature_1", "feature_2", "feature_3"]]  

# Step 2: Define the number of clusters
num_clusters = 3

# Step 3: Initialize and fit the KMeans model
kmeans = KMeans(n_clusters=num_clusters, random_state=42, n_init=10)
df["Cluster_Label"] = kmeans.fit_predict(features)

# Step 4: Create and display cluster profiles
# Replace 'profile_columns' with columns you want to profile the clusters by
profile_columns = ["feature_1", "feature_2", "feature_3"]
cluster_profiles = df.groupby("Cluster_Label")[profile_columns].mean().round(2)

# Display results
print(cluster_profiles)

Don’t want to code? Ask an LLM to help you out. With a well-crafted prompt, these models can write the code for you, or even run the clustering for you.

AI is changing insights

AI-powered clustering isn’t just a smarter way to segment. You can use it as a strategic unlock across your whole organization.

Whether you’re a data analyst, marketer, or brand strategist, adopting AI-driven insights will not only help you discover emerging consumer trends but also allow you to anticipate needs and preferences before they become widespread.

Stop guessing who your audience is. Let AI show you who’s actually out there and how to build for them, with them, in real time.

Let’s talk about how you can use AI to drive real business impact.

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The post How to use AI to surface evolving trends (even before they arise) appeared first on BOI (Board of Innovation).

10 AI questions every executive should be asking

Mikulas — Tue, 27 May 2025 15:09:07 +0000

10 AI questions every executive should be asking

Vincent Pirenne

Global Partner & Head of AI Strategy (EMEA)

AI is no longer just a tech initiative.

It’s becoming the lens through which strategy, operations, and growth are viewed. But while most companies are experimenting with AI, very few are doing it with the right strategic intent. And that often comes down to one thing – leaders aren’t asking the right questions.

The goal isn’t to chase hype or stack up pilots. The goal is to drive impact. And that starts by asking better questions.

“The goal isn’t to chase hype or stack up pilots. The goal is to drive impact. And that starts by asking better questions.”

⸻ Vincent Pirenne

Here are 10 questions we believe every executive team should be tackling right now.

These won’t solve everything, but they will help spark the conversations that move you forward.

1. Are we building the AI operating model we’ll need in two years?

AI is moving fast, but your operating model probably isn’t. Many companies still manage AI like IT projects. But the next phase requires something else entirely.

If you’re still using legacy structures to drive next-gen transformation, it’s time to redesign the system.

AI Strategy

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2. How do we prioritize AI use cases that scale across the organization?

Scaling AI isn’t about launching more pilots. It’s about identifying the few initiatives with real impact potential and doubling down.

Without clear priorities, AI becomes an expensive distraction. AI has the power to transform, but only when applied with laser focus to what matters most.

Johnson & Johnson recently narrowed down from 900+ AI ideas to a select few that were moving the needle.

“Now we’ve moved from the thousand flowers to a prioritized focus.”

⸻ J&J’s CIO for WSJ, 2025

3. Are we building differentiators, or just catching up?

Many companies use AI to reduce costs. Fewer use it to create something new.

The real strategic opportunity with AI lies in differentiation — rethinking how you deliver value, not just doing things faster or cheaper.

“If your AI roadmap is only about finding efficiency, you’re not playing to win, you’re just trying not to fall behind.”

⸻ Vincent Pirenne

We argue that AI adoption is unfolding in three waves:

Wave 1: Time, cost, efficiency
Wave 2: Quality, better output
Wave 3: New systems, transformation

Webinar

(Re)Watch our webinar on how to set a solid AI strategy across the three waves

4. What data foundation do we need to unlock AI’s potential?

AI success doesn’t start with a model — it starts with data. Clean, connected, labeled, and accessible. If your data is siloed, incomplete, or buried in legacy systems, even the best models won’t help.

“Most companies overestimate their AI readiness and underestimate their data problems.”

⸻ Vincent Pirenne

5. How do we prevent siloed AI experiments?

It’s easy to let teams launch experiments in isolation. But without shared learning, central oversight, and a clear plan for scaling, these initiatives rarely deliver value. Create a system to track, review, and sunset pilots that don’t scale.

Continue reading

Dive into our POV on why a bottom-up approach to AI is doomed to fail

6. What’s our plan for developing internal AI talent?

If your AI expertise lives mostly in vendor contracts, it’s worth asking whether you’re building enough capability internally. External partners can play a key role in accelerating progress, but the long-term impact also depends on internal teams having the skills and context to drive initiatives forward. Offer learning paths, build hybrid roles, and enable people to rotate into AI squads.

7. What are we doing to increase AI fluency across leadership?

AI is no longer just for data teams. It’s a board-level topic. As we pointed out in our latest Fast Company article, companies led by AI-literate teams are more likely to spot value and act on it. Start by building shared language and base knowledge across your leadership bench.

“Companies led by AI-literate teams are more likely to identify where AI can create value — and act on it.”

⸻ Fast Company, 2025

8. Is our vision strong enough to weather future AI shifts?

The tools will change. The hype cycles will come and go. What matters is whether your vision is durable. Are you building toward a future-proof business model that gets better with AI? Or are you stuck chasing trends?

9. Who in our leadership team is accountable for AI?

This might be the most important question. If no one owns it, it won’t happen. AI doesn’t belong in a silo; it needs cross-functional leadership, clear KPIs, and integration into core strategy.

10. Do we understand how agents will reshape our workforce?

With agents becoming more capable, certain tasks may no longer need direct human coordination. That’s not just automation, that’s a shift in your workforce. Have you through about what happens when agents start owning outcomes, not just tasks? Which parts of your value chain change? Having a clear point of view now will help you lead the shift rather than react to it.

These questions aren’t meant to be overwhelming. They’re meant to start better conversations that move you from exploration to execution, from scattered pilots to real transformation.

And in a world that’s becoming more AI-native by the day, these conversations might be your strongest competitive edge.

Keen to dive deeper?

Drop us a note.

Vincent Pirenne

Global Partner & Head of AI Strategy (EMEA)

Vincent is a senior leader in AI strategy and business transformation, helping Fortune 500 organizations unlock the full potential of artificial intelligence. As a Global Partner at Board of Innovation, he specializes in shaping AI-driven growth strategies for consumer goods, retail, and technology leaders across the USA and Europe. His expertise lies at the intersection of AI, business strategy, and enterprise transformation, helping senior executives navigate AI adoption and scale AI-driven decision-making. He’s focussed on helping organizations build future-ready AI strategies that deliver real business outcomes.

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The data lake trap: why many AI strategies stall?

Mikulas — Fri, 23 May 2025 13:32:53 +0000

The data lake trap:
Why many AI strategies stall

Amir Ouki

Managing Director,

Applied AI & Technology

Many companies kick off their AI journey by investing in large-scale data lake initiatives: terabytes of historical data are centralized, cleaned, tagged, and stored with the hope that it will serve as a launchpad for AI.

The assumption is simple: to do AI right, you need all your data in one place first.

But in reality, that’s often the wrong sequence.

Data lakes aren’t always a prerequisite for AI. They’re an outcome.

The most effective AI initiatives begin not with storage but with clear business goals. To support that, you need the right data, not all the data.

AI is a capability for improving decisions:

What to recommend?
When to intervene?
How to optimize an outcome?

A focused use case (like reducing cart abandonment or predicting maintenance failures) allows you to work backward to identify the minimum viable dataset required.

Once you’ve proven that the multiple AI solutions deliver impact, you can invest in infrastructure that makes them repeatable and scalable. That might include a data lake, but now it’s in service of something, not speculative.

Building for storage vs. building for decisions

The temptation to centralize all data is understandable. It feels like a future-proofing move. But without clear use cases driving it, a data lake often becomes a data swamp: difficult to navigate, hard to trust, and expensive to maintain.

The alternative is to build just enough.

When the starting point is a valuable decision worth improving, the data stack can be smaller, faster, and far more targeted. Instead of a massive upfront investment, you build incrementally as your needs evolve.

What comes first: the AI or the lake?

Neither. What comes first is the decision.

AI strategy should begin with identifying high-impact decisions that could benefit from augmentation or automation. Once you know the decision, you can figure out the data needed to support it. Then you can define how to collect, store, and access that data.

This decision-first sequencing is far more effective than a bottom-up infrastructure-led approach, which risks creating technical complexity without strategic clarity.

When a data lake is the right first step

There are clearly situations where starting with a data lake is justified. If your organization already has a well-defined set of AI or analytics use cases across departments and you’re facing systemic issues with data access, quality, or duplication, a data lake can help create coherence.

In these cases, a lake can create leverage. But even then, its structure should be defined by the actual decisions and use cases it needs to support. Not by the urge to centralize everything just in case.

AI strategy ≠ data lake strategy

AI success is about shortening the loop between insight and action. You don’t need a decade of historical data to improve how decisions are made tomorrow. You need usable, relevant data tied to specific business outcomes.

Before you greenlight your next big data lake initiative, ask yourself: what decisions are we trying to improve, and what is the fastest path to improving them?

You may find the lake can wait.

AI Strategy Playbook

This AI Strategy Playbook is your guide to win in the age of AI. Learn how to build AI capabilities around real business decisions.

Amir Ouki

Managing Director, Applied AI & Technology

Amir leads BOI’s global team of product strategists, designers, and engineers in designing and building AI technology that transforms roles, functions, and businesses. Amir loves to solve complex real world challenges that have an immediate impact, and is especially focused on KPI-led software that drives growth and innovation across the top and bottom line. He can often be found (objectively) evaluating and assessing new technologies that could benefit our clients and has launched products with Anthropic, Apple, Netflix, Palantir, Google, Twitch, Bank of America, and others.

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Scaling enterprise innovation beyond pilots: 5 lessons from Bayer’s AI startup

Mikulas — Wed, 21 May 2025 15:01:00 +0000

Scaling enterprise innovation beyond pilots: 5 lessons from Bayer’s AI startup

Inside Bayer’s approach to building GenAI applications that deliver real value.

How do you go from sandbox experiments to fully deployed GenAI solutions that drive business value without getting lost in the hype?

That’s the question Bayer has been answering through its pioneering work in GenAI, particularly with its domain-specific model: E.L.Y. (Expert Learning for You)

In a recent episode of AI Insiders, Vincent Pirenne sat down with Dan Kurdys (Global Business Leader for GenAI at Bayer) and Sachi Desai (VP of AI Go-to-Market & Partnerships), and their story so far holds lessons for any organization looking to move past pilots into scalable innovation:

1. Start with the problem, not the tech.

Too many AI strategies start with the technology, not the problem. Bayer did the opposite:

Their GenAI journey began with a practical pain point: farmers were waiting too long for agronomic answers. Sales reps felt the strain of needing to show up as experts, and agronomists were overwhelmed trying to scale their knowledge.

That shaped the real problem: how to get timely, expert agronomic advice into the hands of frontline teams?

Everybody wants an answer 24 seven and there’s all kinds of questions at certain times of the year because decisions have to be made.

⸻ Dan Kurdys

In their experimentation, Bayer then found GenAI to be a strong force multiplier for teams that were overwhelmed, and by its adoption, they achieved 60% faster response time.

This clarity of purpose helped E.L.Y. find product-market fit early: GenAI wasn’t the solution from the outset; it became the best-fit solution once the problem was fully understood.

You start with a problem, and there’s a tool for about every problem. We happened to find that GenAI was a good tool for our problem

⸻ Dan Kurdys

2. Build fast, iterate faster.

The E.L.Y. team didn’t wait for a perfect strategy or enterprise-wide roadmap. They moved quickly by wrapping existing frontier models (like GPT-4) with retrieval-augmented generation (RAG) techniques and Bayer’s proprietary logic and documents.

All to get a working prototype into users’ hands.

It worked. Compared to base models, the tool delivered:

47% more accuracy on a 5,000-question benchmark
20% better breadth of knowledge recall
60% faster response time in field tests

We proved our data mattered … And once we saw that, we just kept running the next experiment.

⸻ Dan Kurdys

The team kept focusing on running experiments; each one designed to unlock a new constraint: accuracy, latency, or cost.

As new needs emerged, they adapted the stack. Eventually, they tested smaller language models to strike a better balance across performance and cost.

3. Make innovation scalable by design.

Instead of hardcoding use cases, the E.L.Y. team created an SDK-based architecture that lets different teams across Bayer spin up new applications fast on top of the existing capabilities.

One core insight: their frontline teams weren’t going to open laptops in the middle of a cornfield. A mobile app was essential:

You’re not opening a laptop in the middle of a cornfield, the mobile app drove the engagement.

⸻ Sachi Desai

Their SDK now powers both the core E.L.Y. app and other internal tools that serve curated agronomic knowledge whenever and wherever it’s needed. This approach also enables the team to continuously test new GenAI models behind the scenes: future-proofing their infrastructure.

4. Culture is the hidden infrastructure

Technology alone doesn’t drive adoption. People do.

Bayer’s Dynamic Shared Ownership (DSO) model enabled the E.L.Y. team to operate like an internal startup: lean, federated, and empowered. Experts from agronomy and knowledge transfer teams shaped onboarding flows and testing protocols from day one.

This cultural setup made a huge difference in rollout. One example: the ability for reps to auto-generate localized, agronomically accurate conversation scripts continues to impress internally:

It blows our people’s minds. They’re like: my gosh, I now have it in my power to create custom collateral that is localized, that has my expertise in positioning.

⸻ Dan Kurdys

In short, GenAI wasn’t imposed top-down. It was built with the people who needed it most.

5. From use case to movement

What began as a tool for agronomic Q&A has become a platform: a bridge between Bayer’s deep (often inaccessible) knowledge and frontline action.

Much of the company’s most valuable expertise (housed in internal PDFs, legacy systems, or the heads of experts) was out of reach for those who needed it most:

The best knowledge in ag often lives in portals no one reads. We’re making it accessible at the moment of need.

⸻ Dan Kurdys

The biggest breakthrough wasn’t flashy automation. It was simplicity. As Dan Kurdys put it: “Sometimes the real innovation is just replacing paper.” That focus on reducing friction (not reinventing the wheel) has helped E.L.Y. build momentum beyond its original scope and set a new standard for how Bayer approaches innovation at scale.

Advice to leaders: don’t wait to get it perfect

For companies trying to break out of AI pilot purgatory, Dan and Sachi offer clear advice:

Find the knowledge gap in your business. Almost every enterprise has one.
Start small with structured experiments, not 10-year business plans.
Use results to earn your next round of support.

And remember: you’re not building an LLM. You’re delivering an outcome.

⸻ Sachi Desai

What BOI sees here

Bayer’s E.L.Y. team is a successful blueprint for enterprises serious about GenAI: start with the business problem, move fast with modular infrastructure, and scale through cultural alignment; not just tooling.

This isn’t just a GenAI success story. It’s what happens when AI is embedded with intent, speed, and the mindset to solve real problems first.

Let’s talk about how you can turn AI ambition into real business impact with a clear strategy.

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How to scale AI across the Enterprise

Mikulas — Fri, 16 May 2025 11:47:15 +0000

How to scale AI across the Enterprise

Vincent Pirenne

Global Partner & Head of AI Strategy

(EMEA)

AI transformation in enterprises isn’t just about adding another internal chatbot or running another pilot in customer service. It’s about rethinking how your organization works at a fundamental level. And right now, too many companies are playing small. They’re testing use cases in isolation, staying in “pilot mode,” and failing to scale.

The real opportunity is making AI part of the way your business operates, not just something your innovation team experiments with.

Here are 8 provocations to help you reframe how AI can actually drive scalable change in your organization:

Treat AI as a capability, not a tool.

Most companies treat AI like a tool to plug into existing processes. But true transformation comes when you shift that mindset: AI isn’t a product, it’s a capability. That means investing in internal knowledge, building infrastructure, and aligning teams around what AI can unlock for your business.

Instead of asking “Where can we use AI?”, start asking “How will AI change what we do and how we do it?”

⸻ Vincent Pirenne

Build for your real workflows.

Too often, AI is layered on top of messy legacy systems and processes. That leads to patchy results. Real impact comes when you reimagine workflows around AI from the ground up. Where can AI remove friction? Automate low-value steps? Give experts more leverage?

This is what OpenAI calls “embedding AI into the way people actually work.”

⸻ Open AI: AI for the Enterprise

Not every AI use case is worth scaling.

You shouldn’t fall into the trap of chasing what’s feasible instead of what’s valuable. To scale AI with impact, leaders need to distinguish between high-leverage initiatives and well-intentioned distractions. The matrix below helps prioritize use cases based on two critical dimensions: their ability to differentiate your business and their potential to scale across the enterprise.

This is what OpenAI calls “embedding AI into the way people actually work.”

⸻ Open AI: AI for the Enterprise

Think in compounding cycles.

AI transformation is not a one-off. The earlier you start, the more value compounds over time. Each use case makes the next one easier. Each success builds buy-in. And every time you unblock a team or clean up a dataset, you make the next wave of AI stronger.

Duolingo launched 148 courses created with AI after sharing plans to replace contractors with AI.

⸻ TechCrunch, 2025

Find and grow AI talent

You can’t transform by buying or licensing external tools. You need internal muscle. That means hiring and training people who can think and build with AI, and giving them tools that don’t require deep ML expertise to get started. In a recent podcast episode with the VP AI go-to-market & Head of GenAI from Bayer, we dove into how the company successfully launched a new business model by selling AI licenses, as referenced by the WSJ.

Set the bar higher for automation.

Many companies automate tasks, but few automate outcomes. If your automation goals stop at eliminating low-level work, you’re leaving value on the table. What would it look like to automate end-to-end processes? Decision cycles? Customer journeys?

Microsoft CEO Satya Nadella recently revealed that AI now generates approximately 20–30% of the company’s internal code, a clear sign that AI agents are no longer experimental, but essential to modern enterprise workflows.

⸻ TechCrunch, 2025

Don’t wait for perfection.

Some leaders are holding back, waiting for AI to be flawless before they commit. But here’s the reality: human work isn’t flawless either. The goal isn’t perfection, it’s progress. What matters is improving output versus the status quo, with guardrails and human oversight in place. LLM performance is improving fast, and the longer you wait, the further behind you fall. You don’t need to go all-in on day one, but you do need to get in the game.

Every iteration teaches you more than another month of debating use cases.

⸻ Vincent Pirenne

Make leadership accountable and AI-fluent.

AI transformation won’t happen without executive ownership. Leadership needs to do more than support, they need to lead. That means being actively involved, setting the ambition, and making sure AI isn’t confined to IT. It also means raising the baseline understanding: if your executive team can’t speak fluently about AI’s opportunities and risks, it’s a liability. Build that literacy, make it part of the leadership culture, and don’t delegate it away.

Enterprise AI transformation isn’t easy. But it’s no longer optional either. The companies making real progress are doing more than testing ideas, they’re building the systems, talent, and habits that let AI scale. They’re embedding it into the way work gets done, from code generation to product launches. And they’re learning faster than the rest.

This shift isn’t about flashy demos. It’s about building momentum, shaping culture, and enabling your teams to move with confidence.

If you’re serious about AI, it’s time to stop experimenting at the edges and start embedding it into how your business runs.

Want to explore how you can become AI-native? Drop us a note.

Vincent Pirenne

Global Partner & Head of AI Strategy (EMEA)

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Why some leaders win and others struggle with AI

Signe Damgaard — Wed, 30 Apr 2025 11:30:33 +0000

Why some leaders win and others struggle with AI

Laura Stevens

Managing Director of Data & AI

AI is everywhere. It’s promoted as a game-changer, a disruptor, and a force that will redefine industries. Yet, despite the overwhelming buzz, many leaders struggle to grasp what AI truly means for their businesses.

What’s real, and what’s just hype?
Where should we invest, and what should we avoid?
How do we harness AI strategically without wasting resources?

These are the questions that executives across industries are asking. And the reality is, without AI fluency, organizations risk making misinformed decisions—either hesitating too long and falling behind competitors or rushing in and investing in initiatives that fail to deliver real business value.

AI is no longer a side project. It sits at the heart of how companies grow, compete and decide. Leaders who fail to build their AI fluency will struggle to navigate this transformation effectively.

Why leaders struggle with AI

The pace of AI advancement has been staggering. In just two years, AI has evolved from niche applications (and frankly poor results) to mainstream adoption across every industry.

Yet, most executives were not trained in AI. Unlike finance, marketing, or operations—where leaders have decades of experience to draw from—AI fluency is often an afterthought. Many leaders feel overwhelmed, struggling to keep up with AI’s rapid evolution and its implications for their organizations.

Common pitfalls

Unrealistic expectations – Expecting AI to deliver instant transformation without understanding its limitations.
Misinformed investments – Jumping into AI projects without clear business alignment, leading to wasted resources.
Resistance to adoption – Failing to integrate AI due to uncertainty, creating a competitive disadvantage.
Missed opportunities – Overlooking high-value AI applications that could drive real business impact.

Why AI Fluency matters

AI fluency isn’t about turning executives into data scientists—it’s about equipping them with the knowledge to make informed, strategic decisions to drive growth with AI. Leaders don’t need to code, but they do need to:

Understand AI’s role in the business – What AI can and cannot do, and how it aligns with business goals.
Be able to separate hype from potential to avoid unrealistic expectations and failed investments.
Identify high-impact AI opportunities – Recognizing where AI can drive measurable value.
Navigate AI risks and governance – Addressing ethical, regulatory, and operational challenges.
Develop an AI strategy – Moving from exploration to execution with a clear roadmap.

Without this foundation, leaders risk making decisions based on misinformation or fear, rather than strategy.

Dive deeper – watch our webinar on AI Fluency for Executives

Common leadership challenges in AI adoption

Through conversations with executives, we’ve identified recurring concerns that highlight why AI fluency is crucial:

1. "We have a huge legacy IT landscape. Can we still progress with AI?"

Many organizations assume that AI requires a complete digital overhaul. While modern infrastructure helps, AI can be integrated incrementally. The key is identifying AI applications that complement existing systems rather than overhauling them entirely. Good AI fluency helps leaders recognize these possibilities and avoid unnecessary disruption.

2. "How do we identify the most valuable AI opportunities?"

Not all AI investments generate business value. Leaders need to focus on AI applications that align with business priorities—whether that’s optimizing operations, or driving entirely new revenue streams. Understanding AI’s capabilities allows leaders to prioritize opportunities with the highest impact.

3. "How do we ensure AI investments deliver measurable business value?"

AI for AI’s sake is a recipe for wasted resources. AI fluency enables leaders to set realistic KPIs, track ROI, and align AI initiatives with broader strategic goals. Successful AI adoption isn’t just about innovation—it’s about solving real business problems.

4. "What are the first steps when we don’t have a clear AI vision?"

A lack of AI vision is common, but waiting too long to act can be costly. The first step is education. Leaders must build foundational AI knowledge to assess where and how AI fits within their organization. From there, they can develop a phased roadmap, starting with small, high-impact initiatives before scaling.

Bridging the AI fluency gap

To address these challenges, we’ve developed an Executive AI fluency session designed to equip leadership teams with the foundational AI fluency they need.

This four-hour session (with optional follow-ups) is built specifically for senior leaders who want to move beyond AI buzzwords and develop a clear, practical understanding of AI’s role in their business.

Through interactive discussions, real-world case studies, and expert insights, our Executive AI fluency session cuts through the noise and provides leaders with the knowledge they need to lead AI initiatives confidently.

Let’s connect and explore how AI can create real business impact for your organization.

Laura Stevens

Managing Director of Data & AI

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What it takes to integrate AI at scale: L’Oréal

Mikulas — Fri, 25 Apr 2025 08:38:18 +0000

What it takes to integrate AI at scale

Behind the scenes at L'Oréal

Vincent Pirenne

Global Partner & Head of AI Strategy (EMEA)

L’Oréal isn’t just experimenting with AI. They’ve embedded GenAI into the daily work of thousands of employees, from R&D to marketing to retail.

The company made GenAI tools like L’Oréal GPT a core part of how business gets done: they are operationalizing it at scale, and they’re doing it with a level of intentionality and cultural alignment that most organizations can learn from.

In a recent episode of AI Insiders, I spoke with Beatrice Dautzenberg, Global Director of Beauty Tech Services at L’Oréal. Here’s what stood out.

1. L’Oréal GPT: Secure, scalable, and widely adopted

Like many companies, L’Oréal developed its own scalable solution–L’Oréal GPT, so that every team can use generative AI securely.

“L’Oréal GPT ensures safe, secure, internal usage of GenAI.”

⸻ Beatrice Dautzenberg

L’Oréal GPT is a custom-built, enterprise-grade GenAI assistant that speeds up daily work, sparks new ideas, and drives better collaboration, all while strictly protecting IP and consumer data.

The results?

It’s been fully adopted across the business. L’Oréal teams use it for a variety of day-to-day tasks, from video analysis to content creation.

2. Augmentation, not automation

The philosophy behind L’Oréal’s AI rollout is simple: the goal isn’t to replace people, it’s to empower them.

“L’Oréal GPT ensures safe, secure, internal usage of GenAI.”

⸻ Beatrice Dautzenberg

Chemists now have digital lab companions. Marketers use the in-house CRE-I-TECH GenAI stack for creative tasks to generate copy and visuals. Media teams optimize spend and content with AI. The company’s culture is built for this shift: augmentation for every function, not just automation for a few.

“It’s clear the culture is absolutely right — and that’s what makes it work.”

⸻ Vincent Pirenne

3. AI literacy for all

AI skills aren’t optional at L’Oréal. From day one, the company invested in GenAI education for all, from executive teams down.

“We had GenAI training for all — mandatory from the start.”

⸻ Beatrice Dautzenberg

Crucially, this learning is continuous, not a one-off workshop. L’Oréal’s ‘Learning for Development’ department leads ongoing upskilling (not “training”), and executives are expected to lead by example–by embracing these new ways of working. This cultural alignment–not just tooling–is considered a key enabler of AI success at L’Oréal.

What sets L’Oréal apart

L’Oréal’s approach is a masterclass in embedding AI across an organization without creating chaos.

Clear governance,
strong ethical boundaries,
and a focus on real augmentation, not hype.

For any enterprise leader wondering how to get started: start with your people. Then give them the tools, training, and trust to explore what’s possible.

Listen to the full conversation with Beatrice on the AI Insiders podcast.

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Making AI work: A practical guide to finding and delivering real value

Signe Damgaard — Wed, 16 Apr 2025 08:45:30 +0000

Making AI work: a practical guide to finding and delivering real value

How leaders can cut through the noise and prioritize what matters.

Laura Stevens

Managing Director of Data & AI

There’s more fuzz around AI than ever, but far fewer companies know how to identify where the real value lies. We’ve all seen it: an executive team hears about a flashy AI use case at a competitor and suddenly there’s pressure to “do something with AI.” A pilot is launched. A dashboard is built. The hype fades. The ROI never materializes.
The challenge is that many organizations still treat AI as a feature instead of a strategic capability. They chase what’s trending instead of identifying where AI can truly move the needle.

“AI isn’t valuable because it’s advanced – it’s valuable when it solves something that matters.”

⸻ Laura Stevens

So how can leaders cut through the noise and focus on what will actually make a difference?

Through real-world practice and hard-earned lessons, we established a practical, 5-step approach that helps leadership teams identify high-value opportunities instead of just interesting ones.

1. Understand the market and competitive landscape

Before turning inward, zoom out. Ask:

What are the AI market drivers shaping our industry?
Where are competitors placing their bets?
What are adjacent industries doing that we’re not?
Which areas are attracting funding, partnerships, or patent activity?

Adopting an outside-in perspective doesn’t just help ensure your AI strategy is differentiating by spotting white spaces – it also makes it well-informed. It helps you understand the most important AI market drivers, emerging trends, and where the major players are placing their bets. Just as importantly, it allows you to cut through the noise and focus on what really matters.

This isn’t just about scanning the market. It’s equally about building AI fluency at the executive level. Just as leaders need financial literacy to make sound investment decisions, they need a baseline understanding of the AI landscape to make smart strategic calls. Without that, it’s easy to fall for hype or miss the real game.

2. Explore AI opportunities through two lenses

We’ve seen that many teams start by asking “Where can AI support the business?” While this is a perfectly valid question, recent evolutions in AI, especially the rise of foundation models and more general-purpose capabilities, have opened up an additional lens: AI is no longer just an enabler of business strategy, it has become a driver of business growth. Hence, there is substantial value in not just considering how to improve what you already do, but exploring what new offerings, services, or business models AI now makes possible.

Doing that well opens up a fundamentally different opportunity space, yet it requires a change in mindset: starting not from today’s pain points, but from a vision of what your business could become because of AI. Such a future-back lens helps you ask questions like “If we started from AI’s capabilities – not our current constraints – what could we create that wasn’t possible before?”.

We recommend using both as complementary lenses.

A. Business-led lens (Today → AI)

Ask: How can AI help us achieve our current goals faster, better, or cheaper?

This often leads to:

Quick wins through automation or optimization
Strong executive buy-in
Measurable improvements in operations, CX, or decision-making

Example: A telco client applied AI to optimize call center staffing – an unsexy but high-impact win.

B. AI-led lens (AI → New business)

Ask: If we started from AI’s capabilities, what new offerings or models could we imagine?

This brings into view:

AI-native products or service
New revenue streams
New ways of delivering value to your customers

Example: A media company we worked with used AI to simulate content virality before publishing. That idea didn’t come from their strategy deck – it came from asking, what’s possible with AI now that wasn’t before?

3. Run an AI opportunity scan across functions

After mapping the broader landscape and defining your opportunity lenses, the next step is to bring this down to the functional level. This is where the strategy starts to meet operations. Go function by function – Sales, Marketing, HR, Finance, Operations – and look for the signals of AI opportunity:

Repetitive tasks
Data-rich, insight-poor processes
Bottlenecks in decision-making

This scan helps surface concrete use cases and clusters of opportunity that are grounded in reality, not just ideas, setting the stage for meaningful prioritization.

Tip: Involve people on the ground. They often know where AI could help before leadership does.

Should we scan across all functions or just a few?
Start broad enough to spot big opportunities, but don’t stay wide for long. A light-touch scan across functions gives you a view of where to dig deeper. Then quickly narrow your focus to 2–3 priority domains where AI can make a real difference. This avoids “boiling the ocean” while still ensuring you don’t miss hidden gems.

AI transformation isn’t about exploring everything – it’s about knowing where to start.

4. Score and prioritize (value vs feasibility)

AI strategy is 50% picking the right problem, 50% not chasing the wrong one.

Not every shiny idea is worth the chase. Use a simple 2×2 to map:

High value, high feasibility → quick wins
High value, low feasibility → long-term bets
Low value → deprioritize or drop

Don’t treat validation as a final step – make it part of the prioritization process itself.

But keep in mind: feasibility isn’t only about internal capabilities like data quality, tech infrastructure, or talent. It again requires an outside-in perspective. Some spaces may appear attractive – large markets, mature tech – but may be difficult to win due to high entry barriers, capital intensity, or strong incumbents. Others may be constrained by regulation or shifting policy.

Assess feasibility by asking:

Do we have – or can we build – the internal capabilities needed to execute?
Are there external constraints (like regulation or market saturation) that make this hard to win?
Is this a space where competitors already have a significant lead or unfair advantage?

This broader view helps ensure you don’t pour time and energy into exciting ideas that aren’t winnable – no matter how technically feasible or high-potential they might seem.

Feasibility and prioritization should be pressure-tested with experts and stakeholders:

Functional leads can speak to business urgency and context
Data and AI teams can assess technical viability
External experts (or even clients/partners) can help uncover blind spots or industry dynamics

This cross-functional input ensures you’re not just chasing ideas that look good on paper, but investing in those that are actionable, relevant, and supported across the business. Refine. Align. Then move forward with the top 3–5 high-value opportunities.

Use this dual-lens canvas to guide the conversation.

5. Don’t stop at “Where to Play” - define “How to Win”

Phase 4 (feasibility analysis and prioritization) helps you decide which AI opportunities are worth pursuing, based on value and feasibility. But Phase 5 is about “How to Win” and it goes one level deeper: once you’ve chosen your plays, this step is about spelling out the enablers that will make success possible.

Ask:

What capabilities do we need to deliver this opportunity?
What gaps exist in our current tech, data, or talent landscape?
Are there gaps we can close through ecosystem collaboration, strategic acquisitions, or new ways of working?

Feasibility tells you if something’s worth doing. ‘How to win’ tells you how to make it real.

And here’s an important note: the more disruptive the opportunity, the more critical these enablers become.

But don’t be fooled: even incremental use cases can fail if the foundations aren’t in place. Whether you’re optimizing call centers or building AI-native products, success hinges on your ability to deliver:

Data that’s accessible, relevant, and well-governed
People who are trained, engaged, and empowered
Technology that is flexible and scalable
Governance that ensures alignment, ownership, and responsible use

Strategy isn’t just choosing where to play - it’s deciding what you’re willing to build or shift in order to win

If you want AI to be more than a buzzword, stop asking “Where can we use AI?” and start asking “Where can AI deliver disproportionate value?”.

And make sure you’re thinking both from today’s pain and tomorrow’s possibility – with a clear plan to make those opportunities real.

Let’s talk about how you can turn AI ambition into real business impact with a clear strategy.

Summary & key takeaways

5 step framework to help executives move beyond AI hype and focus on high-value, feasible opportunities

To unlock real value, leaders must ask not just “Where can we use AI?” but “Where can AI deliver disproportionate value—and how will we make it real?”

1. Start outside-in:

Analyze the AI market landscape, competitor moves, and adjacent innovation.
Build executive-level AI fluency to avoid falling for hype and make informed calls.

2. Explore AI opportunities through two lenses:

Business-led (Today → AI): Where can AI optimize current operations?
AI-led (AI → New Business): What new offerings or models become possible because of AI?

3. Run a cross-functional opportunity scan:

Identify signals of AI potentials on a functional level to surface concrete use cases and clusters of opportunity that are grounded in reality
Narrow down to 2–3 high-priority domains where AI can make a real difference

4. Score and prioritize (value vs feasibility):

Use a 2×2 matrix to separate quick wins from long-term bets.
Assess feasibility and prioritize AI opportunities through two lenses/

5. Define “How to Win”:

Identify required capabilities in data, tech, people, and governance.
Close gaps through hiring, partnerships, or ecosystem collaboration.

Laura Stevens

Managing Director of Data & AI

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The post Making AI work: A practical guide to finding and delivering real value appeared first on BOI (Board of Innovation).