How SMEs Should Implement AI: The Strategic Mindset Behind a Winning Generative & Agentic AI Strategy
Hi, I’m Aby
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From Utility to Strategy: What SMEs Must Do Now to Win in the Age of Agentic AI
Introduction
This week marks the fourth and final edition of our series on Agentic AI for SMEs. If you missed the earlier parts—including How SMEs Can Shift From Generative AI to Agentic AI for Faster Growth and Scale and How Agentic AI Transforms SME Growth: From Manual Hustle to Autonomous Scale and Agentic AI for SMEs: How SMEs can Strengthen Cash Flow, Reduce Debt, and Build Resilient Growth —you can catch up here.
Now, three years into the mainstream adoption of AI, we close the series by asking a direct question: How should SMEs strategically use AI to build a strong, growing business with sustainable competitive advantage—one capable of generating 7‑8‑9‑10‑figure outcomes?
To explore this, we turn to insights from the HBR podcast episode You Need a Generative AI Strategy. The discussion captures where businesses truly are in the AI adoption curve—and what leaders must do next to stay ahead.
The message is unambiguous: businesses have moved beyond the cool demo phase and into the hard reality of competitive advantage. Generative AI is no longer a nice‑to‑have productivity booster. It represents a fundamental shift in how value is created, delivered, and captured.
The episode highlights three critical truths for SMEs:
- Standard chatbot tools won’t create competitive advantage. Everyone has access to the same interfaces and the same data.
- Productivity gains alone are not enough. Efficiency is helpful, but it does not differentiate a business in a crowded market.
- Understanding the shifting value chain is essential. Competitive advantage now lives in the last mile—the specific workflow, interface, or customer interaction where AI directly creates value. Businesses that own this last mile will outperform those offering generic AI experiences
These are the exact arenas where SMEs are currently competing. Understanding these strategic levers is no longer optional—it is the foundation for building a resilient, high‑growth business in an AI‑driven economy.
This week, our FLIGHT 78910™ SME Case Study showcases how a start up used AI as a strategic tool to build an AI‑powered product—and the process that enabled them to reach 500,000 users in just six months.
The SME AI Playbook: How to Build a Generative AI Strategy That Creates Real Competitive Advantage
This week’s blueprint offers SMEs a clear guide to the strategic considerations required when crafting an AI plan that drives real competitive advantage. Drawing from the themes in the HBR podcast, the following points translate those insights into a practical reality check for smaller businesses.
For an SME, the question is no longer whether to use AI, but how to design an AI strategy that creates value, differentiation, and long‑term growth. The podcast’s themes highlight the core areas SMEs must evaluate as they build their AI roadmap: understanding where value is shifting, identifying the workflows where AI can create defensible advantage, and moving beyond generic tools toward specialised, last‑mile applications that directly impact customers.
How to Apply This Blueprint to Your SME
1. Don’t Just Play Productivity Catch‑Up
Most SMEs think: Great, I can use AI to write my social media posts and save five hours a week.
The reality is very different. If you and all your competitors use the same generic AI tools to produce the same generic content, you gain zero competitive advantage. You simply increase the noise level in your industry while blending into it.
SME Takeaway:
Use the time saved from basic AI automation to focus on the work your competitors cannot automate—high‑touch customer relationships, nuanced problem‑solving, strategic thinking, and the parts of your business where human judgment creates differentiation.
2. Your Small Data Is a Big Deal
The podcast emphasises the power of proprietary data. For an SME, this doesn’t mean Big Data—it means Deep Data. Your competitive advantage comes from the specific, accumulated knowledge inside your business that no generic AI model has.
SME takeaway:
The application: Feeding your historical data into a secure, private AI environment—such as a Retrieval‑Augmented Generation (RAG) system—creates an AI tool that understands your business, not just the business. This is where real differentiation begins.
The moat: A generic LLM may understand plumbing in general, but a 20‑person plumbing firm holds 15 years of invoices, local building codes, customer histories, and hard‑earned trade notes. That is defensible, high‑value data.
The application: Feeding your historical data into a secure, private AI environment—such as a Retrieval‑Augmented Generation (RAG) system—creates an AI tool that understands your business, not just the business. This is where real differentiation begins.
3. The Last Mile Is Your Home Turf
SMEs often win because they stay closer to the customer. Large corporations struggle to tailor AI to the specific, sometimes quirky needs of individual users. This “last mile” of value creation—where AI meets the end customer—is where smaller businesses naturally excel.
SME takeaway:
Use AI to deliver hyper‑personalised experiences. A boutique agency, for example, can analyse a client’s unique brand voice, tone, and behavioural patterns more deeply than a large firm could ever afford to do manually. Owning that specific client relationship—and embedding AI into it—is your last‑mile advantage, and it’s where SMEs can create differentiation that big competitors cannot replicate.
4. The Agility Advantage
Large‑company culture slows AI adoption. Layers of approval, long review cycles, and risk‑averse governance make it difficult for big organisations to move quickly. SMEs, on the other hand, hold a structural advantage: agility.
SME takeaway:
You don’t need a 12‑month “AI Ethics Committee” review to test a new workflow. You can pilot an AI‑driven process in one department next Monday, gather feedback by Friday, and refine it the following week. This ability to fail fast and learn fast is the only reliable way to discover a winning AI strategy before the technology shifts again.
Flight 78910™ SME Spotlight: Phoebe Gates
WATCH Video Feature: How Phoebe Gates Built an AI Start up With 500K Users in 6 Months
Phoebe Gates and co‑founder Sophia Kianni created a fashion‑shopping app only after multiple searches, pivots, and iterations to find true product–market fit. Throughout the process, they treated generative AI not as a gimmick, but as a toolbox—a set of capabilities they could apply across research, prototyping, user‑testing, and product development. That strategic use of AI became the foundation for a $9M fashion‑tech startup, with their app Phia growing from a few hundred early users to 500,000 downloads in just six months.
They didn’t simply use AI. They re‑engineered their strategy around it, identifying a defensible position in the fashion value chain and building an AI‑powered product that owned that niche. Their success shows how SMEs can use AI not just for efficiency, but to create a moat—a unique, hard‑to‑replicate advantage that drives rapid adoption and scale.
A natural next step is to break down the specific strategic levers they used so SMEs can apply the same thinking in their own markets.
SME Takeaways
1. Did They Use AI? – The Toolbox Check
Phoebe and Sophia used AI extensively, but the strategic breakthrough wasn’t that they used AI—it was how they specialised it. Instead of relying on generic tools, they built assets and infrastructure that created a defensible moat.
Proprietary Index:
Rather than becoming another interface sitting on top of ChatGPT—a commodity trap the HBR episode warns against—they built a proprietary search index containing more than 300 million products across 40,000+ retailers. This shifted their product from AI‑powered to AI‑differentiated, giving them a data advantage no competitor could easily replicate.
In‑House Models:
By 2025/2026, Phia moved away from third‑party LLMs and began developing their own models. This reduced latency by 80% and created a technical barrier that generic competitors couldn’t match. Owning the model meant owning the performance, the speed, and the user experience—turning AI from a tool into a strategic asset.
A natural next step is to examine how SMEs can apply this same principle of specialisation without needing a $9M budget.
2. HBR Strategic Analysis of Phia
HBR’s principles map cleanly onto how Phia built its competitive advantage. Instead of relying on generic AI capabilities, Phoebe and Sophia aligned their product with three core strategic levers: data, last‑mile ownership, and value‑chain positioning.
The Data Moat
Phia built a deep, proprietary dataset that generic AI models simply don’t have. They collected anatomical fit data, trend‑tracking signals, user taste profiles, and resale‑value insights. This “deep data” became their defensible moat, enabling recommendations and search results that feel personalised and context‑aware in a way off‑the‑shelf AI cannot replicate.
The Last Mile
Phia embedded itself directly into the user’s shopping workflow through a browser extension and app. By living at the moment of purchase, they owned the last mile—the exact point where decisions are made and value is created. Instead of waiting for users to come to them, they positioned themselves where the user already is, giving them control over the most critical part of the customer journey.
The Value Chain Shift
Phia didn’t stop at being an AI‑powered search engine. They integrated the resale and second-hand market, positioning themselves as the AI alignment layer between fast fashion, sustainability, and consumer behaviour. This moved them up the value chain and allowed them to participate in multiple parts of the fashion ecosystem, not just search.
3. Future Strategy Ideas: How Phia Can Strengthen Its Competitive Advantage
HBR’s guidance on long‑term AI strategy points to several next‑step opportunities for Phia. These moves would shift the company from a fast‑growing AI app to a defensible, category‑defining fashion‑tech platform.
Move Toward Agentic Shopping
Phia currently functions as an assistant—you ask, it answers. The next evolution is an autonomous shopping agent that acts on the user’s behalf. This aligns with HBR’s view that competitive advantage comes from owning the last mile and embedding AI into real workflows. An agentic version of Phia could handle tasks such as: finding outfits based on a user’s 3D body scan, monitoring price drops, making conditional purchases, and managing resale listings to offset costs.
Develop a B2B Strategy by Reversing the Data Flow
HBR emphasises that strategy is about doing things differently. Phia’s aggregate data—search gaps, unmet demand, trend velocity, and resale patterns—could be transformed into insights for brands. By telling retailers exactly what customers are searching for but not finding, Phia could evolve from a consumer app into a critical intelligence layer within the global fashion supply chain.
Build Hyper‑Localised Style Intelligence
Standardised AI tools produce average results. To avoid this trap, Phia can use generative AI to create hyper‑local “style genomes” that understand the cultural nuances of different neighbourhoods and cities. An AI that recognises the aesthetic differences between East London and Lower Manhattan can deliver recommendations that feel human, contextual, and culturally aligned rather than mathematically generic.
Cultural Advantage: Why Phia Reached 500K Users in Six Months
Phoebe Gates’ rapid traction—500,000 users in just six months—was driven by a cultural operating system that most SMEs can replicate. She built in public, invited real‑time feedback, and created a continuous improvement loop through her “roast the app” nights. This mirrors the HBR insight that culture, not technology, determines who wins with AI. While large retailers are slowed by committees and approval cycles, her small team iterated through “vibe coding,” testing and refining features in days instead of months.
The candid truth is that she didn’t win because her AI was inherently smarter. She won because she used AI to solve a specific last‑mile problem: the chaos of having 50 tabs open while shopping. By turning a universal frustration into a seamless workflow, she transformed AI from a utility into a strategy—one that met users exactly where their pain point lived and removed friction at the moment of decision.
This section naturally leads into how SMEs can adopt the same cultural and strategic mindset to accelerate their own AI-driven growth.
Apply the Playbook →
Every Blueprint and Spotlight in this newsletter is a strategic lever.
Which one will you use to build a stronger, more competitive SME?
Strategic Takeaway:
For SMEs and business owners, strategy ultimately comes down to where you choose to compete, how you differentiate, and why your approach keeps you ahead of the market rather than trapped inside it. Competing in a commodity environment is the default outcome when everyone uses AI in the same way. Some businesses are already breaking out of that trap, and many more will enter the market with sharper, more specialised AI strategies.
HBR captures this clearly: Strategy is about making choices. If you’re doing what everyone else is doing with AI, you don’t have a strategy; you have a utility. The distinction is critical. Utilities create efficiency. Strategies create advantage.
For SMEs, this means shifting the core question. Instead of asking How can we use AI?—a question that leads to generic tools and generic outcomes—leaders should be asking questions that anchor AI to competitive advantage:
- Does this AI initiative strengthen our existing moat?
If it doesn’t deepen differentiation, it’s not strategy. - Are we relying on a commodity capability that anyone can buy?
If the answer is yes, the advantage disappears the moment a competitor signs up for the same tool. - How does this change our business model, not just our task list?
AI should reshape workflows, customer experience, and value creation—not simply automate chores. - These questions force SMEs to think beyond productivity and into positioning, defensibility, and long‑term value creation, which is where AI becomes a strategic lever rather than a short‑term convenience.
Conclusion
Closing Perspective: What This AI Shift Means for SME Owners and Entrepreneurs
AI—both generative and agentic—is now a permanent part of the business landscape. It is reshaping industries, lowering barriers to entry, and creating new pathways for SMEs to compete and scale. Like cloud computing, SaaS, integrated digital workflows, remote‑work technologies, and social platforms before it, AI is another transformative layer that boosts productivity, efficiency, and market access. Many of our weekly FLIGHT 78910™ case studies—including this week’s Phia example—show how SMEs are already using these shifts to build stronger, more profitable businesses.
Over the past four newsletters, this AI series has explored how SMEs can maximise AI strategically—not just tactically. The blueprints outlined the opportunities, competitive risks, and trade‑offs business owners and leaders must consider before embedding AI into their business strategy. The HBR episode You Need a Generative AI Strategy reinforces why this matters, offering a framework for thinking about AI as a strategic choice rather than a productivity tool. And at the Davos Conference in January global leaders emphasised where businesses collectively stand in the AI adoption curve—and the risks and opportunities ahead.
Adding to this, the Managing Director of Amazon Web Services EMEA summarised the AI journey in three stages:
- Efficiency — where most businesses are today, using AI to automate processes and improve productivity (chatbots, automated responses, workflow shortcuts).
- Operational Transformation — deploying AI end‑to‑end across functions, requiring redesigned workflows and integrated systems.
- Strategic Reinvention — using AI to create new business models, reshape industries, and unlock entirely new value pools.
These insights give SMEs a practical roadmap for making strategic choices that increase cash flow, revenue, profitability, and competitive advantage.
This week’s case study illustrates what execution looks like in practice. A small team used AI across the entire value chain to build Phia, a fashion‑tech startup that leveraged AI’s rapid adoption curve to enter the industry and scale to 500,000 users. This mirrors what we’ve seen in other FLIGHT 78910™ SME examples—companies like ZURU, Contrarian Thinking, and Stax—all of which capitalised on emerging trends that lowered barriers to entry in toys, media, and finance, enabling them to carve out strong, profitable 7‑8‑9‑10‑figure businesses.
SMEs are now positioned to capture similar AI‑driven opportunities by asking sharper, more strategic questions:
“It starts with being clear about the problem you’re trying to solve. Don’t get distracted by everything uncontrollable—focus on the big issues that matter for your business. Challenge yourself and your teams to keep asking ‘why.’ Stay curious, question assumptions, and rethink how things are done. It’s the same mindset founders have: getting scrappy, experimenting, and learning as you go.”
And from the Microsoft strategy perspective:
QUESTION: “What would the head of strategy for Microsoft tell the head of strategy for a smaller firm about entering the AI space?”
ANSWER: “Start using it and get familiar with it. Then ask: How can I improve productivity across functions? How can I apply AI to workflows? What new experiences or business models can I create? This is a process any company—large or small—can follow.”
By approaching AI with this mindset, SMEs can build strong, growing, profitable businesses with durable competitive advantages—businesses capable of generating 7‑8‑9‑10‑figure outcomes.
If you’re an SME owner who feels overwhelmed about how to position your business strategically in this new landscape and want to unlock strategic visibility, reach out to me contact@the2015bgroup.com
References
- You Need a Generative AI Strategy – HBR On Strategy | Podcast on Spotify
- You Need a Generative AI Strategy
- Tanuja Randery on Europe’s digital skills gap | McKinsey
- How Phoebe Gates Built an AI Start up With 500K Users in 6 Months
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Related Posts
- Agentic AI for SMEs: How SMEs can Strengthen Cash Flow, Reduce Debt, and Build Resilient Growth – The2015B Group
- How Agentic AI Transforms SME Growth: From Manual Hustle to Autonomous Scale
- How SMEs Can Shift From Generative AI to Agentic AI for Faster, Scalable Growth –
- Davos 2026: What the World Economic Forum Means for SME Growth, Competitiveness, and Strategy
Until next week—
Set bold strategy. Set big targets. Take massive action. Measure what matters.
About the Author
Aby Rufus
Business Investor Strategy Expert Entrepreneur with an MBA in Strategic Planning—offering billion-dollar strategic solutions for SMEs.
