Seven Strategies to Compound Your AI Advantage

How companies like Moveworks, Intercom, Spotify, Stitch Fix, Morgan Stanley, Canva, and Ocado Group are strategically leveraging AI.

Enterprise leaders are awakening to a new fact: winning with AI isn't about having the biggest model or the most data. It's about strategy. Below are seven clear AI strategies, each a repeatable playbook, that organizations can adopt to focus their AI efforts and build a compounding advantage over time. Each strategy is paired with a real-world example (often a mid-sized innovator, not just Big Tech) demonstrating bold, system-level implementation.

1. Wedge-and-Expand Strategy – Dominate One Painful Task, Then Broaden

Instead of trying to "AI-enable" everything simultaneously, this strategy zeroes in on a single, critical workflow and nails it with AI. The company identifies a thin wedge – a frequent or frustrating task that even a modest AI improvement feels like a breakthrough. By solving this one use case deeply and reliably, the AI solution earns its place and gains traction on its own. Once the wedge takes hold, the company can expand, tackling adjacent tasks and processes. This focused beachhead approach compounds advantages: the AI improves with each interaction, and the initial trust paves the way for broader adoption.

Real-world example: Moveworks, a mid-sized AI startup, applied the wedge strategy to IT helpdesks. It began by automating the resolution of common IT support tickets – a narrow but high-impact pain point in large enterprises. Employees can chat in natural language to report issues, and the Moveworks bot instantly handles over 60% of requests end-to-end. By delivering value "from day one" in this single domain, Moveworks earned the right to expand its scope. Every interaction makes the system smarter at handling employee issues, creating a flywheel of improvement. Having conquered the basic IT ticket workflow, Moveworks is now expanding into HR requests and other support areas. It is using its foothold to grow into a full AI platform for employee service. The key started with a well-defined use case that could deliver undeniable value and become a defensible foothold for further AI integration.

2. Distribution Leverage Strategy – Embed AI Where Your Users Already Are

This strategy is for companies with an engaged user base or frequent touchpoints. Rather than inventing something new, they infuse AI into the core experiences and behaviors that users regularly do. By riding on existing distribution and habits, any AI feature gets instant traction – the opposite of "If you build it, will they come?" Crucially, the AI is used to amplify or streamline actions users take daily, not to create tangents or add. The result is immediate uptake and compounding usage: each improvement deepens user engagement on a well-traveled path. In short, leverage your existing relationships and workflows to deploy AI where it will actually get used.

Real-world example: Intercom – a customer communications SaaS – illustrates this play. Intercom had thousands of companies already using its chat widget to handle support queries. Instead of launching an unrelated AI app, Intercom introduced an AI support bot called "Fin" into that existing chat workflow. Fin, powered by GPT-4, was plugged into companies' knowledge bases to answer customer questions automatically. Because it lived inside Intercom's popular support interface, Fin saw immediate, widespread use across "thousands of live support flows" with zero new user acquisition needed. Similarly, the productivity platform Notion rolled out "Notion AI" within its existing docs and notes product, which was adopted overnight by its user community. These companies didn't need a better model or exclusive data; their advantage was being in the right place. By integrating AI into familiar tools (writing, customer Q&A), they amplified behaviors users were already doing weekly, driving higher engagement and value without forcing any new habit. The lesson: if you have distribution, use it – weave AI into the daily routines of your customers to compound your reach.

3. Middleware Mastery Strategy – Build the AI Glue That Makes Everything Work

Some companies win at AI not by flashy features but by engineering excellence behind the scenes. This strategy focuses on the middleware – the connective tissue that turns raw model output into reliable system behavior. Middleware includes prompt pipelines, memory stores, orchestration logic, retrieval caches, fallbacks, and all the other apparatus that make an AI feature consistent and production-grade. Mastering this layer means your AI is more reliable, coherent, and fast-evolving than competitors'. Strategically, companies strong in middleware can ship new AI capabilities faster and with greater reliability because their underlying systems handle the heavy lifting. They don't necessarily need proprietary models – they win by making any model (open or third-party) perform better, integrate deeper, and fail gracefully. Over time, this compounds into a formidable advantage: your AI feels integrated, not bolted on, and you can plug in improvements (like newer models or data sources) with minimal friction. In short, invest in the scaffolding, and you'll automatically benefit as models get better.

Real-world example: Spotify's AI DJ feature shows the power of orchestration and middleware. To create a personalized "DJ in your pocket," Spotify didn't rely on a single AI model – it combined several AI components into a seamless system. The DJ pulls from Spotify's personalization engine (to curate songs you'll like), uses OpenAI's generative AI to create commentary about the tracks, and then feeds that into a custom voice synthesis model (from its Sonantic acquisition) to speak to the listener in a friendly DJ voice. This orchestration – a carefully engineered pipeline of recommendation AI, language AI, and voice AI – makes the experience magic. The middleware layer coordinates what to say, when to talk vs. play music, how to transition, etc., akin to an "AI air traffic control." The result is a coherent user experience: the AI DJ feels uncannily personal and engaging because Spotify tuned the system behavior end-to-end. Importantly, any competitor could access similar models, but Spotify's system integration is complex to copy. The company notes, "most of what makes an AI product feel good – or terrible – is happening in this [middleware] layer." Spotify delivered a differentiated feature that keeps users listening longer by out-executing the glue that holds AI together. The takeaway: great AI products are 20% model and 80% how you use it – reliability, context, timing – so excel in that 80% to compound your lead.

4. Data Flywheel Strategy – Turn User Interactions into Ever-Smarter AI

A powerful strategic pattern is designing your AI so that every use makes it better. This is the classic flywheel effect: the system learns from user-generated data, improving the experience, attracting more use, and so on. The key is to focus on a domain where feedback loops and iterations can dramatically improve accuracy or personalization. Rather than hoarding the most data, it's about learning faster and more sharply from the data you get. Organizations employing this strategy instrument their AI features to capture rich feedback (explicit or implicit) and continuously refine their models or rules. Over time, they build a unique data asset – not just raw data, but validated patterns about what works best for each user or scenario. The compounding payoff is a product that seems to "know" the user extremely well, creating delightful "How did it know?" moments that keep customers returning for more. This strategy doesn't require billions of users; it requires smartly leveraging each interaction to train your AI advantage.

Real-world example: Stitch Fix, an online personal styling company, has embraced AI-driven personalization since day one. Its model uses dozens of data points about each customer's size, style preferences, and feedback to recommend clothing items. Crucially, Stitch Fix has a loop where customer feedback directly makes the algorithms smarter: every time a customer rates an item or returns it (with reasons like "too tight" or "not my style"), that data helps the system better predict preferences for that customer and others with similar profiles. One Stitch Fix executive explains that this feedback loop continuously "evolves our understanding of client preferences." Over the years, the company amassed an ever-growing knowledge base of fashion tastes, allowing it to deliver eerily accurate picks – at scale. The AI turns personal styling (once a bespoke, human-only luxury) into a mass-customized service that improves with every shipment. This data flywheel has become a moat: new competitors starting today lack the rich history of fit and style feedback that Stitch Fix's AI uses to delight customers. Enterprise leaders can replicate this pattern by asking: how can our AI gather and learn from user input to get better automatically? The goal is AI that doesn't stagnate – it gets smarter every day.

5. Human-AI "Co-Pilot" Strategy – Pair AI with Human Expertise for Leverage

Not every AI strategy is about full automation; many of the most effective ones combine AI and human strengths in a complementary loop. The co-pilot strategy centers on augmenting skilled employees or customers with AI assistance rather than replacing them. By putting a "junior AI" at the side of a human expert, you can collapse tedious tasks, surface insights, or draft outputs – which the human then guides or edits to final form. This pairing lets people handle more volume or complexity with the same effort, multiplying productivity while maintaining quality. Crucially, the interaction also generates feedback: humans correct the AI or choose among its suggestions, and those signals train the AI to improve (tying into the flywheel effect). Strategically, this approach builds compounding institutional knowledge. AI captures patterns from many human decisions, while humans can spend time on higher-level judgment or creative work. Companies deploying AI co-pilots often see dramatic efficiency gains and consistency improvements, because the best practices of top performers get distilled into the AI and shared across the team. Over time, the organization transforms – work gets redefined so that people focus on what only people can do, and AI handles the rest.

Real-world example: Morgan Stanley has rolled out an AI co-pilot for its financial advisors, showing how human-AI teaming can elevate even high-stakes professional work. In 2023, Morgan Stanley introduced a GPT-4 powered assistant (internally called "AI @ Morgan Stanley Assistant" and later "Debrief") to help its 16,000+ wealth managers answer client questions and manage information. The AI is trained exclusively on the firm's vast internal research and product data, so it can instantly retrieve relevant insights for an advisor during client interactions. When advisors meet with clients (with permission), the AI sits in: it automatically takes detailed meeting notes, highlights action items, drafts follow-up emails, and identifies the next steps. This saves the advisors from hours of paperwork and ensures no detail is missed. Morgan Stanley's managers report that "the quality and depth of the notes" generated by the AI are "significantly better than those by human analysts" – a perfect example of AI augmenting human work with superhuman thoroughness. By handling rote documentation and information lookup, the AI co-pilot "drives immense efficiency in an advisor's day-to-day," freeing them to spend more time on meaningful client advice. Importantly, the system learns from how advisors use it – which suggestions they accept or tweak – continually improving its usefulness. Many other organizations are adopting similar co-pilot models (e.g., legal AI assistants for lawyers and medical AI assistants for doctors). The bold idea is that every knowledge worker in your company could have an AI helper, dramatically amplifying their output. Embracing this strategy turns AI into a force multiplier for your talent, compounding the expertise and judgment of your team rather than substituting for it.

6. AI Ecosystem Play – Build a Platform and Community Around Your AI

Another way to compound AI impact is to open up your AI capabilities for others to build upon. In the ecosystem strategy, a company goes beyond using AI internally or in one product – it creates a platform or marketplace that lets third-party developers, partners, or even customers extend the AI in new ways. The company harnesses external innovation to add value to its core offerings by providing APIs, plugins, or integration hooks. The genius of this approach is leverage: for relatively low effort, you get many use cases and extensions created by others, making your platform more attractive, drawing in even more users (a network effect). Strategically, it compounds advantage by making your solution the center of a rich ecosystem – switching to a competitor becomes harder when a whole community and suite of add-ons grows around your platform. Moreover, you can gather data and learn from the myriad use cases others explore. The ecosystem play works especially well for mid-sized companies that can't build everything themselves but can position themselves as the hub where AI solutions meet users. It's a bet on breadth and community: empower others to amplify your AI's reach.

Real-world example: Canva, the online design platform, executed this masterfully. Canva had an "AI everywhere" vision for its design tools, but it didn't try to develop every AI feature in-house. Instead, Canva built an API and app marketplace for AI. It opened its platform so developers could create AI-powered plugins (apps) that integrate seamlessly into the Canvas interface. The result? An expansive catalog of AI apps within Canva – from an AI presentation generator to image generators and beyond – created by third parties but available to all of Canva's 225 million users. This ecosystem approach means Canva's users constantly get new AI features and niche capabilities that Canva itself might never have time to build. It also helped Canva rapidly expand AI coverage of the end-to-end design workflow by "leveraging powerful collaborations" (like using OpenAI and others) alongside its research. The strategy has paid off: Canva stays at the cutting edge of AI in creativity by acting as a platform for innovation. They even benefit from the network effects – as more developers build AI apps for Canva, more designers are drawn to use Canva for its rich AI toolbox, attracting more developers. It's a self-reinforcing cycle. Enterprise leaders can note that even if you're not a consumer platform like Canva, you can cultivate an AI ecosystem in your industry (for example, by offering APIs or partnering for an app marketplace) to multiply your innovation capacity and stickiness.

7. End-to-End AI Reinvention – Rebuild a Core Process for a 10x Leap

The boldest strategy is to completely reimagine a fundamental process using AI – not as a patch or enhancement, but by redesigning the workflow from the ground up with intelligent automation. This is a step beyond a single "wedge" task; it's about transforming an entire system or value chain with AI at its core. Companies that do this often integrate multiple AI technologies (and sometimes robotics or IoT) to achieve quantum leaps in efficiency, speed, or capability. The strategy requires vision and commitment: you identify a mission-critical operation (logistics, customer service, manufacturing, etc.) and invest in an AI-driven architecture to run it radically better than the traditional way. When it succeeds, the advantages are staggering – not 10% improvements, but 10x improvements that competitors running legacy processes can't match. Moreover, these gains tend to compound because an AI-driven system can continuously optimize itself, scale easily, and even enable new business models (like offering your AI-driven service to others). End-to-end AI reinvention turns a core competency into a defensive moat. It's the strategic choice to play a different game entirely, changing the terms of competition in your favor.

Real-world example: Ocado Group, a UK-based grocery company, transformed itself by reinventing online grocery fulfillment through AI. Traditional grocery picking and packing is labor-intensive and slow, but Ocado spent years building an AI-powered, robotic warehouse system called "The Hive." In Ocado's automated fulfillment centers, thousands of robots zoom around a grid orchestrated by a central AI "air traffic control" that coordinates their every move ten times per second. Vision algorithms and robotic arms handle the items, guided by AI to pack efficiently without damaging goods. The payoff has been enormous: Ocado's system can pick and pack a 50-item order in just five minutes – about 6× faster than a human-driven process. And it's not just speed; the AI optimizes the use of space, energy, and inventory with a level of complexity no manual operation could achieve. Ocado's end-to-end AI reinvention of grocery logistics was so successful that it didn't just give them an internal advantage – it became a product. The company now licenses its Ocado Smart Platform (the AI-driven warehouse tech and software) to other grocers worldwide, turning its AI superiority into a new revenue stream. This illustrates how a daring reimagining of a core process powered by AI can yield order-of-magnitude improvements and compound into strategic dominance. Enterprise leaders should ask: what is a critical process in our business that we could entirely reinvent with today's AI capabilities? The answer might redefine your industry.

Bold, Coherent, and Compounding

In the age of AI, strategy is the difference between frantic experimentation and lasting advantage. The seven strategies above share a common thread: focus and leverage. Whether focusing on a key use case, a strength (user base, data, or infrastructure), or a vision of a transformed process, the idea is to avoid scattershot AI projects and commit to a clear game you can win. Each playbook – from the thin wedge to the full-stack reinvention – works not by brute-force scale but by compounding intelligent choices over time. As Nate Jones, the author of "You Don't Need Better Models—You Need a Better Strategy" put it, if your AI efforts align to the point of real leverage, they will compound; if not, they'll stall as disjointed experiments. The opportunity is open to companies of all sizes and industries. You don't need to be a tech giant to leverage AI – you need strategic clarity on where and how you'll apply AI for maximum impact. For forward-thinking leaders, now is the time to pick your play, align your teams around it, and execute relentlessly. Focus beats fortune in this arena. Even a modest AI initiative can snowball into a transformational advantage for your business with the right strategy.

About the Author

Jason Hauer is CEO of HauerX Holdings and an Inc. 500 honoree. He partners with commercial AI tech and solutions companies to turn ambition into market leadership.