Innovation Strategy in the Age of AI: What’s Working Now

Everyone says they’re interested in product innovation. Few have a strategy for actually doing it. Especially now, when AI is changing the rules faster than teams can keep up.

An innovation strategy isn’t about chasing trends or throwing ideas at the wall. It’s about knowing why you’re innovating, where to focus, and how to build something that lasts. 

In this piece, we’ll break down what an innovation strategy is, how to create one that works in today’s AI world, and share examples from product teams doing it right.

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What Is an Innovation Strategy?

An innovation strategy is a structured plan that defines how a company will use innovation to achieve its product goals. It outlines where to focus innovation efforts, why they matter, and how to turn ideas into real outcomes (not outputs!) that drive growth, team effectiveness, or market differentiation.

It’s not just about launching something “new.” It’s about aligning product innovation with the broader strategy of the business. Without that alignment, teams risk building features no one needs, chasing trends that don’t serve their users, or spending months solving the wrong problem.

Why an innovation strategy matters

Most teams want to be innovative. In practice, though, that often looks like random brainstorming, hackathons, or shipping experimental features without a clear purpose. A proper business innovation strategy brings clarity to the process. It helps teams:

• Focus on the right problems

• Allocate resources effectively

• Set measurable goals for innovation

• Reduce risk by aligning ideas with business needs

Take AI agents as an example. One team might see it as a way to automate repetitive tasks and reduce costs. Another might use it to create entirely new products or services. Both are innovating, but without a strategy, neither will know if they’re actually creating value.

An innovation strategy sets the direction. It ensures you're not just doing innovation, but doing it with intent.

Juan Manuel Agudo Carrizo

Head of Product. Formerly at Real Madrid & eBay.

“The first driver of innovation is psychological safety. If you don’t create an environment based on empathy, where people can raise their voices, build rapport, and feel safe making mistakes, then they’ll be afraid and won’t innovate. So the first step is creating a space where people can speak up, challenge each other, and feel not stressed but excited and engaged about what they’re doing.

The second one is empowering teams. If you don’t give teams the ability to choose their own KPIs — KPIs that actually influence your North Star metrics — then they’re going to stay reactive. They’ll become feature factories. You need to give them the bandwidth to choose meaningful KPIs, align on goals, and then commit to those metrics. Without that space, there’s no real innovation. Teams will just learn how to game the system to deliver on time. That’s not the environment you want. In the end, innovation is about empowerment and making teams outcome-driven.”

6 Stages of Product Innovation Strategy

An effective innovation strategy doesn’t happen in one big leap. In reality, it’s a series of well-defined steps. Each stage serves a specific purpose and demands different types of thinking, tools, and leadership. Here's how high-performing teams approach it.

1. Define the innovation intent

Every strong innovation strategy begins with intent, not ideas. That means choosing where to play based on your long-term product vision, product strategy, and market dynamics. You should not make decisions based on hype, peer pressure, or internal FOMO.

One of the most common mistakes product teams make is defaulting to “We need an AI strategy” instead of asking, “Where does innovation actually move the needle for us?” 

Jumping on emerging tech trends without a clear AI use case is how you end up with bloated roadmaps, fragmented platforms, or pilots that go nowhere. Intent should focus your efforts, not distract them.

A clear innovation intent answers questions like:

• Are we solving for revenue growth, cost efficiency, or long-term differentiation?

• Is our goal to deepen value for existing customers, or unlock an entirely new market?

• Do we need to build a new product, reshape our process, or rethink our product positioning?

Use AI tools to surface market patterns and strategic whitespace. For example:

• Apply LLMs to mine customer feedback at scale—look for unmet needs across your customer base that map to growth opportunities.

• Feed past roadmap outcomes into AI to identify what kinds of innovation (new features, integrations, workflows) historically produced ROI.

• Use generative AI to simulate “what-if” scenarios: What if we targeted this new persona? What if we restructured our product pricing model?

AI should be used to enhance clarity, not replace vision. It helps you ask sharper questions, faster. The why behind your innovation still needs to come from product leadership.

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2. Identify the strategic opportunity areas

Once you have a clear innovation intent, your next step is to identify the right battlegrounds. You need to understand the places where your company has both leverage and urgency. That means zooming in on opportunity areas where customer needs are shifting, competitors are underperforming, or new tech is enabling a 10x leap.

A bad practice here is chasing “high-potential” spaces that don’t actually align with your business. Example: building AI-powered personalization when you still don’t have clean customer data or a product analytics pipeline that can support it. That’s not innovation, it’s distraction dressed as ambition.

Strong product teams ground this stage in:

• Voice of customer analysis: Where are people hacking your product to get more out of it?

• Competitive blind spots: What are your competitors ignoring because it doesn’t look urgent (yet)?

• Internal constraints: What bottlenecks are slowing teams down that, if removed, would unlock disproportionate value?

This is where AI can be operationalized to accelerate opportunity mapping:

• Use clustering algorithms on product usage data to discover underserved workflows or segments.

• Apply LLMs to synthesize competitive intel (e.g. changelogs, product reviews, earnings calls) and highlight trends your competitors might be betting on (or missing).

• Generate concept maps from your backlog or support tickets using AI to spot patterns humans would take weeks to surface.

Your job isn’t to innovate everywhere. It’s to choose opportunity areas that are both strategically aligned and operationally winnable. AI just helps you find them faster.

3. Choose the type of innovation to pursue

Once you've identified where you want to innovate, the next step is choosing how. That means selecting the type of innovation strategy in business that fits both the opportunity and your company's appetite for risk, complexity, and return.

You can look at this as an alignment checkpoint. 

Too many teams pick the wrong lane: they pursue disruptive innovation (e.g. building an entirely new AI-powered product) but budget for incremental UX tweaks and give it a 2-month timeline. Others run internal process optimization initiatives as if they’re moonshots, with endless ideation cycles and zero urgency. The mismatch kills momentum fast.

The four classic types of innovation you can build your strategy around:

• Incremental – small improvements to existing products or workflows (low risk, low reward)

• Adjacent – extending existing capabilities to new markets or users (moderate risk, moderate reward)

• Disruptive – offering simpler, cheaper, or more accessible solutions that change the market (high risk, high potential)

• Radical – pioneering entirely new tech or business models (very high risk, often long horizon)

Choosing the type of innovation means making hard tradeoffs:

• What timeline are we solving for—quarters or years?

• What kind of investment can we actually support?

• Are we ready to shift our org structure if this succeeds?

How AI helps here? Well, AI can give you sharper input, but not answers. Here’s how to make it work for you:

• Use AI to simulate investment scenarios: What would a disruptive move require in terms of team size, infrastructure, and cost over time?

• Run historical analysis on your feature launches and categorize them by type. Use LLMs to summarize which kinds of innovation have led to user retention gains, NRR lift, or market share increases.

• Use generative AI to prototype radically different product experiences in minutes (not weeks) to assess whether the leap is worth it.

The point isn’t to pick a “cool” innovation type. The goal is to pick the one your team can actually execute, support, and scale. Strategy without commitment is just theater.

4. Set measurable innovation goals

Innovation dies in vagueness. The moment your team starts talking about “fostering a culture of innovation” or “reimagining what’s possible” without attaching real numbers to it, you’ve already lost clarity. You’re on the path to wasted sprints, unclear success criteria, and executive misalignment.

Every innovation effort should have specific, measurable, and time-bound product goals tied to your type of innovation and business objectives. You’re running a strategic bet here.

Examples of clear innovation goals:

• Launch a new feature for Segment X that increases adoption by 15% within 3 months

• Reduce manual effort in Workflow Y by 40% using generative AI

• Test and validate a new monetization strategy with at least 2 enterprise clients in Q2

• Reach 25% usage of a newly introduced AI-powered assistant within 90 days of rollout

Poor practice here is either setting goals that are too abstract (“be more innovative”) or too disconnected from real product or user outcomes (“run 3 design sprints”).

AI here can’t tell you what to aim for. Still, it can help you validate whether your goals are feasible, aligned, or based on flawed assumptions:

• Use AI to model baseline performance trends from historical product data to set realistic targets

• Analyze similar launches internally or across the market (via public data, changelogs, or reviews) to benchmark what’s reasonable to expect

• Use LLMs to reverse-engineer goals from customer pain points—what outcomes would actually move the needle for them?

AI is also useful in tracking innovation KPIs in real-time. You can automate dashboards that pull performance against innovation-specific metrics like product adoption metrics, product success metrics, product-led growth metrics, or engagement in specific workflows.

Innovation goals don’t need to be perfect. Still, they need to be clear enough to drive focused effort and honest reflection. Without them, you’re not iterating, you’re just guessing.

5. Build, test, and iterate

This is where strategy turns into motion. Too often, teams launch a feature or pilot a bold idea, declare it “innovative.” They move on without validating whether it solved a real problem, created adoption, or had any meaningful impact.

Strong innovation teams treat this stage like a continuous experiment. They test hypotheses with different types of prototypes, not just functionality. They define what success looks like before building. And they create multiple paths forward.

Poor practice here is overbuilding too early, especially with AI projects. Just because a prototype works in a demo doesn’t mean it delivers value at scale. Many teams burn months developing AI-powered assistants, automations, or recommendations without confirming whether users will trust or even use them.

This is where AI shines—if used with discipline.

• Use generative AI to spin up realistic mockups, user flows, or onboarding sequences in hours. You can validate UX before any code is written.

• Run synthetic user interviews by feeding prompts into AI based on real personas. You won’t get human nuance, but it’s a fast way to stress-test assumptions.

• Use AI to prioritize your backlog by scoring ideas against business goals, historical impact, and resource constraints (yes, this works with structured inputs).

• For internal tooling or operational innovation, test AI automations in sandbox environments before integrating into live systems. Track time saved, error rates, and satisfaction from internal teams.

Above all, remember: iteration doesn’t mean endless cycles. It means fast, structured loops where feedback leads to decisions, not delays.

6. Scale or sunset

This is the moment of truth: do you double down or walk away?

Scaling an innovation means investing real resources — marketing, infrastructure, support, maybe even team restructuring. Sunsetting means pulling the plug, absorbing the loss, and moving on. Both require clarity, discipline, and leadership.

Poor practice here is letting “innovation debt” pile up. Companies tend to keep half-baked pilots alive because no one wants to admit they didn’t work. Or worse, they scale something with weak signals because someone important is emotionally attached to it.

Smart teams treat this stage like product portfolio management. They ask: Did this innovation hit its goals? Is there momentum? Do users care? Can we support this at scale without starving other critical priorities?

How AI helps here:
AI helps you make this decision with sharper evidence:

• Use AI to aggregate and summarize user feedback, support logs, and feature engagement from your test groups. Look for signs of organic pull.

• Analyze performance across cohorts using AI-powered dashboards: Which segments retained the feature? Which ones ignored it?

• Model long-term ROI under different scaling scenarios. If you invested in marketing or added onboarding support, how much lift would be needed to justify it?

AI can surface the story, but leadership needs to read it honestly.

Pro tip: Create an internal "product sunset playbook" just like you have for launches. Built in retrospective analysis, documentation, and team debriefs. 

Types of Innovation Strategies

Not all innovation requires moonshots. These are the core strategy types companies use to innovate with purpose, whether they’re optimizing an existing product or building something entirely new.

1. Incremental innovation strategy

This strategy focuses on small, continuous improvements to existing products, services, or internal processes. Here, it’s about making the wheel roll smoother, faster, or smarter.

Product teams use incremental innovation when their core product is stable and well-adopted, but customer expectations keep rising. That might mean improving loading speed, streamlining the product-led onboarding flow, simplifying product pricing, or automating backend tasks to cut support tickets.

When to use it: You’re in a competitive market and want to stay ahead without introducing massive risk or change.

Example: Spotify is gradually improving its playlist recommendations, interface fluidity, and device handoff, no headline-grabbing overhaul, just persistent polish.

2. Disruptive innovation strategy

Disruptive innovation is about entering the market from below, with a simpler, cheaper, or more accessible solution that existing players overlook. It typically starts by serving a segment incumbents ignore, then scales up and displaces them.

This strategy often looks unimpressive at first with limited features and narrow use cases. But it wins by meeting people where they are and solving something very important better than the rest.

When to use it: You’ve identified underserved users or seen signs of market fatigue with bloated or expensive incumbents.

Example: Zoom gained traction by offering fast, simple video conferencing, while legacy platforms like WebEx were focused on the enterprise and complexity.

Warning sign: Don’t call something “disruptive” unless you’re genuinely challenging a dominant model or simplifying the market. Disruption isn’t just new. It’s cheaper, faster, or more accessible to an overlooked audience segment.

3. Adjacent innovation strategy

Adjacent innovation is about applying your current capabilities to a new market or problem. You’re adapting or reframing something that already works to meet a new need.

This could mean targeting a different vertical, bundling an internal tool as a public product, or spinning off a popular AI use case into a standalone feature.

When to use it: You have an asset or advantage that solves problems outside your current market, and you can repurpose it without diluting focus.

Example: Slack evolved from a game studio’s internal comms tool into a global collaboration platform. They didn’t build Slack as a product; they recognized its adjacent value.

How to approach it: Use AI tools to analyze adjacent use cases across your power users or customer support logs. You’ll often find repeatable value hiding in what people are already doing unofficially.

4. Radical (or breakthrough) innovation strategy

Radical innovation is the high-risk, high-reward end of the spectrum. It involves developing entirely new technologies, business models, or experiences that don’t currently exist in the market. It often takes years to mature and requires cross-functional org-wide alignment.

With this one, you’re replacing a standard way of doing things or leapfrogging it. These bets are expensive, but they’re also where category creation and long-term moats are born.

When to use it: You have strong R&D capabilities, a long time horizon, and product leadership willing to take big bets that may fail.

Example: Tesla’s vertical integration of EV manufacturing, charging networks, and battery tech. They redefined the infrastructure behind the vehicles altogether.

What it requires: Budget, culture, patience, and a clear link between R&D and eventual commercial viability. Radical innovation without a commercialization strategy is just expensive experimentation.

5. Open innovation strategy

Open innovation flips the assumption that product innovation must be built in-house. Instead, it involves partnering with startups, universities, open-source contributors, or even your customer base to source ideas, IP, or working solutions.

This strategy works well when speed matters or when your team doesn’t have the deep expertise needed to explore a fast-moving space (e.g. AI research, edge computing, or biotech).

When to use it: You want to accelerate innovation, reduce risk, and tap into external talent or infrastructure without building everything from scratch.

Example: Google acquiring DeepMind instead of trying to replicate its research internally. Or Microsoft partnering with GitHub’s developer community to drive plugin ecosystems.

Pro tip: Use AI to monitor external IP filings, public datasets, or open-source trends to identify areas where partnership beats internal buildout.

6. Sustaining innovation strategy

Sustaining innovation helps you meet the rising expectations of existing customers. It’s about going deeper, not broader, by enhancing performance, usability, reliability, or support for your core product.

This strategy aims to strengthen your product-market fit and product positioning. For many B2B companies, this is the primary form of innovation year over year.

When to use it: You have strong product-market fit, and your goal is to retain and expand by deepening value, not diversifying.

Example: Apple’s yearly iPhone updates (better camera, faster chip, improved battery) designed to keep existing users happy and engaged.

Watch out for: Confusing this with innovation theater. Sustaining innovation still requires clear product goals, timelines, and user impact, not just “more features.”

7. Business model innovation strategy

This strategy doesn’t touch the product. It touches how you deliver and monetize it. Business model innovation can include new pricing structures, packaging formats, delivery channels, or monetization strategies that unlock new customer segments or margin improvements.

Often overlooked, this is one of the most powerful levers for mature companies looking for growth without massive product changes.

When to use it: Your product works well, but growth is stalling or you’re entering new segments with different willingness to pay.

Example: Adobe’s shift from boxed software to Creative Cloud subscriptions transformed a flat business into a SaaS giant.

AI angle: Use predictive models to simulate how pricing experiments or freemium conversions affect LTV, CAC, or user retention before rolling them out

8. Platform innovation strategy

Platform innovation turns your product into a foundation that others can build on. It’s about creating extensibility in the form of APIs, SDKs, plugins, and marketplaces. It’s also about scaling through network effects rather than internal feature velocity.

Done right, it lets your ecosystem do the innovating while you capture the value. But to do so, you need quality! As Glen Coates, the VP of Product at Shopify, says on The Product Podcast:

“The key to a successful product change in a platform like Shopify is making sure it’s not just big but also good. Quality is just as important as innovation.”

When to use it: You’ve reached product maturity and have developer or partner demand for extensibility.

Example: Shopify’s app store and partner ecosystem have turned it from a storefront tool into a commerce platform.

Success metric: Developer engagement, product adoption, and ecosystem revenue.

Examples of Real Innovation Strategies

These examples span different types of innovation strategies (incremental, adjacent, disruptive, radical) and show how real product teams connect innovation to actual business outcomes.

1. Slack’s adjacent innovation: Reframing internal tools into a product

Slack didn’t start out as a product. It started as an internal communication tool for a failed gaming company. The strategy was to repurpose something that already worked well internally and offer it to a broader market that was fed up with bloated enterprise chat software.

What made it a good strategy:

• It aligned with the company’s existing strengths (UX, real-time communication)

• It didn’t require new technology, just repositioning and polishing

• It solved a real, widely shared pain point in a fresh way

Adjacent innovation doesn’t always require invention. It sometimes means reframing what you already do better than most, and finding a new product-market fit for it.

2. Figma’s disruptive innovation: Design collaboration in the browser

Before Figma, most design tools were desktop-based and terrible at real-time collaboration. Figma’s innovation strategy was simple but bold: bring collaborative design into the browser, and give designers a multiplayer workspace, like Google Docs.

Why it worked:

• It was anchored in a deep technical insight (WebGL could handle the rendering load)

• It solved a cross-functional workflow gap (design–PM–engineering handoff)

• It didn’t try to compete on feature depth at first—just access and collaboration

How it evolved?

Figma didn’t stop with the core product. Their product roadmap extended into plugins, community files, and eventually dev mode, continuously scaling innovation around their core job-to-be-done.

A disruptive strategy works when it starts with a wedge that’s technically feasible, clearly painful for users, and expandable over time.

3. Duolingo’s incremental + AI strategy: Using AI to personalize content

Duolingo made learning fun, gamified, and addictive. But in the last few years, their innovation strategy evolved into layering AI into micro-moments: adaptive difficulty, smart lesson planning, and personalized review sessions.

How they used AI intelligently:

• They trained models on millions of anonymized learning sessions to optimize content sequencing

• They launched “Explain My Answer,” a GPT-powered feature that gives learners feedback in plain language

• They maintained engagement by adapting difficulty to the user’s real behavior—not just static paths

What they didn’t do:
They didn’t replatform or overhaul the product experience for the sake of “AI transformation.” They quietly embedded AI into parts of the experience that directly improved outcomes.

You don’t need to launch an AI-powered product. You can drive real innovation by using AI to enhance what’s already working.

4. Netflix’s radical innovation: From DVD rentals to global content production

Netflix is the poster child for radical innovation strategy. Not once, but twice.

• First, they shifted from mail-order DVDs to streaming

• Then, from content distributor to content creator

Each move cannibalized their own business model, but they did it before someone else could.

Why it worked:

• Their innovation strategy was tightly tied to user behavior and long-term infrastructure bets (e.g. internet bandwidth)

• They treated radical innovation as a survival move—not an experiment

• They committed to it with full operational and organizational transformation

Radical innovation requires full-stack commitment. You can’t half-pivot a business model.

How to Set Innovation Goals That Actually Work

Innovation goals should clarify your direction, align your team, and define what success looks like in a world that’s still unfolding. Here's how to set goals that actually guide innovation:

• Anchor goals in business outcomes:
  Always start with why—tie your innovation effort to a real business goal like entering a new market, improving NPS, increasing retention, or lowering delivery costs. The more specific the link, the better the execution.

• Avoid activity-based goals:
  “Run 10 experiments” or “launch an AI feature” is not a goal, it’s a to-do list. Focus on impact, not activity. Example: “Reduce onboarding time by 40% through automation by Q3.”

• Make goals measurable, but tolerant of uncertainty:
  Use directional product OKRs and ranges instead of rigid success metrics. Innovation isn’t always linear. Setting thresholds like “increase signups by 15–25%” gives room to learn without losing accountability.

• Use time-boxed horizons:
  Set near-term learning goals (3–6 months), medium-term scaling goals (6–18 months), and long-term strategic bets (2–5 years). Different types of innovation need different timelines.

• Validate with real data before scaling:
  Goals should evolve. Run limited tests or pilots, gather evidence, and refine your targets. AI tools like predictive modeling and sentiment analysis can help forecast traction early.

• Make goals cross-functional:
  Innovation rarely lives in one department. Involve product, engineering, design, and marketing from the start and give them a shared North Star that cuts across silos.

• Align goals with your innovation strategy type:
  Incremental goals are different from disruptive or platform-driven ones. Know whether you're optimizing, expanding, or redefining and set goals that reflect that intent.

The Edge in Innovation Strategy Is How You Decide

Every team has ideas. What sets apart the companies that actually innovate is their decision-making under uncertainty. There’s simply no point at which it becomes clear, certain, or very low-risk.

A good innovation strategy helps you answer hard questions when there’s no obvious answer:

• Should we go with the proven feature or the riskier leap?

• Do we double down on what’s working or rethink it entirely?

• Is this AI tool an advantage—or just noise?

In a world of infinite experiments and endless hype, your edge isn’t the tech. It’s the clarity behind how and why you choose to build what you build.

Sure, ride the wave of new, emerging tech. But more than anything, be deliberate and strategic. 

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