7 minute read
In the noise around AI, our resident AI expert urges product professionals to pause and come back to what matters: meaningful, customer-centered product work.
Product leaders often find themselves in a curious predicament: overwhelmed by noise, underwhelmed by clarity. This article is not a how-to manual for bolting AI onto your backlog, but rather a reflection on the right kind of ambition – an invitation to pause and ask: What is truly worth building? For those exhausted by the ceaseless march of expectations and the tyranny of urgency, this piece offers something rarer than a framework – it offers a moment of thoughtfulness, and a method for aligning product work not with fashion, not as a result of mandate, but with need.
Context
The dominance of AI in today’s headlines is unmatched. Every week, new capabilities, breakthroughs, and use cases flood the media, creating an atmosphere where it feels like everyone is doing something with AI – and if you’re not, you’re already behind.
Product leaders are feeling this pressure acutely. Executives and board members are asking variations of the same question: “What are we doing with AI?” The subtext is clear: standing still is not an option. In response, some product teams have rushed to ship AI features – any features- just to have something to point to. But acting without a clear purpose carries real risks.
The Technology-First Trap
One of the most dangerous outcomes of this pressure is a shift in mindset: rather than starting with a customer problem and evaluating whether AI is a fit, product teams begin with the technology and go hunting for problems to attach it to.
This rarely ends well.
Building from the technology up encourages forced solutions. It’s easy to fall in love with what the AI can do rather than whether it should be doing it in the first place. The result is often a feature that may demonstrate technical novelty but feels disconnected from customer needs. It adds noise to the product, not clarity. These misaligned efforts can confuse users, erode trust, and damage a brand’s credibility. Customers may start to wonder: If this doesn’t help me, do they still understand my problems at all? Worse still, once credibility is lost, it’s hard to rebuild.
- Instead of strengthening your market position, shipping a tech-first AI feature can dilute it. And, potentially, even worse, your customers asking something no product manager wants to hear – “What’s the point of this ?”
A Return to First Principles
Rather than chasing the next AI use case, go back to the basics of product management: solve meaningful problems. Understand the challenges your product is built to address. Break those problems down. Then, only if the solution truly demands it, apply AI.
To evaluate whether a proposed AI feature is worth pursuing for your product, we teach a four-part framework in our upcoming AI in Product course. This framework helps assess potential features through four distinct value lenses: buyer, user, vision, and competitor.
1. Buyer Value
Does adding this feature to the product provide value to the people making the purchase decision?
Buyers want to know how your product contributes to strategic or financial outcomes. AI features should support those goals through:
Cost reduction: Helps buyers lower operational expenses, making the solution more attractive and easier to justify financially.
Revenue growth: Offers the potential to increase sales or open new revenue streams, which appeals to the buyer’s growth objectives.
Scalability: Enables buyers to handle growth without proportional cost increases, supporting long-term expansion plans.
Risk mitigation: Reduces compliance and operational risks, which reassures buyers concerned with security and governance.
Differentiation: Provides unique value that can help buyers stand out in their market or better serve their own customers.
Fast time-to-value: Delivers measurable benefits quickly, aligning with the buyer’s need for rapid results and justifiable investments.
Ask:
- What buyer goals does this feature help accomplish?
- Will this feature meaningfully impact the purchase decision?
- What metrics or KPIs will the buyer use to evaluate success?
- Does the feature help the buyer achieve their internal strategic initiatives or mandates?
2. User Value
Does the feature improve the end-user’s experience or output?
AI should make tasks easier, faster, or better for users. If it doesn’t, it risks becoming a barrier instead of a benefit. Look for:
Time savings: Helps users complete tasks more quickly, increasing productivity and freeing time for higher-value work.
Improved accuracy: Reduces errors and increases the reliability of outcomes, which boosts user confidence and performance.
Cost reduction for the user: Lowers the need for additional tools or resources, which can make the product more appealing and economical.
Ease of use: Makes adoption simpler and reduces friction, which encourages sustained use and satisfaction.
Personalization: Tailors the experience to individual needs, improving relevance and engagement
Accessibility: Expands usability to a broader audience, including those with disabilities, which enhances inclusivity and market reach.
Ask:
- How does this improve users’ daily workflows?
- Are the benefits measurable and meaningful?
- Does this reduce friction or common frustrations users face today?
- Can it increase user retention or satisfaction over time?
3. Vision Value
How does this feature align with your company’s long-term strategy?
Not every new product feature needs to move the needle today. Some support future positioning or innovation goals. Valid vision value includes:
Signaling innovation: Shows the market and internal stakeholders that the organization is forward-thinking and actively investing in advanced technologies.
Advancing strategic goals: Reinforces the company’s core mission or key priorities, aligning new features with long-term direction.
Preparing for market shifts: Equips the organization to adapt to changing regulations, customer expectations, or technological disruptions.
Supporting your tech ecosystem: Enhances compatibility and synergy with existing tools or platforms, increasing overall system value.
Contributing to thought leadership: Provides opportunities to lead industry discussions, publish insights, or showcase expertise.
Attracting high-value talent: Makes the company more appealing to top candidates who want to work on cutting-edge or meaningful projects.
Ask:
- Does this help advance our mission?
- Can it serve future positioning or internal priorities?
- Does this feature help articulate or reinforce the company’s long-term story to investors or stakeholders?
- Can it be leveraged in external messaging to highlight innovation and leadership in your industry?
4. Competitor Value
Does this feature help protect or grow your position in the market?
Including AI features in products can give you a competitive edge – but only if they’re tied to real value. That includes:
Offering something unique: Sets your product apart in the market, making it easier to capture attention and justify premium pricing.
Matching key competitor offerings: Helps prevent customer churn by keeping pace with the features offered by rivals.
Reaching market first: Establishes early leadership and can create momentum before competitors catch up.
Creating customer loyalty: Builds stronger relationships and long-term retention by delivering tailored, high-value experiences.
Reducing operational costs: Enables more efficient operations that can support aggressive pricing or improved margins.
Enabling faster product cycles: Allows you to respond quickly to market demands or feedback, improving agility and customer satisfaction.
Ask:
- Is this a differentiator?
- Does it address something our competitors haven’t?
- Will it be difficult or costly for competitors to replicate?
- Can it be highlighted in marketing or sales as a competitive advantage?
Be Strategic, Not Reactive
The pressure to “do something with AI” is real. But speed without purpose often leads to missteps. Don’t let fear of missing out drive your product roadmap.
Start with the problem. Validate that it’s real, urgent, and worth solving. Then assess whether AI is the best way to solve it – and whether it delivers value across the dimensions that matter to your customers and your business.
What distinguishes a smart AI product strategy isn’t how fast you move, but how clearly you think.
More AI Resources for Product Management Professionals:
- Leveraging AI to Improve Market Research
- How Product Managers Can Use AI in Their Daily Work
- 64 AI Prompts for Product Managers
