How to be a future-proofed product manager

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TLDR

In my recent conversation with Steve Johnson, former lead instructor at Pragmatic Marketing and product management coach with 30 years of experience, we explored how AI is reshaping product management. Steve emphasized that while AI will automate many tactical aspects of product management, the strategic role becomes even more critical. Product managers must focus on understanding customer problems rather than jumping to solutions, avoid disconnection from customers, and break down organizational silos. As AI transforms our work, product managers who embrace their strategic role will thrive, while those who don’t risk becoming obsolete.

Key Topics

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• AI’s impact on product management roles and responsibilities
  
  
• Using AI as a companion rather than a replacement
  
  
• The importance of a problem-centered approach
  
  
• How organizational structures often hinder effective product innovation
  
  
• The evolving role of Product Ops and its proper implementation
  
  
• Breaking down organizational barriers to information flow
  

Introduction

Today’s episode is a free-form discussion. In this series, I’m reaching out to past colleagues and experts whom I’ve talked with before to catch up and discuss the state of innovation and product management. My guest doesn’t know what I’m going to ask him, and I don’t know what he’s going to ask me.

With me is Steve Johnson. I have known Steve Johnson for a few years. I knew him by reputation when he was the lead instructor at Pragmatic Marketing, where he led instructional efforts and created training for 15 years you. We met at a conference a few years ago and have kept up since then. Today, Steve coaches product teams with a focus on the strategic role of product management, which will come into our discussion today.

AI’s Impact on Product Management

Steve began our discussion by mentioning his concern about the impact of AI on product management. He told a personal story that illustrates AI’s current capabilities. After experiencing heart-related concerns, he wanted to analyze his medical history to identify any patterns. Faced with ten years of text-based medical records, he uploaded the PDFs to ChatGPT and asked it to create a chart tracking his blood pressure, pulse, and weight over time. Within seconds, he had the visualization he needed. However, later someone asked him if he was concerned about training the model with his personal health data. Like many people, Steve had never considered this concern before.

Many professionals are uploading sensitive information to AI systems without fully understanding the implications. Sales teams upload call sheets, product managers share product ideas, and countless others input proprietary data into these systems without considering who might access this information.

For product managers specifically, Steve described AI as a “watershed” moment comparable to the introduction of the internet and World Wide Web—a truly business and life-changing technology. His perspective balanced optimism with caution:

Opportunities

Challenges

Elimination of tedious product management tasks

Long-term employment concerns as AI replaces roles

Ability to build more products, faster

Economic concerns if AI displaces too many jobs

Greater need for strategic product decision-making

Privacy and security risks with sensitive data

Steve articulated a perspective that should reshape how we think about our roles: As AI makes it easier to build more products, it becomes increasingly critical to ensure we’re building the right products. As AI automates the tactical aspects of product management, the strategic role becomes more important than ever.

This theme—that product managers must embrace their strategic function or risk being replaced by AI—emerged as a central concern throughout our conversation. In an era where AI can quickly generate requirements documents, mockups, and even code, the unique human ability to understand customer problems and make strategic decisions becomes the key differentiator for product managers who want to remain relevant.

The first phase of AI integration into product management is already happening, with tools like ProdPad incorporating AI capabilities directly into their platforms. These integrations streamline workflows by allowing product managers to leverage AI without switching contexts. For example, a product manager might use an AI assistant within their product management tool to analyze customer research data and identify potential gaps in understanding.

However, the fundamental question remains: As AI transforms how we work, what aspects of product management will remain uniquely human, and how can product professionals position themselves to thrive in this new landscape?

AI as a Product Manager’s Companion

We discussed the possibility of using AI as a workplace companion rather than a replacement. The integration of AI into product management tools isn’t just happening at the platform level—many of us are already incorporating AI assistants into our daily workflows on a personal level.

I shared my own experience using AI tools as a brainstorming partner. This personal adoption reflects a broader trend where professionals are integrating AI systems into their teams as thinking partners rather than simply as tools.

Steve observed that currently this integration is largely happening through individual initiative. He described using AI to help draft blog posts, starting with bullet points of his ideas and having the AI create an initial draft that he could then refine and personalize.

We both found that this collaborative approach with AI offered several benefits:

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• Overcoming “blank page” syndrome – Getting past the initial block of starting a writing project
  
  
• Idea generation acceleration – Triggering connections and thoughts that might not emerge in isolation
  
  
• Efficiency improvements – Converting rough ideas into structured drafts more quickly
  
  
• More enjoyable workflows – Making traditionally frustrating tasks more engaging
  

The relationship between a product manager and AI is similar to working with an intern—the output requires verification and refinement. Current AI systems can hallucinate or generate plausible-sounding but incorrect information. For product managers without deep domain expertise or customer understanding, evaluating the accuracy of AI-generated content becomes nearly impossible—a significant risk in strategic decision-making.

There’s also a growing concern about the proliferation of generic, AI-generated content lacking distinctive viewpoints. Steve cautioned that the quality of AI output depends heavily on how well you can construct a prompt and your ability to edit and enhance what the AI produces. In essence, AI amplifies your existing capabilities rather than replacing the need for expertise.

The implications are clear: Product managers need to view AI as a collaborative partner that extends their capabilities rather than as a replacement for their strategic thinking and domain knowledge. The human component remains essential for providing context, direction, and judgment.

This raises important questions about skill development for product managers in an AI-enhanced world:

AI Augmented Skills

Critical Human Skills

Content creation and documentation

Customer empathy and problem understanding

Data analysis and pattern recognition

Strategic decision-making and prioritization

Requirements documentation

Cross-functional collaboration

Market research synthesis

Business acumen and market judgment

As we move forward, product managers who can effectively collaborate with AI—leveraging its capabilities while maintaining their unique human strengths—will be positioned to excel in an increasingly competitive landscape. The key is understanding where AI can accelerate your work versus where human judgment and expertise remain indispensable.

The Dangers of Disconnection from Customers

One of the most significant risks in an AI-enhanced product management environment is the potential disconnection from customers. Steve expressed serious concerns about product managers who might rely too heavily on AI-generated insights without the foundational customer knowledge needed to evaluate their accuracy.

This risk isn’t entirely new. Steve observed that many marketing and development professionals have long preferred to outsource customer interactions.

The introduction of AI tools that claim to replicate customer research outcomes exacerbates this problem. I mentioned a concerning trend—new AI-powered customer research tools promising to deliver most of the insights you’d get from actual customer conversations. One such tool claimed to help you understand your customers’ unmet needs nearly as well as talking to an actual customer. While this might sound impressive, talking to an AI rather than a real customer could cause you to miss crucial competitive insights.

Organizations have been creating structural barriers between product managers and customers well before AI. Steve and I discussed how the adoption of Agile methodologies, particularly Scrum, created confusion between product management and product owner roles. I shared an example of an organization where product managers were converted to product owners and explicitly told not to talk to customers—instead, sales would represent the customer perspective.

This misalignment creates several problems:

1. 
   
2. Different listening objectives – Salespeople listen for closing opportunities, not unmet needs or future possibilities
   
   
3. Limited perspective – Sales teams interact with prospects, not necessarily existing customers
   
   
4. Support teams focus on resolution – Support staff aim to close tickets quickly, not explore deeper user challenges
   
   
5. Marketing’s reliance on secondary research – Preference for analyst reports over direct customer observation
   

Steve illustrated this distinction through a story about volunteering at an organization. While setting up computers, he noticed other volunteers yelling at their screens. Upon investigation, he discovered they were dealing with database corruption that shifted data fields. He quickly created a simple macro to fix the problem, leaving the volunteers ecstatic.

When he asked the office manager why she hadn’t mentioned this significant problem, she responded that “computers suck” and suffering through issues was just expected. It had never occurred to her that the problem could be fixed programmatically.

This story captures why product managers must talk directly to customers. The office manager (like many customers) had accepted a painful workaround as “just the way it is” rather than articulating the actual problem that needed solving.

Steve said, “When customers tell you what they want, they’re almost always wrong. When they tell you what problem they have, they’re almost always right.”

The risk of AI-driven disconnection from customers becomes even more concerning when we consider AI hallucinations—instances where AI systems confidently present incorrect information. Without direct customer knowledge, product managers can’t effectively evaluate whether AI-generated personas, user stories, or market insights reflect reality.

Signs of Customer Disconnection

Countermeasures

Relying exclusively on sales for customer input

Establish direct observation programs with customers

Using only AI-generated personas or user stories

Verify AI insights through customer validation

Feature-driven rather than problem-driven roadmaps

Document customer problems before proposing solutions

Inability to critique AI-generated content

Maintain deep domain expertise and market knowledge

The core message is clear: While AI can augment customer research and help process insights, it cannot replace direct customer interaction. Product managers who maintain direct customer connections will have both better inputs for AI tools and the knowledge to evaluate AI outputs critically. This human touch remains a crucial competitive advantage that AI alone cannot replicate.

The Problem-Solution Mindset Shift

A foundational challenge in product management is shifting from solution-focused to problem-focused thinking. Throughout our conversation, Steve repeatedly emphasized this distinction as perhaps the single most important mindset change for effective product management.

As product managers, we should think: “Don’t bring me solutions; bring me problems.” Steve explained that this approach contradicts common corporate wisdom where managers often say, “Don’t bring me problems; bring me solutions.” For product managers, this conventional advice is exactly backward.

The innovation process I teach in my MBA program at the University of Frederick aligns with this perspective. Based on research by Jeff Dryer and Nathan Defer in The Innovator’s Method, the process starts with an insight or observation, followed by deep understanding of the problem, and only then moves to solution exploration. Half of this proven innovation model focuses entirely on problem understanding before solution development begins.

Yet despite this evidence-based approach, both Steve and I have observed that humans naturally gravitate toward solution language, often without even recognizing it. I see this regularly with my students who immediately jump to describing solutions (“We need an app that does X, Y, Z”) rather than articulating the customer problem they’ve identified.

This tendency creates several challenges for product management:

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• Premature solution commitment – Falling in love with a specific solution before fully understanding the problem
  
  
• Technical tunnel vision – The “curse of knowledge” where technical experts immediately jump to implementation details
  
  
• Limited solution exploration – Narrowing the potential solution space by locking onto the first idea
  
  
• Misalignment with actual customer needs – Building features that don’t address the core customer problem
  

Steve shared a story about conducting win-loss analysis for a company where the sales team was losing deals due to a technical capability that the product had but the salespeople weren’t aware of. His initial instinct was to create training material to educate the sales team about the database methodology.

However, a marketing colleague offered a different approach: record the CTO explaining the technical capabilities on video, which salespeople could then play during customer meetings when relevant questions arose. This solution—which Steve admitted he never would have considered—proved highly successful.

The story illustrates how our solution biases can limit innovation. Had Steve insisted on his training solution rather than focusing on the core problem (salespeople unable to explain technical capabilities), they might have missed this more effective approach.

Steve has found that a problem-focused approach also works well with AI tools. Our natural tendency to frame requests in solution-oriented terms (“write code that does X”) limits AI’s potential to suggest novel approaches. Steve noted that his interaction with AI has evolved: Instead of saying, “Could you write me some code that does this thing?” he now says, “Here’s what I’m trying to do. How do you propose I do it?”

This problem-focused approach yields better results from both human collaborators and AI systems. It preserves the creative exploration phase rather than prematurely narrowing options.

Solution-Focused Approach

Problem-Focused Approach

“We need a mobile app that tracks nutrition”

“People following keto diets struggle to track their macronutrients”

“Write training materials on our database structure”

“Our sales team can’t effectively explain our technical capabilities”

“Show me how to use the LEFT function in Excel”

“I need to extract first names from a column of full names”

As AI increasingly automates solution implementation, the human skill of problem definition becomes more valuable. Steve says that product managers are still responsible for “the what, the who, and the why, not the how.” Using AI for product management isn’t as simple as saying “build me an accounting product.” Effective problem articulation requires specifying for whom you’re solving a problem, why you’re building a product, what it should do, what it shouldn’t do, and how it will be competitively differentiated. These strategic elements remain firmly in the human domain.

Organizational Structures That Hinder Innovation

Beyond individual mindsets and skills, our conversation explored how organizational structures often create barriers to effective product management and innovation. These structural challenges can severely limit a product manager’s ability to understand customer problems and develop appropriate solutions.

I raised the question of organizational constructs that help or hinder product innovation, noting that traditional functional silos frequently impede effective collaboration. Organizations typically structure themselves around functions—marketing, engineering, sales—creating artificial barriers between groups that should be collaborating closely.

These organizational dynamics create several significant challenges:

• 
  
• Information silos – Critical customer insights remain trapped within specific departments
  
  
• Conflicting objectives – Different teams optimize for different (sometimes competing) metrics
  
  
• Communication barriers – Specialized vocabulary and different priorities complicate cross-functional dialogue
  
  
• Solution imposition – Teams develop “solutions” for other departments without understanding their actual needs
  
  
• Resource competition – Departments compete rather than collaborate for limited organizational resources
  

One example of organizational dysfunction happened when Steve was conducting training for about 20 product managers. During the session, he emphasized the importance of customer insights gathered directly by product managers, not filtered through sales, support, or professional services. One participant mentioned getting valuable information from Salesforce, prompting another to ask, “How did you get access to Salesforce?” Only one of the twenty product managers had access to this customer data system. Those with access had it only because they previously worked in sales or support and still had their login credentials. The other product managers were surprised to discover they were being denied access to valuable customer information.

Organizational Barriers

Innovation-Enabling Structures

Rigid functional silos with limited communication

Cross-functional product teams with shared objectives

Restricted access to customer data and systems

Open information sharing with appropriate governance

Gatekeeping customer access

Coordinated but open customer relationship protocols

Solution-focused rather than problem-focused collaboration

Joint problem exploration before solution development

Steve also touched on the disconnect between leadership’s stated priorities and the organizational structures they maintain. A survey found that 87% of CEOs say that innovation is critical to their future and less than 20% think they have the team to get them there. This suggests that while innovation is nominally valued, organizations often fail to create structures that enable it.

The Evolving Role of Product Ops

As organizations seek to address some of the structural challenges that hinder effective product management, many have turned to creating dedicated Product Operations (Product Ops) functions. Our conversation explored the evolving role of Product Ops, its potential benefits, and the risks of implementing it as a patch for deeper organizational issues.

I asked Steve about his perspective on Product Ops, particularly in relation to a company’s ability to innovate. He expressed both appreciation for certain aspects of Product Ops and concern about how it’s sometimes implemented.

Steve observed that Product Ops emerged in part because of the transformation of product management roles in organizations adopting Agile methodologies, particularly Scrum. In these environments, product managers often became product owners who were primarily focused on supporting development teams rather than strategic market understanding.

This approach concerns both of us because it treats the symptom rather than addressing the root cause. I compared it to the creation of Project Management Offices (PMOs), which often emerged not to enhance project management capabilities but to compensate for perceived deficiencies in project managers’ competencies. Steve agreed, noting that PMOs often police product managers’ day-to-day work, checking whether tasks on Gantt charts have been completed.

Instead of this enforcement model, Steve suggested that Product Ops should function like DevOps, which helps programmers standardize their processes.

This vision of Product Ops focuses on:

• 
  
• Standardization – Creating consistent methods, templates, and processes for product management work
  
  
• Onboarding – Helping new product managers understand how the organization approaches product management
  
  
• Tool selection and implementation – Choosing and managing the product management tool ecosystem
  
  
• Best practices – Documenting and sharing effective approaches within the organization
  
  
• Access protocols – Establishing how product managers interact with customers and access internal systems
  

Product managers are often not properly onboarded, and only about 20% of product managers have received formal training in product management. Without standardized onboarding, new product managers are often left to figure things out on their own, leading to inefficiency and confusion.

When implemented correctly, Product Ops can bring significant value by addressing these standardization and onboarding challenges. However, both Steve and I expressed concern about Product Ops being used to patch over more fundamental issues, such as:

Problematic Product Ops Implementation

Effective Product Ops Implementation

Taking over strategic responsibilities from product managers

Enabling product managers to focus on strategic work

Creating a policing function to enforce compliance

Establishing helpful standards and best practices

Compensating for undertrained product managers

Providing training and development to enhance capabilities

Maintaining organizational silos with complex processes

Breaking down barriers to customer and market information

Product Ops should enable product managers to be more effective in their strategic roles—not take over that role or compensate for fundamental deficiencies in product management capabilities. When implemented correctly, Product Ops can help break down organizational barriers and standardize processes, allowing product managers to focus on high-value strategic work.

Conclusion

The path forward requires product managers to develop deeper customer understanding, strengthen problem articulation skills, and break down organizational barriers that impede effective product innovation. Organizations must invest in product management training, standardize processes through effective Product Ops implementations, and integrate AI tools thoughtfully to augment human capabilities rather than replace them. By focusing on these strategic priorities, product managers can position themselves as essential leaders, delivering value to both customers and their organizations.

Useful Links

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• Check out Product Growth Leaders
  
  
• Connect with Steve on LinkedIn or X
  
  
• Check out Steve’s book, The Strategic Role of Product Management
  

Innovation Quote

“Don’t bring me solutions. Bring me problems.” – Steve Johnson

“Never take down a fence until you know why it was put there.” – Steve Johnson

Application Questions

1. 
   
2. Problem-Solution Mindset Shift: How could you identify instances in your current product development process where your team jumps to solutions before fully understanding customer problems? What specific changes could you implement to shift toward more problem-focused discussions?
   
   
3. AI as a Companion: How could your team begin integrating AI tools as “companions” in your daily workflow? What tactical aspects of your product management role could benefit most from AI assistance, and how would this free you to focus on more strategic activities?
   
   
4. Breaking Down Organizational Barriers: What specific information silos exist in your organization that limit your effectiveness as a product manager? How could you advocate for greater access to customer data and insights across departmental boundaries?
   
   
5. Product Ops Implementation: How could your organization implement Product Ops in a way that enhances rather than replaces strategic product management? What standardization and onboarding practices would bring the most value to your product team?
   
   
6. Strategic Role Evolution: How prepared are you personally for the strategic evolution of product management in an AI-enhanced world? What skills gaps might you need to address to remain effective, and how could you develop deeper expertise in customer problem understanding?
   

Bio

Steve Johnson is an author, speaker, and product coach using modern methods to move products from idea to market. His approach is based on the belief that minimal process and simple templates result in a nimble product team.

Steve is CEO of Product Growth Leaders, offering a unique approach to applied coaching for product teams.

Steve was a long-time executive at Pragmatic Marketing as well as co-creator of the popular QuartzOpen framework. 

Steve’s work experience includes technical, sales, and marketing management positions at companies specializing in technology-enabled products. 

In 2020, Steve was awarded one of ten 51331 coins for contributing to the best practices of product management.

And for a little fun, Steve’s album of original music is available online. 

Thanks!

Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below.

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