As artificial intelligence (AI) continues to mature and the prospects of artificial general intelligence (AGI) loom on the horizon, product management in particular in software-as-a-service (SaaS) organisations stands at a critical junction. Traditionally, product managers have served as orchestrators — shaping the direction of discovery, prioritisation, design, development, and commercialisation of software solutions. Yet as powerful AI increasingly automates tasks such as feature prioritisation, data analysis, and aspects of user research, product managers are well advised to redefine their roles.

This post begins by examining the foundational frameworks and responsibilities of product managers today. It then explores how AI and the eventual rise of AGI may automate and shape these functions, compelling product managers to shift their focus. Finally, it highlights emerging opportunities for product professionals to move upstream into strategic leadership roles, downstream into technical or design-oriented areas, or laterally into adjacent domains such as design and marketing. Throughout, it emphasises the new, critical role that product managers will play — interpreting automated insights through a human lens of ethics, empathy, and strategic foresight.

SaaS product management has historically relied on well-established frameworks to manage complexity and keep products aligned with organisational and customer goals.

Market and Customer Understanding

Product managers conduct ongoing market and customer research to identify user needs, pain points, and emerging trends. They employ methods such as interviews, surveys, usability tests, and ethnographic studies. Principles from design thinking and methodologies like Jobs-to-be-Done (JTBD) guide this discovery phase, ensuring a deep understanding of the problems that their products must solve.

Prioritisation and Roadmapping

Established frameworks such as RICE (Reach, Impact, Confidence, Effort) and MoSCoW (Must-have, Should-have, Could-have, Won’t-have) help product managers organise extensive backlogs into coherent roadmaps. Agile methodologies, such as Scrum (or Kanban), enable iterative development that responds dynamically to customer feedback and market fluctuations.

Data-Driven Decision-Making

Evidence-based decision-making underpins effective product management. Product managers rely on analytics platforms, usage telemetry, segmentation analysis, and A/B testing to validate assumptions and measure key performance indicators. This data-driven approach ensures continuous improvement and adaptation to real-world usage patterns.

Cross-Functional Collaboration

Modern product managers operate at the intersection of multiple disciplines. They collaborate closely with engineers, CX (Customer Experience) and UX (User Experience) designers, product owners, business analysts, data scientists, marketers, sales teams, and customer success managers. Their role as communicators and negotiators ensures alignment, resolves conflicting priorities, and transforms broad strategic goals into tangible development tasks.

Commercial Strategy and Positioning

Product managers set pricing strategies, define market segments, articulate unique value propositions, and shape go-to-market plans. They monitor competitive landscapes, economic conditions, and brand positioning. This commercial perspective ensures the product not only solves user problems but also generates sustainable revenue and brand loyalty.

Emerging powerful AI capabilities, and the anticipated breakthroughs that AGI promises in the future, have the potential to automate many routine product management tasks. While “narrow” AI tools available today can already analyse data, draft insights, and suggest improvements, AGI is likely to advance this to a transformative degree.

Automated Market and Competitive Analysis

AI-driven tools can rapidly ingest vast quantities of market data — news articles, competitor pricing, feature sets, usage trends, and sentiment analysis — and synthesise them into actionable insights. This frees product managers from laborious data gathering, allowing them to focus on strategic interpretation and visionary thinking rather than repetitive research.

Intelligent Backlog Management and Feature Prioritisation

By examining user behaviour, support tickets, and revenue patterns, AI can advise on backlog priorities with remarkable speed. Advanced models can simulate potential feature impacts on metrics such as engagement or conversion. Product managers still retain the authority to approve or challenge these AI-driven recommendations, but initial sorting and filtering will be largely automated.

Scalable User Research and Customer Feedback Analysis

Natural language processing enables AI to parse interviews, support call transcripts, survey responses, and social media discussions for recurring themes. These patterns allow product managers to understand latent user needs more quickly and thoroughly than ever before. Instead of manually sifting through transcripts, product managers can focus on the strategic implications of user sentiments uncovered by AI.

Adaptive Roadmapping and Forecasting

AI can continuously adjust product roadmaps in response to real-time data, economic indicators, or emerging platform technologies. Instead of updating roadmaps every quarter, product managers can rely on AI-driven models to monitor progress and propose shifts in priorities in real-time. This frees them to spend more time on higher-level planning, scenario analysis, and nurturing an innovation pipeline.

Automated Reporting and Visualisation

Routine reporting and data visualisation can be delegated to AI agents that monitor metrics and highlight anomalies. Product managers gain instant access to performance insights, reducing the need for manual report generation. With the help of AI they can then direct their efforts towards interpreting these insights, understanding underlying causes, and crafting strategies to address them.

As AI progressively automates operational tasks, product managers must reorient their focus. Rather than seeing automation as a threat, they can embrace it as an opportunity to enhance their influence. There are three broad directions in which product managers can expand their remit:

Upstream: Strategic Leadership and Business Influence

Moving upstream entails stepping closer to executive-level strategy, governance, and portfolio oversight. Product managers who choose this path can:

• Shape corporate portfolios, guiding which products to invest in, scale back, or retire.
• Oversee ethical frameworks and compliance standards, ensuring responsible use of AI and adherence to evolving regulations.
• Lead innovation and R&D initiatives, exploring future product categories enabled by advanced AI capabilities.

Stepping into upstream roles often suits those with an MBA or a similar business degree, who excel at holistic thinking, stakeholder negotiation, and envisioning long-term organisational growth. Product managers taking this route may increasingly resemble “mini-CEOs” of their product lines.

Downstream: Technical and Operational Depth

The downstream path involves acquiring technical, design, or operational skills to remain close to the product’s implementation. Product managers who choose this direction might:

• Develop deeper engineering literacy, understanding system architecture, APIs, and cloud infrastructures to collaborate effectively with technical teams.
• Enhance UX or CX design expertise, refining interfaces and user flows that AI initially drafts, ensuring final products are both intuitive and appealing.
• Gain capability in low-code/no-code development, enabling rapid prototyping and iterative improvements on product features.

This approach appeals to product managers who relish hands-on involvement in product quality, user experience, and technical feasibility. It ensures that as AI handles routine tasks, human oversight preserves the nuance and creativity required for exceptional products.

Lateral: Broadening into Design, Marketing, and Other Domains

Lateral movement means leveraging the time saved by automation to incorporate skills traditionally associated with other roles:

• Integrate design thinking into strategic decision-making, championing accessibility, usability, and emotional resonance within products.
• Develop marketing capabilities, influencing brand positioning, messaging, and user acquisition strategies. This is especially relevant when AI generates campaign concepts that product managers then refine and customise.
• Delve into business analytics and operations, becoming adept at integrating product performance with broader organisational metrics.

By blending insights from various fields, product managers can remain relevant as multifaceted leaders who align product strategy with holistic user experiences, market narratives, and organisational priorities.

Balancing Upstream, Downstream, and Lateral Moves

Choosing a single path — upstream, downstream, or lateral — is not strictly necessary. Many product managers will adopt a “T-shaped” skill set: cultivating depth in one area (for example, strategic planning) and breadth in several complementary fields (such as UX design, data science, and marketing). This balanced approach enables them to translate strategic goals into practical initiatives, while remaining adaptive as AI capabilities evolve and organisational structures flatten.

Even as existing AI and potential future AGI systems automate many day-to-day tasks, product managers retain a uniquely human value proposition. They excel at interpreting context, integrating qualitative factors, and applying judgment in ways that AI cannot.

AI can summarise user behaviour and generate recommendations, but it struggles to understand organisational culture, brand reputation, or evolving social norms. Product managers add irreplaceable value by interpreting automated insights through the lens of these intangible factors. As trust becomes a key differentiator in an AI agent-negotiated world, product managers ensure that products respect user privacy, follow regulatory requirements, and consider societal implications. They maintain empathy for users, ensuring that products cater to diverse needs and do more than simply optimise metrics.

In uncertain markets defined by disruption and complexity, product managers excel at navigating ambiguity. While AI can model probabilities and forecast trends, humans bring the capacity to weigh moral considerations, balance stakeholder interests, and envision futures not easily captured by data. The strategic, human-centric product manager — grounded in empathy, and imaginative thinking — becomes the keystone of innovation in any product-led organisation.

In an organisation transformed by powerful AI, product managers emerge as strategic integrators. As day-to-day analysis, reporting, and some forms of user research become automated, product managers can shift their time and energy towards shaping product portfolios, advising executives, and cultivating an environment where AI-driven recommendations are guided by human values.

Organisations are likely to flatten hierarchies as automation reduces the need for multiple operational layers. In this scenario, product managers have the opportunity to interact more directly with executive leaders, influencing multi-year product strategies and long-term investment decisions. Freed from mundane tasks, they focus on synthesising cross-functional insights — aligning insights from design, engineering, marketing, and data science into coherent narratives that inform organisational direction.

As AI advances, product managers will continue to evolve from what used to be a lot of project coordination. They will be in demand as visionary leaders who combine strategic foresight, ethical stewardship, and a nuanced understanding of human behaviour and culture, ensuring that advanced technologies serve genuinely valuable and meaningful ends.

To thrive in this environment reshaped by AI, product managers are well advised to invest in new skill sets today:

• Strategic and Systems Thinking:
  Develop a deeper understanding of business strategy, economics, and systems thinking. This equips product managers to anticipate market shifts, set long-term visions, and align product portfolios with organisational objectives.
• Ethics, Governance, and Policy:
  Build knowledge in AI ethics, data governance, and regulatory frameworks. As product managers help shape products that interact with sensitive user data and complex legal landscapes, ethical guidelines and compliance standards will become central to their remit.
• Advanced Analytics and Data Literacy:
  While AI can handle analysis, product managers should remain critical evaluators of automated outputs. Understanding Bayesian reasoning, causal inference, and basic data science principles helps them challenge AI-driven recommendations and identify where deeper investigation is warranted.
• User Experience and Design Thinking:
  Continual education in design thinking, user research methods, and inclusive design ensures that products remain empathetic, accessible, and culturally sensitive, even as automation increases. This emphasis on human-centred design sets truly remarkable products apart from those that merely operate efficiently.
• Leadership, Negotiation, and Communication:
  As product managers interact more directly and more often with executives and cross-functional stakeholders, strong leadership and communication skills become crucial. Invest in programmes that enhance negotiation, team facilitation, and influencing skills.
• Agile Methodologies and Continuous Learning:
  Even with widespread automation, iterative learning cycles remain fundamental. By embracing agile principles, product managers can continually adjust product strategies, using real-time feedback to iterate, improve, and pivot as needed.
• Practical Exposure to Emerging Technologies:
  Hands-on experimentation with AI platforms, machine learning toolkits, and low-code environments ensures product managers stay abreast of technological advances. Practical projects foster curiosity and help product managers guide AI’s application more effectively to reimagine our future.

The rise of powerful AI and, in time, AGI will automate many operational aspects of product management, from backlog sorting to user sentiment analysis. However, this must not herald the end of the product manager’s role. Rather, it invites a transformation — freeing product managers to focus on strategic and human-centric dimensions of product leadership.

By moving upstream into executive strategy, downstream into technical craftsmanship, or laterally into complementary domains like design and marketing, product managers can enhance their value and relevance. They will remain essential as interpreters and custodians of automated insights, ensuring that products respect customer trust, cultural values, and societal well-being.

For those passionate about product management, now is the time to invest in the skills that will define the future of the craft. Advancing one’s strategic thinking, leadership capabilities, technical craft and design sensitivity will prove invaluable. The future belongs to product managers who integrate human insight with machine intelligence — shaping strategies that are not only profitable but also responsible, inclusive, and impactful

Below is a categorisation of tasks from the ISPMA reference architecture according to how significantly AGI could reshape them. This classification follows these guidelines:

• Completely reshaped: Tasks that are highly amenable to automation, data-driven optimisation, and autonomous decision-making by AGI with minimal human oversight.
• Mostly reshaped: Tasks that AGI will strongly influence through automation, predictive analytics, or content generation, but still require human validation or strategic adjustments.
• Somewhat reshaped: Tasks that AGI can support by providing insights, recommendations, or partial automation, but where human judgement, creativity, strategic vision, or nuanced negotiation remain central.
• Not at all reshaped: Tasks for which AGI may offer insights but will have minimal impact on the fundamental human-driven decision-making process.

Completely Reshaped by AGI

(Primarily data-driven, repetitive, or operational tasks that AGI can largely take over)

• Market Analysis: AGI can continuously ingest market data, identify trends, and recommend responses.
• Product Analysis: Automated product usage analytics, error detection, and pattern recognition can be handled by AGI.
• Detailed Requirements Engineering: AGI can translate user feedback, usage metrics, and stakeholder input into structured requirement drafts at scale.
• Operational Marketing (e.g. campaign execution, basic content personalisation): Automated content generation, campaign optimisation, and channel targeting.
• Opportunity Management (identifying sales leads, scoring opportunities): AI-driven lead scoring, customer segmentation, and predictive recommendations.
• Channel Preparation: Automated analyses of channel performance, partner fit, and content localisation.
• Operational Sales: Dynamic pricing suggestions, personalised sales scripts, automated follow-ups.
• Operational Fulfilment: Automated order management, inventory predictions, and logistics coordination.
• Technical Support: Chatbots and virtual agents handling troubleshooting, FAQs, and common issues.
• Service Execution (routine service tasks): Automated scheduling, ticket triage, and knowledge base maintenance.
• Operations (routine tasks in service/support): AI-driven optimisation of repetitive operational workflows.

Mostly Reshaped by AGI

(AGI will provide strong analytical, generative, or optimisation capabilities, but humans remain involved for final judgement calls or strategic alignment)

• Roadmapping: AGI can propose roadmap revisions based on performance data, competitor moves, and user behaviour, while humans ensure strategic fit.
• Product Requirements Engineering (at a higher, more strategic level): AGI drafts initial requirements and identifies gaps, humans refine strategic priorities.
• Pricing: AI suggests dynamic pricing strategies based on usage patterns and market signals; humans oversee brand perception and customer fairness.
• Release Planning: AGI forecasts ideal release windows, resource needs, and user impact; humans validate for strategic coherence.
• Marketing Planning: AI proposes campaign themes, timing, and budgets; humans finalise messaging and ensure brand alignment.
• Value Communication (messaging, content drafting): AI drafts messaging variants; humans refine tone, brand identity, and cultural nuances.
• Product Launches (operational aspects): AGI coordinates launch timelines, predicts adoption curves; humans guide messaging and stakeholder communication.
• Sales Planning: AI predicts sales forecasts, quota distributions, and channel mix; humans decide strategic targets and relationship investments.
• Customer Relationship Management (tactical execution): AI suggests personalised engagement strategies; humans handle high-level relationship building, at least for key accounts.
• Service Planning and Preparation: AI allocates resources, predicts service load; humans ensure alignment with service quality standards.
• User Experience Design (initial drafts): AI proposes layouts, workflows, and prototypes; humans refine aesthetics, brand identity, and emotional resonance.

Somewhat Reshaped by AGI

(AGI offers substantial insights and automation in research, forecasting, or support, but these tasks depend heavily on human creativity, negotiation, risk tolerance, or strategic vision)

• Positioning and Product Definition: AI can suggest market positions or potential differentiators, but brand identity and product ethos require human creativity.
• Delivery Model and Service Strategy: AI can highlight service gaps or opportunities, but humans choose strategic directions and partnership models.
• Ecosystem Management: AI identifies potential ecosystem partners and integration points; humans cultivate relationships, negotiate terms, and align visions.
• Sourcing: AI can rank vendors and predict costs; humans handle negotiations, trust, and long-term partnership considerations.
• Financial Management: AI assists in forecasting and scenario analysis; humans evaluate risk, ensure compliance, and make trade-off decisions.
• Legal and IPR Management: AI can summarise regulations or suggest contract templates; humans interpret laws, manage negotiations, and accept legal risk.
• Performance and Risk Management: AI flags risks, models scenarios; humans set risk tolerance, ethical standards, and response strategies.
• Product Architecture Management: AI proposes architectural options and identifies technical trade-offs; humans choose solutions aligned with regulatory, ethical, and long-term maintainability standards.
• Development Environment Management: AI optimises toolchains and workflows; humans ensure compliance, security, and cultural fit.
• Development Execution: AI writes code, runs tests, identifies bugs; humans provide vision, oversee architectural coherence, and make complex design choices.
• Quality Management: AI finds defects and suggests improvements; humans define quality thresholds, handle non-quantifiable criteria, and ensure user delight.
• Product Life Cycle Management: AI suggests when to retire or pivot products; humans consider brand legacy, strategic partnerships, and customer loyalty implications.

Not at All Reshaped by AGI

(AGI can inform but will not fundamentally alter the human-driven nature of these tasks, which rely heavily on top-level judgement, corporate ethics, strategic vision, and human relationships)

• Corporate Strategy: AI can provide data-driven forecasts and scenario analyses, but setting long-term corporate vision and cultural values remains a human responsibility.
• Portfolio Management: AI can recommend portfolio adjustments, yet senior leadership must balance strategic bets, corporate mission, and brand synergy.
• Innovation Management: AI can suggest new areas to explore, but identifying breakthrough opportunities, fostering creativity, and nurturing an innovation culture depend on human intuition and strategic risk-taking.
• Resource Management: AI can propose resource allocation and manage AI agents; humans must navigate organisational politics, morale, and intangible team dynamics.
• Compliance Management: While AI can flag potential compliance issues, interpreting complex regulations, negotiating with regulators, and making ethical calls rest with humans.

In practice, every task may be influenced by powerful AI at least to some extent, as even strategic decisions can benefit from data-driven insights. However, the categorisations above reflect where AGI’s influence will be strongest and most autonomous (completely or mostly reshaped) versus where it will remain advisory, requiring significant human input (somewhat reshaped), or remain largely untouched in its fundamental human-driven essence (not at all reshaped).