Drive Product & Market Planning With AI

Harness AI to profile your business, segment your market, and benchmark competitors.

Analyst perspective

Integrating AI‐driven signals into marketing, sales, and R&D pipelines enables organizations to tap vast data sets, ranging from customer behavior and usage analytics to patent filings and market trends, to identify unmet needs and emerging opportunities.

This data‐informed approach accelerates ideation, reduces development risk, and ensures that new products and launches are not just incremental improvements but genuine innovations. When complemented by robust competitive intelligence (CI) software response capabilities, this approach gives companies the agility to monitor rivals’ moves, supply chain shifts, and regulatory changes in real time. Armed with this insight, companies can prioritize features that resonate with customers while preemptively countering competitive threats, ultimately strengthening their market positioning.

Embedding ethical oversight as a core pillar of this AI and CI strategy is critical. As organizations push forward with rapid product cycles, there’s a tendency to overlook privacy, fairness, and unintended social consequences – risks that can erode consumer trust and invite regulatory scrutiny. By weaving ethical frameworks into every stage of innovation, from data sourcing to feature deployment, companies can safeguard against bias, protect user data, and uphold transparency.

This holistic approach – merging AI-fueled innovation, CI responsiveness, and ethical stewardship – is a recognized best practice because it drives sustainable growth, delivering breakthrough products, outpacing competitors, and maintaining stakeholder confidence over the long term.

Joanne Morin Correia
Principal Director, Marketing
Info-Tech Research Group

Executive summary

Info-Tech Insight: Competitive intelligence/marketing intelligence (CI/MI) teams that combine AI-driven analysis with robust ethical frameworks deliver significantly higher strategic impact, enabling faster market responses and more innovative product planning.

Challenges to AI adoption do exist

Marketing and product teams using Gen AI are seeing its effects on revenue

“An increasing share of respondents report value creation within the business units using generative AI.”

– McKinsey & Company, 2025

AI has reached the point where we can both analyze data and generate insights in one system

This leap has provided a tremendous opportunity for product leaders to leverage LLMs to reliably augment their market research processes.

Machine Learning

• Analyzes data to uncover trends and segment audiences.
• Powers automation, predictive analytics, and strategic insights.

Generative AI

• Enables personalization and rapid A/B testing.
• Creates content and outputs (text, images, videos, audio).

AI tactics to drive value and mitigate risks

Drive continuous product innovation with an AI‑powered lifecycle

“Machine learning has enabled the development of AI systems that can perform certain human activities often better than humans can.”

– World Economic Forum, 2022

Use cases can span from broad to very detailed and narrow

Micro: Product & Opportunity Deep Dive
Complete feature-level comparison, understand voice of the customer, analyze green/ whitespace and differentiation, and assess ROI impact.

Meso: Segment & Company Analysis
Narrow by segment or persona, benchmark against peers, map out go-to-market tactics, and evaluate alliance and partner networks.

Macro: Market-Level Intelligence
Define the universe, identify top players, surface high-level KPIs, and spot emerging disruptors and M&As

What you look for depends on the segment and how thoroughly you want to explore it

Top AI use cases for product & competitive intelligence teams

Market Research & Competitive Analysis

Product Roadmap Prioritization

Price Optimization

Customer Feedback & Sentiment Analysis

Resource Allocation & Prioritization

Timeline & Risk Predictions

Use multiple LLMs when collecting results

We recommend using multiple models to both test and validate the data output. Additionally, you can run a comparison or gap report between two reports to summarize the differences and find new fact base sourcing.

ChatGPT reporting offers a more focused and in-depth analysis of market segmentation. It is more suitable for users who require: A detailed understanding of potential customer segments. Guidance on how to create buyer personas.	Gemini reporting provides a broader overview of the market. It places less emphasis on detailed segmentation. However, it includes a greater volume of data overall.

Models will behave differently and should be used to test, compare, and validate

Example: Run a ChatGPT vs. Gemini SWOT

The goal is to extract valuable insights through using AI

Evidence-Backed
Provides a concise takeaway grounded in the data or sources you’ve gathered.

Actionable
Points to what you can do or investigate next.

Relevant to Your Challenge
Directly answers your framed
CI question (“What…?”).

Concise & Contextualized
Outputs no more than 1-2 bullets. Includes the “so what” and/or action to take.

Create responsible AI guiding principles

Define a responsible approach to developing, implementing, and using AI systems.

• Responsible AI guiding principles are a cornerstone for AI governance. They should guide decision-making at all levels of the organization to ensure the organization leverages the innovative potential of AI while protecting shareholder value from risk and failure.
• You will use your responsible AI guiding principles as inputs into your policies, your governance structure, and governance processes. Think of your responsible AI principles as your written constitution, which constitutes the basis for the way you’ll govern the use of AI across your organization.
• Once completed and approved, publish your principles to help staff govern themselves in their approach to the use of AI in their day-to-day roles and to help your external stakeholders understand how you govern the use of AI internally.

Source: Info-Tech’s Govern the Use of AI Responsibly With a Fit-for-Purpose Structure blueprint

AI issues to be aware of

Key principles for establishing an AI-enabled research practice

Train your model and temper your expectations and usage of the AI outputs based on the use case

• Accountability
  • Define clear ownership for AI decisions and outcomes.
  • Establish governance structures to oversee AI systems.
• Fairness & Bias Mitigation
  • Audit training data for representation gaps.
  • Implement bias‑detection processes throughout development.
• Explainability
  • Surface interpretable, defensible model outputs for stakeholders.
  • Provide plain language rationales for automated decisions.
• Data Privacy & Consent
  • Ensure user data is collected, stored, and used with explicit permission.
  • Adopt data minimization and anonymization techniques.
• Continuous Monitoring
  • Track performance drift and ethical compliance over time.
  • Perform independent reviews and ethical impact assessments.

Info-Tech’s methodology to drive product and market planning with AI

Insight summary

Deliver Significantly Higher and Faster Impact
CI/MI teams that combine AI-driven analysis with robust ethical frameworks deliver significantly higher strategic impact, enabling faster market responses and more innovative product planning.

Create a Solid Foundation
Establishing the right data sources and AI tools creates a solid foundation that can be scaled across departments.

Use Insights to Accelerate Decisions
Embedding AI insights into routine planning sessions accelerates decision-making and enhances product alignment.

Reinforce Ethical Frameworks
Reinforcing ethical frameworks ensures that this rapid innovation and market agility occur responsibly, protecting brand reputation and building long‐term stakeholder trust.

Collaborate to Build Scenarios for Wider Adoption
Using scenario-based workshops and interactive dashboards fosters cross-functional understanding and prompt wider CI adoption.

Cross-Department Usage Helps Planning
Democratizing insight usage expands the impact beyond product teams into marketing, finance, and strategic planning.