Product Manager - Business Analytics (IBM)

As a GTM Product Manager, I shaped GTM strategy for IBM's analytics suite, building curated product enablement, and managed product instrumentation.

Highlights: Year: 2022-2023

  • Go-to-Market Leadership: Shaped GTM strategy across IBM’s Business Analytics portfolio - Cognos Analytics, Planning Analytics, Cognos Controller, Analytics Content Hub, and Business Analytics Enterprise.

  • AI-Powered Enablement: Designed and launched a chatbot that automated sales inquiries, reducing dependency on product teams and improving internal knowledge access.

  • Cross-Functional Collaboration: Partnered with sales, marketing, legal, and engineering teams to ensure messaging was accurate, compliant, and aligned to roadmap strategy.

My work

From Product Capability to Market Clarity

I owned the go-to-market strategy across IBM’s core Business Analytics portfolio, including Cognos Analytics, Planning Analytics, Cognos Controller, Analytics Content Hub, and Business Analytics Enterprise.

My role sat at the intersection of product, sales, and marketing, translating complex analytics capabilities into clear, compelling narratives that sellers and customers could act on:

  • Led market positioning and use-case development for enterprise analytics, planning, and financial performance products.

  • Built sales enablement assets: battlecards, architecture explainers, demo flows, and competitive narratives, used by 50+ global sales and business stakeholders.

  • Partnered cross-functionally with marketing, business partners, engineering, and field teams to ensure messaging was accurate, compliant, and aligned with roadmap direction.

  • Supported multiple 0→1 product launches of Analytics Content Hub and Business Analytics Enterprise, aligning pricing, packaging, and value propositions to accelerate early adoption.

Computer screen displaying analytics dashboards with graphs of page load times, bounce rates, sessions, and session metrics.

AI Enablement

  • Sales Bottlenecks Slowed Growth

    Despite strong products, sellers were heavily dependent on product teams to answer repeated questions about:

    Architecture and integrations

    Product capabilities and limitations

    Analytics frameworks and positioning

    This manual reliance created friction:

    Slower deal cycles

    Inconsistent messaging

    Limited product team bandwidth for roadmap execution

  • An AI-Powered PM-bot

    I led a team to design and launch an AI-powered internal PM chatbot that centralized product knowledge across the Data and AI portfolio.

    What it did:

    Integrated analytics documentation, product catalogs, and GTM assets.

    Delivered real-time answers on product capabilities, architecture, and use cases.

    Enabled sellers to self-serve product knowledge without waiting on product teams.

    This shift enabled unified self-service as a channel.

  • Reduction in dependency on product teams via the AI-powered PM bot, improving internal learning velocity and scalability

    Identified, through usage data, when custom sales narratives were required and when standard messaging was sufficient, thereby enabling teams to scale faster without sacrificing relevance.

Key Takeaways

  • A line graph on a computer screen shows fluctuations in data over time, with peaks and valleys, featuring time markers at the bottom and numerical values on the left side.

    GTM is a product lever

    Clear positioning and enablement directly drive adoption and revenue velocity.

  • A computer chip with the letter 'AI' on top of it, connected by gold pins to a circuit board.

    AI works best when operationalized

    Internal tools can unlock outsized efficiency before ever facing customers.

  • Digital artwork of a robotic hand reaching out to a human hand against a blue gradient background.

    Analytics adoption is about trust

    Sellers and customers engage faster when complexity is abstracted into usable narratives.