Data as a product, AI as a catalyst: Winning requires a systemic, hands-on plan

To cut through industry platitudes, HFS sat down with a select group of enterprise data and analytics leaders. Our mission: uncover what it truly takes to win with data as a product and AI as a catalyst. The discussion focused on the core questions facing every ambitious enterprise:

• What organizational, cultural, and architectural changes are required to make data win?
• How are leading organizations leveraging AI to reinvent the data-to-decision lifecycle—and what’s hype versus reality?
• What does an effective data strategy look like, both inside the enterprise and across the broader ecosystem?

What emerged was an honest, practical view: winning in data and AI is not about technology selection or isolated innovation. The real differentiator is a systemic, hands-on plan that transforms data from a backend function into an actual business asset.

Most enterprises aren’t winning, and technology isn’t the problem

Despite significant investments, only one-third of enterprises are genuinely satisfied that their data aligns with business objectives. Ambitious AI roadmaps are everywhere, but most organizations struggle to generate value. The culprit is not a lack of AI tools or cloud platforms; it’s the persistent failure to address fragmented, incomplete, and poor-quality data, compounded by unclear data ownership and weak governance. If you’re not making data readiness a top business priority, your AI and analytics efforts simply won’t deliver results.

Winning demands systemic change, not just shiny new tools

Enterprise leaders can’t win by merely delegating data custodianship to IT or treating it as an afterthought. The discussion reinforced that winning organizations are those that shift culture, ownership, and accountability, i.e., elevating data to a revenue-generating, profit and loss (P&L)-oriented business product. This requires moving away from traditional central command-and-control models. Instead, the most effective enterprises are empowering business-led, federated data teams and building self-service data products that are actively managed within business units, not isolated in IT. If you still operate with centralized, legacy approaches, you’re simply not in the game.

AI is a catalyst for only those who have done the groundwork

AI is an accelerator for those who’ve done the foundational work. The focus group highlighted that leading organizations are now moving beyond basic operational AI to more innovative, business-driven use cases: customer benchmarking simulators, data enrichment platforms, and real-time compliance tools. But none of this is possible without rock-solid data integration, quality, and governance. For enterprises without this foundation, AI only serves to magnify existing dysfunction.

From hype to execution: The five-stage OneOffice Data Cycle

The HFS OneOffice model (see Exhibit 1) should be your north star, not just for IT, but for every business leader.

Exhibit 1: HFS OneOffice Data Cycle reinvents the data-to-decision lifecycle

Source: HFS Research, 2025

The key components of the OneOffice Data Cycle are:

1. Align data capture to business priorities: Don’t collect what you can’t monetize or operationalize.
2. Fix processes before moving to cloud: Cloud migration without redesigning for clean data is a recipe for disaster.
3. Automate with discipline: True automation is data-driven, not just robotic patchwork.
4. Make data available at speed, at scale: Invest in cataloging, lineage, and MDM before scaling analytics or AI.
5. Apply AI where it matters: Predictive, contextual, and customer-focused, but grounded in robust data practices.

Real-world enterprise moves: What winners are doing

• America’s leading diversified transportation services company: Built Catalyst AI, allowing customers to benchmark against simulated best-in-class fleets. This is only possible because of a decade-long commitment to disciplined data practices.
• Japanese international advertising and public relations company: Established a data transformation office, embedded data product teams within the business, and drove data literacy from the top down, securing genuine buy-in for foundational data modernization.
• American financial services company: Adopted a hub-and-spoke model that embeds business users in every data project and prioritizes simplicity, adoption, and business relevance over technical complexity.

According to forward-looking enterprises, here’s your winning plan:

• Redefine data ownership and accountability. Build federated, business-led data teams with P&L responsibility for data products, not just IT oversight.
• Establish radical transparency on data quality and governance. Invest in enterprise-wide data cataloging, lineage, and MDM. Expose these metrics regularly at the executive level and tie improvement to business KPIs.
• Make data and AI literacy a business mandate. Drive practical, role-based education from the boardroom to the front line, ensuring that everyone understands how to use and trust data.
• Prioritize foundational change before chasing advanced AI. Fix processes, governance, and data quality first, then pursue automation and analytics that deliver measurable business value.
• Architect for interoperability. Accept that fragmentation is reality; win by connecting business outcomes across platforms, not by forcing all data into one system.
• Measure and reward business outcomes, not technical achievements. Tie all data and AI initiatives directly to business goals—revenue, efficiency, customer experience—and be ruthless about discontinuing what doesn’t deliver.

The Bottom Line: Winning in the data and AI era is about operationalizing bold, practical change across the enterprise. The advantage will go to those who build governance, culture, and operating models that turn data into sustained business outcomes.

The ‘single source of truth’ is a myth in a multi-cloud, fragmented world. The future is about pragmatic interoperability and orchestration—often through emerging agentic AI approaches—not forcing all data into one lake. Enterprise winners are building data strategies that thrive amid fragmentation, focusing on connecting business outcomes across platforms and silos.