Harvesting Data for Insights

Building Practical Data Foundations for AI

Insights are only as effective as the data fueling them. But data starts necessarily messy and distributed—and fragmentation remains a persistent challenge. While many organizations have invested in modern architectures, the ability to seamlessly integrate, govern, and apply data at scale is still evolving. To truly unlock business value, the gap between data collection and data-driven decision-making must be bridged.

Enterprises are exploring mesh or fabric-based architectures and rethinking ownership models that decentralize control while maintaining standards. Leaders are balancing pressure between strategic and core horizontal investments and vertical solutions that solve near term business problems. Questions around operating models, stewardship responsibilities, and cross-functional accountability are central to designing for scale, quality, and resilience: Where does data sit in the enterprise: who owns it, who governs it, and how does it integrate into decision-making.

Modern data foundations must support real-time processing, observability, integration with ML Ops platforms, and a balanced approach to centralization versus edge utilization. With the growing number of AI models and data platforms, evaluating long-term viability, partner dependency, and ecosystem dynamics — and avoiding unnecessary vendor lock-in is essential, all while accelerating innovation at the same time. Best practices are emerging for platform selection, model partnerships, and prioritization across use case portfolios aligned to business goals.

The data-to-insight challenge is not new — but the landscape is shifting rapidly. LLMs and Generative AI are redefining how organizations approach unstructured data, insight generation, and scalability. As the velocity, complexity, and volatility of business increases, the size of the prize grows, and the need for foundational agility becomes ever more critical.

Operationalizing Enterprise Value at Scale

As organizations refine their AI and data strategies, a key challenge is ensuring today’s decisions remain adaptable to future technological, regulatory, and competitive shifts. The risk isn’t just falling behind—it’s overcommitting to a direction that may not align with the next wave of disruption. Data ecosystems, AI governance models, and cloud architectures will evolve — leaders build flexibility into their long-term strategies.

To move from pilot to platform, organizations must resolve issues around data quality, operationalization, and model governance. This includes building lifecycle management for insights, aligning use cases to business outcomes, and surfacing untapped opportunity areas in functions like commercial analytics, field ops, and customer support. As AI models move into production, risks such as model drift, opacity, and inconsistent outputs pose challenges for compliance and continuity. Data lifecycle management must include robust testing, monitoring, and revalidation practices across evolving contexts.

Organizations are increasingly embedding AI into workflows—not as a layer on top, but as a core part of how the business runs. Leaders are asking: where can AI be embedded into day-to-day decisions? How can we ensure that action—not just insight—emerges from data? What frameworks enable speed without sacrificing governance?

Operational scale also demands attention to AI economics. As compute requirements grow, many leaders are revisiting trade-offs between proprietary vs. open-source tools, on-demand vs. reserved infrastructure, and centralized vs. distributed models. Cost predictability, FinOps capabilities, and efficiency metrics are becoming as important as performance.

Key Questions:

1. What concrete steps can we take to accelerate the transition from fragmented data environments to a unified, AI-ready data foundation?
2. How do you organize for data at the corporate level - for instance - who in the company actually owns what data should we have that we don't ?
3. What frameworks are most effective for evaluating and selecting among the growing number of AI platforms and model partners?
4. How are organizations prioritizing data use cases that lead to actionable business outcomes rather than just generating insights?
5. How do we weigh architectural trade-offs—such as Medallion vs. Mesh—and define accountability for data ownership across the business?
6. How are organizations navigating quality assurance and lifecycle management for AI-driven insights?
7. What strategies are being used to bring telemetry or edge-generated data into enterprise analytics safely and effectively?
8. How do we ensure entitlements, controls, and auditability remain intact when aggregating diverse sources of enterprise data?

Data Ethics in the current Geopolitical Climate

Global Governance and Regulatory Complexity

With governments enforcing stricter AI and data privacy regulations, enterprises must navigate evolving compliance landscapes while remaining competitive. The absence of universal standards complicates compliance, requiring organizations to develop adaptable, forward-thinking governance frameworks. AI governance is now as critical as financial compliance, with trust and reputation tied to responsible AI deployment. Frameworks like the EU AI Act, U.S. executive orders on data transfers, and China’s cross-border regulations are reshaping how global firms approach data.

Regulatory fragmentation is a real concern. Organizations must harmonize their strategies across jurisdictions, build traceability into AI models, and ensure board-level oversight of AI risk. National security and sovereignty concerns are accelerating calls to re-architect how and where data resides. The trade-offs between global efficiency and local control are becoming more pronounced—and risk exposure is shifting accordingly.

Boards are evolving from passive oversight to active engagement—setting risk appetites, guiding capital allocation, and shaping accountability. AI is now core to competitive strategy, even as boards and leadership sharpen their fluency in evaluating both progress and risk exposure.

Ethics as Strategic Capability

Embedding ethical use into business strategy requires going beyond legal compliance. Enterprises are implementing AI ethics boards, responsible AI toolkits, and independent audits. Ethics is not just about bias mitigation—it’s about designing operating models that are secure, transparent, and accountable from the start. Customer expectations, partner standards, and ESG pressures are driving clearer thresholds for responsible behavior. Ethics by design has become a source of resilience and competitive differentiation.

In practice, this means knowing where data resides, how it flows, and which vendors and infrastructure partners introduce risk. Governance frameworks must remain adaptable—avoiding rigid models or technological dependencies that could become liabilities in a rapidly evolving landscape. Enterprises must ask: what’s required to make cross-border data flows safe, legal, and ethical? What are the trade-offs of localized cloud deployments or sovereign data zones?

Key Questions:

1. What practical approaches can ensure our data governance frameworks align with emerging and increasingly fragmented global regulations while supporting innovation?
2. In regions with shifting political agendas, how do we ensure the substance of ethical practices doesn't get lost behind changing language?
3. How do we address new risks introduced through shifting ecosystem partnerships in the changing geopolitics?
4. How do we balance AI autonomy with control, compliance, and ethical oversight?
5. What role should the board play in shaping, overseeing, and communicating ethics and governance stance globally?
6. What governance models best manage the ethical deployment of AI across distributed teams and global operations?

Leadership in the Age of Data and AI

Strategic Leadership and Operating Models

The industry is flooded with AI experimentation, but few companies have translated pilots into scalable value. AI leaders must shift from proof-of-concept to enterprise-wide transformation. This requires rethinking the operating model—moving from isolated initiatives to a coherent portfolio aligned to business strategy. Quick wins must be paired with long-term capability building. Pilots must evolve into embedded, scalable processes.

Success hinges on integrating AI into workflows across the enterprise—from supply chains to customer support. Agentic AI especially requires a re-architecture of decision-making at the edge. Scaling responsibly means defining clear governance, risk tolerance, and organizational accountability.

AI also demands a fresh look at AI economics: as compute requirements grow, leaders are exploring trade-offs between proprietary vs. open-source tools, on-demand vs. reserved infrastructure, and centralized vs. distributed models. Scaling responsibly includes planning for cost predictability, FinOps, and efficiency metrics alongside performance.

Talent, Capabilities, and Workforce Transformation

One of the biggest hurdles in scaling AI is leadership capability—not just technology. Organizations must address the growing skill gap, particularly among mid-level leaders tasked with AI execution. Leadership teams must rethink roles and responsibilities: where do AI translators, ethics leads, and product owners sit? How are KPIs aligned to AI value?

Best organizational structures support agile, cross-functional collaboration. Enterprises are embedding data scientists into business units, creating centers of excellence, and deploying agents close to decision-making. Boards and senior leaders play a key role in setting the tone, defining risk appetite, and building measurement frameworks.

Developing AI-ready talent is as important as developing AI itself. Future-ready organizations invest in reskilling, leadership development, and fluency across functions. The rise of “AI translators”—professionals who bridge business goals and technical execution—highlights this need. Recruiting, retaining, and reskilling talent are now core to strategy. As AI reshapes work, thoughtful change management becomes essential to bring people along.

Key Questions:

1. How do we move from isolated AI pilots to a portfolio of scalable, high-impact initiatives with measurable outcomes?
2. Agentic AI to be successful requires operating model changes - what organizational structures best support driving AI-driven business transformation?
3. How do we ensure senior leadership are waking up thinking about - and fully equipped to lead through AI-driven change?
4. What emerging roles (e.g., AI translators, ethics leads) are critical to scaling AI across the business, and where should they sit?
5. How do we integrate AI initiatives into performance metrics, compensation models, and incentives at all levels of leadership?
6. What are effective approaches to benchmarking our data and AI investment relative to industry peers?
7. How are firms balancing near-term optimization with long-term bets on transformative AI operating models?

Executive Technology Board (c)