How do you use data and AI at scale? 6 building blocks

20 hours ago
4 min read

Many organizations have invested significantly in data and AI. Platforms are in place, talent is hired and use cases exist. Yet enterprise-wide impact often remains limited. In this article, we outline six building blocks that help organizations expand the impact of data and AI at scale. This means moving beyond successful pilots or isolated AI solutions in various departments, towards fully end-to-end utilization of data & AI across the organization.

What does “data and AI at scale” look like in practice?

Using data and AI at scale means integrating them into core business processes and decision-making. This ensures sustained contribution to performance and growth, no longer just on isolated pilots and AI initiatives. Data and AI operate as core capabilities alongside established business disciplines.

For instance, demand forecasts in supply chains are continuously updated using machine learning and directly drive planning decisions, pricing models dynamically adjust based on real-time market and customer data, and customer service teams rely on AI-assisted tools to resolve issues faster and more consistently.

At this level of AI maturity, organizations enable new revenue streams or services, support faster and more informed decisions, and create ongoing value across functions.

Why is scaling often difficult?

Organizations frequently encounter structural and organizational challenges on their way to scaling. Common issues are limited alignment between strategic priorities and data initiatives, unclear ownership across business and data teams, inconsistent data quality, uneven adoption across departments and struggling to keep up with evolving regulatory and governance requirements.

To help overcome these challenges, IG&H has defined six building blocks to utilize data & AI at scale.

family of 3 sitting behind wooden building blocks on table, playing with building blocks together

The six building blocks to scale data and AI

1. Translate business strategy into a Data and AI strategy

Scaling begins with focus. Leadership teams benefit from a clear view on how data and AI contribute to business outcomes. This creates direction and supports consistent decision-making. An effective Data & AI strategy:

Clearly links to the overall strategy and shows how Data & AI contribute
Shows how the impact and success of Data & AI is measured
Includes clear Data & AI guiding principles which are the ‘rules of the game’ to realize the strategy

2. Design a target operating model

Execution improves when roles, responsibilities and collaboration models are well defined. Governance becomes tangible through the operating model. Success depends on clearly embedded mechanisms that guide how teams make decisions, manage risks and collaborate in daily operations. A strong data & AI operating model includes:

Clear ownership and decision rights; This includes ownership for data and ownership for AI (e.g. algorithms, agents)
Structured collaboration between business and data teams, with clear accountabilities and responsibilities on both sides
Maximum alignment with the existing operating model, governance structures and organization; utilizing as much as possible from existing structures

3. Implement strong data and AI processes

Bad data breaks AI long before bad models do. Weak data quality limits AI performance early in the lifecycle, often before model design even becomes a factor. Reliable outcomes depend on disciplined data practices. Gain and maintain control over data by setting up data management as a continuous process. This way, data becomes a valuable and reliable resource for data analysis and AI, as well as for your existing business operations. Key elements include:

Continuous data quality management, embedded in the organization
Continuous management of the master data, the metadata and the risks related to data and AI
Continuous execution of an AI delivery funnel, from idea to scaled solution. Including the structural management of AI solutions when operational

4. Establish a single source of truth

Without a single source of truth, teams operate with different versions of reality. Demystify outcomes by selecting your source data. This often demands navigating multiple systems, legacy complexity and peeling back layers of ownership. Continuous data management can be targeted at the assigned sources of truth. A single source of truth:

Defines authoritative data sources
Supports consistent reporting and increases trust in AI solutions
Reduces ambiguity in decision-making

5. Strengthen adoption through change management

Sustained impact depends on behavioral change across the organization. Data & AI fluency is just as important as deep specialist expertise. Not everyone needs to build models or build data pipelines, but every function (finance, operations, supply chain, HR, etc.) needs people who can confidently work alongside data and AI.

The most effective AI teams are not necessarily always the most technically advanced; they are the most integrated. Ideally, domain expertise and data and AI experts operate in true partnership, engaging in continuous dialogue rather than working in parallel. Effective approaches include:

Targeted capability building, via utilization of AI in practice, experimenting (including failing), ambassadorship and training
Communication that links data initiatives to business value at both strategic ánd operational level so people can relate
Visible leadership engagement and lead by example
Interventions tailored to specific teams or moments, based on experience in practice

6. Establish AI risk and control frameworks

Establishing clear AI risk and control frameworks is essential for responsible and scalable use of AI across the organization. Structures provide clarity and guardrails for teams, build confidence among stakeholders, and create the conditions needed for broader and more sustainable adoption. Important components include:

Policies and guidelines
Risk management frameworks
Alignment with regulatory requirements such as the AI Act

Scaling data and AI is a balancing act

In reality, scaling data and AI is not only a technological challenge, but also an organizational one. It requires a coordinated approach that brings together strategy, organization, data, technology and behaviour. Key is developing your data and AI practices with the end goal in mind, which is real sustainable impact for your organization. Delivering a pilot is a valuable step in this process, but not the end result. Embedding data and AI in your processes, systems, governance structures and way of working will help with scaling and realizing true impact.

Author: Pim Roessink

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