Scaling GenAI: 6 essential considerations

Many organizations have explored GenAI extensively, with numerous pilots. However, simply adding more pilots and experimenting with different use cases doesn’t result in meaningful enterprise value. Achieving a significant ROI comes from investing and building the right foundations that enable scalable, repeatable solutions across the business.

Leaders who succeed with GenAI go beyond cost savings. They use it to reshape core business processes across the organization, spark innovation, and unlock new growth.

What defines these leaders? Below we outline 6 essential considerations CIOs and technology leaders should prioritize if they want to see real results from GenAI.

1. Anchor every initiative in business value

To deliver lasting impact, AI initiatives should start with a clear anchor: how the change will improve one or many measurable business outcomes. Ensure each use case is tied directly to KPIs, providing a clear thread to strategic value. Further principles that set successful organizations apart are:

• Focus on identifying use case patterns that can be applied across multiple parts of the business, rather than isolated pilots. Success comes from disciplined execution, focusing on solutions that deliver measurable value and compound over time.
• Scaling should follow proven results from pilots, where evaluation criteria defined beforehand show satisfactory scores. Effective scaling requires balancing speed with value-driven investment, supported by continuous progress tracking to minimize waste.
• Remain grounded in the practical reality of GenAI today. Avoid overestimating impact or underestimating complexity, especially when shaping investment cases. Managing the expectations of senior sponsors, who have often seen bold headlines and compelling demos, is key to maintaining long-term support.

2. Data readiness and governance are non-negotiables

Enterprises need to understand their data sources, and types of data (structured, semi-structured, and unstructured).

A data lakehouse unifies data for analytics, ML, and GenAI. Early MDM creates a single source of truth and prevents duplicates and inconsistencies.

Governance is equally important: clear accessibility policies, decision rights, and lifecycle management ensure that data can be found, accessed, and used appropriately – without falling back into silos.

3. Treat AI platform(s) as products, for flexibility and scale

Treat your AI platform (e.g. AWS Bedrock and SageMaker, Azure AI Foundry, or Google Vertex AI) as a product: develop a roadmap, prioritize employee experience, continuously improve, and ensure strong governance. These platforms evolve rapidly, so staying informed about emerging capabilities is essential to support development.

We advise using sandbox environments for safe experimentation and designing modular solutions so components like models and data sources can be swapped easily.

Once key GenAI patterns (e.g. embedding and indexing data, chatbots with tool use, agentic AI) are proven, establish them as enterprise capabilities. Reuse learnings across teams to accelerate progress.

Legacy systems add complexity. Use integration layers where possible, but if systems can’t meet security or performance needs, plan for modernization.

4. Organize for delivery: centralize platforms, decentralize outcomes

How you structure your AI operating model directly affects delivery and risk. A central team should own AI platforms, standards, and guardrails, while domain teams apply proven patterns locally.

Cross-functional teams working closely with the business drive fast iteration and practical feedback. Be mindful that too many disconnected pilots can create complexity and slow progress.

Successful initiatives ensure model refinement is understood to be an ongoing process, with appropriate capacity set aside. Define procedures for updates and ensure strong observability to catch issues early and maintain control.

5. Prioritize security, explainability and compliance

As AI scales, so do risks. Security, privacy, and ethics must be built into the platform. ‘Responsible by design’ means embedding filters, compliance, and observability from the start, not as afterthoughts.

Ongoing monitoring is key: track model drift, gate launches with evaluations, and keep humans in the loop for critical decisions. Every output must meet current regulations and be adapted to future ones.

Lastly, for some organizations, data locality or risk posture will make private environments the right choice. For others, the ability to scale quickly and flexibly will make the public cloud indispensable.

Read more on ensuring your AI is secure (opens in a new tab)

6. Control costs with smarter model choices

Critical to ensuring a high ROI is successful management of costs. GenAI solutions can drive enormous costs if not carefully deployed and managed. Start with cost in mind – use the smallest capable model and apply caching, quotas and workflow-level budgets.

Crucially, budgets should account for development and operations. Ongoing monitoring and improvement take time, and without discipline, ‘run’ costs can quickly exceed development spend.

Closing thoughts

Scaling GenAI is not easy, but leaders successfully manage all 6 of these areas: they prioritize understanding the enterprise data they have, where GenAI can enable business transformation, and develop solutions grounded in practical reality. They also build on emerging best practice and establish enterprise platforms and patterns.

Planning for the long run includes investing in the right foundations, managing expectations, and maintaining a constant focus on cost management and ROI.

Read how Gen AI helps Helvetia speeds up document processing (opens in a new tab)

⁽¹⁾ Source: https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf