Driving Adoption of Scalable Data & AI Capabilities

CIOs are being challenged to move beyond AI experimentation and deliver business impact at scale. However, while building AI models is now more accessible than ever, many proofs of concept stall in the gap between pilots and production.

Indeed, the odds of progressing AI initiatives to a place where they are delivering full business impact, securely, are not great: industry estimates find that 70–90% of AI pilots remain stuck in “pilot purgatory” ⁽¹⁾.

To help clients address this huge innovation gridlock, our experts provide advice on building and driving adoption of secure, scalable Data & AI capabilities, effectively moving from ‘pilot to production’.

The answers lie not in the technology alone, but in the strategy behind it. Let’s dive in.

4 key data & AI challenges

How do we build scalable AI systems?

Our experts have identified 6 essentials every CIO should prioritize to scale GenAI effectively, starting with anchoring use cases to measurable business outcomes and KPIs, not hype.

From there, data readiness, governance, and platform integration become non-negotiables. Delivery models must be able to balance speed with control, while security, legislation, and responsible AI should be built in from the start. Lastly, effective cost management and a clear model strategy keep scaling sustainable.

Scaling GenAI effectively only comes from investing and building the right foundations, meaning those which accelerate solutions being scaled, and support reshaping activities and business transformation.

Read the article to understand how to shift GenAI from isolated pilots to repeatable, scalable solutions that compound value and accelerate our AI transformation.

Read the full article

How do we build secure & trustworthy AI?

AI is creating new, unpredictable vulnerabilities beyond traditional IT. To respond, organizations need a holistic approach to ‘Trustworthy AI’ that goes beyond cybersecurity to include legal, ethical, privacy, and business risks.

In his latest article, cyber expert Florian Pouchet outlines how CIOs are best positioned to steer their organizations through this new risk terrain, aligning innovation with security, trust, and compliance through cross-functional collaboration.

It’s no easy task: staying ahead requires adapting security practices across the full AI lifecycle, and the CIO’s role is to embed trust-by-design, break down silos, ensuring AI adoption is responsible and resilient.

In this article, we guide you through the 5 key areas CIOs must champion to deliver secure, trustworthy AI, drawing on learnings from the Wavestone 2025 AI Cyber Benchmark.

Read the full article

How do we drive effective AI adoption?

Many organizations rush into AI experimentation and deployment, only to see adoption stall. The reason? AI adoption is considered a tech challenge rather than a leadership one. Change management, communication, and culture are often after-thoughts.

In our article, our experts distil their guidance on how CIOs can drive lasting AI adoption by aligning people, processes, and governance; not just technology. You’ll discover:

How to tailor AI communication to different audiences across the product lifecycle.
Why AI literacy is now a strategic advantage and a regulatory requirement.
Ways to address cultural barriers like mistrust, scepticism, and change resistance.

Find out how to create the environment where AI can thrive, building trust and upskilling teams.

Read the full article

How do we move from AI Pilot to Production?

The question of why AI pilots stall, and never make it from a promising proof of concept to enterprise-scale adoption, is a critical one for technology leaders.

In our experience, organizations too often chase what’s possible rather than what’s valuable; only to discover late in the game that the data isn’t ready, the processes can’t scale, or governance gaps pose unacceptable risks.

The result? Delays, re-work, and wasted investment.

In this article, out experts outline the most common reasons pilots stall and provide ‘5 Levers to Industrialize AI’, a practical blueprint to turn prototypes into value-generating assets.

Read the full article

In the next few years, the credibility of technology leaders will hinge on their ability to build secure, scalable data foundations and leverage AI against high value use cases that deliver real business impact. The key challenge will be to operationalize AI safely, ethically, and at scale while balancing risks and reward.

Richard Graham, Partner, Wavestone

Helping clients transform with AI

Our Data & AI teams help clients across the AI lifecycle. Below are some recent examples.

Client Story

Life Sciences · Data & AI

Unlocking AI innovation with a scalable, compliant Data Operating Model

Your next steps

Our experts have built a variety of accelerators to help clients build and drive adoption of secure, scalable Data & AI capabilities:

Enterprise Data & AI Maturity Model

Helping clients define a roadmap for AI transformation, assessing current capabilities in data and AI across dimensions such as strategy, governance, technology, talent, and culture.

Data & AI Operating Model Archetypes

Helping clients design or refine their operating model for data and AI initiatives, ensuring it aligns with their business structure, culture, and strategic goals.

Use Case Library (with sector flavors)

Helps clients explore proven, high-impact data and AI use cases relevant to their industry, helping them identify opportunities and accelerate ideation.

AI Opportunity Prioritization Framework

Helps clients evaluate and prioritize AI initiatives based on critical parameters, such as business value, feasibility, risk, and strategic alignment.