Ankura Consulting Group has launched AI by Design, a UK-based practice serving EMEA that focuses on helping enterprises turn artificial intelligence investments into measurable business outcomes through AI strategy, governance engineering and workforce transformation.
Enterprise AI adoption is entering a phase where deploying a model is often only the beginning. Organizations now have to determine where AI can generate measurable value, how systems should be governed and how jobs and operating processes need to change around increasingly capable AI tools.
Ankura Consulting Group is targeting that implementation gap with the launch of Ankura, AI by Design, a new technology-agnostic practice in the UK serving clients across EMEA.
Rather than positioning the practice around a particular AI platform or model provider, Ankura says its approach is built around the organization’s existing strategy, workforce, customers, systems and operating model. The objective is to identify opportunities where AI can increase revenue, expand organizational capabilities and improve profitability.
The practice is organized around three areas: AI Strategy, AI Governance Engineering and AI Workforce Transformation.
AI Strategy focuses on identifying opportunities for top-line growth and helping boards and leadership teams determine where AI can contribute to business objectives. Governance Engineering is intended to address the controls required to deploy AI at scale, including making governance processes easier for employees to understand and operate.
The third pillar focuses on workforce transformation. Ankura plans to redesign roles, reskill employees and introduce human-centered change programs intended to embed AI into everyday operations.
That combination reflects an increasingly important shift in enterprise AI. The technical ability to access generative AI has become widespread, but organizations still face challenges integrating those systems into business processes, managing risk and determining how human responsibilities should change.
Ankura’s practice is supported by an internal AI Factory, which the company says builds, tests and deploys AI in real delivery environments. This is intended to connect advisory work with practical implementation rather than separating strategic recommendations from the engineering and operational work needed to deploy them.
The company is also building an external ecosystem around the practice. Ankura says it is working with Decoded on AI literacy and workforce reskilling, Seymour-Powell on human-centered design and Inference Group on AI engineering and delivery.
The model places Ankura in a growing enterprise AI services market alongside consulting firms, systems integrators and technology providers that are increasingly combining AI strategy with implementation and organizational change.
The governance component is particularly relevant as companies move from employee-facing copilots toward autonomous or semi-autonomous AI agents. Agentic systems can interact with business applications, access enterprise information and execute tasks, increasing the importance of permissions, oversight, auditability and accountability.
Ankura’s approach treats governance as an engineering and user-experience problem rather than simply a policy exercise. The company says AI governance should be intuitive and efficient for employees to operate, potentially reducing the friction between risk controls and day-to-day AI adoption.
Workforce redesign is similarly becoming a central component of enterprise AI deployment. The question is increasingly not simply whether a task can be automated, but how responsibilities should be divided between employees and AI systems, which skills workers need and where human judgment remains important.
This is also reflected in broader market research. McKinsey’s 2025 State of AI survey found that 88% of organizations regularly use AI in at least one business function, although most organizations had not yet begun scaling AI across the enterprise. The research identified workflow redesign as one of the practices associated with organizations reporting stronger AI outcomes.
Ankura’s launch is therefore aimed at a part of the AI stack that sits above the underlying models and infrastructure. Microsoft, Google, Amazon, Salesforce and other major technology vendors can supply AI platforms and foundation-model access, but enterprises still have to determine how those capabilities fit into their own organizations.
The practice’s Advisory Council is intended to add perspectives from technology, academia, governance and industry. Ankura says the council will provide an independent perspective on where AI is producing genuine value and inform the practice’s work across strategy, governance and workforce transformation.
For Ankura, the strategy also extends its existing capabilities in strategy, risk, technology and workforce transformation into AI-specific advisory services.
The launch comes as enterprise AI moves toward a more operational phase. Organizations that began with experimentation are now facing questions about adoption rates, business-case measurement, governance, employee skills and the redesign of processes around AI-enabled systems.
That makes the distinction between AI capability and AI implementation increasingly important. Access to advanced models can provide technical potential, but realizing that potential requires organizations to connect AI systems with business objectives, operating processes and human responsibilities.
Ankura, AI by Design is built around that organizational layer. Its success will ultimately depend on whether the strategy can translate into measurable improvements for clients, but the practice illustrates how enterprise AI services are expanding from technology deployment toward the broader challenge of making AI part of how organizations actually operate.
Market Landscape
Enterprise AI adoption is broad, but scaling remains difficult. McKinsey’s 2025 global survey found 88% of respondents reported regular AI use in at least one business function, while most organizations were still working toward enterprise-wide scaling.
The market is consequently expanding beyond model implementation into AI governance, workflow redesign, workforce transformation, AI literacy and agent management. Consulting firms and systems integrators are increasingly competing with AI platform providers for this implementation layer.
Ankura’s technology-agnostic model targets organizations that need to connect AI strategy with governance and organizational change rather than simply acquire another AI tool.
Top Insights
- Ankura’s new AI practice targets the organizational layer of enterprise AI, combining strategy, governance engineering and workforce transformation.
- The practice uses an internal AI Factory to connect advisory recommendations with building, testing and deploying AI in real business environments.
- AI governance is positioned as an operational engineering challenge, with emphasis on making controls easier for employees to understand and use.
- Workforce transformation focuses on redesigning roles and reskilling employees as AI becomes embedded across business processes.
- Ankura’s partner ecosystem combines AI literacy, human-centered design and AI engineering capabilities to support enterprise deployments.
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