AI and technology driven transformation

Strategic and implementation support

AI maturity assessment and strategy

Quarterly benchmarking across the portfolio, followed by a co-developed AI strategy covering use case prioritisation, board-approved targets and governance design.

AI accelerators and expert network

Senior AI advisors, functional specialists and forward-deployed engineers who work directly with management teams to move from strategy to implementation.

Leadership capability and transformation governance

The AI Adept Leadership Framework helps boards and management teams assess readiness, prioritise investments and guide AI transformation. Informed by observed Traits, Behaviours and Results across the portfolio, the framework supports leaders in building the capability and focus required for sustained transformation.

Portfolio resources and community

Use case library and AI product catalogue

50+ proven use cases spanning all functions, drawn from successful portfolio implementations, with tooling recommendations, impact benchmarks and deployment guidance.

Change, training and peer community

Modular training from basic prompting to agentic workflows. Over 100 technology and product leaders sharing experience through dedicated summits, webinars and working groups.

Stage 1 - Tools

Foundation AI tools are deployed for productivity, including chat assistants, coding assistants and knowledge tools. Most of the organisation has access, and individual productivity gains are real.

Acceleration at this stage depends on individual adoption, however, and nothing material moves at the EBITDA line.

Stage 2 - Use cases

This is where most companies are today. Funded initiatives are established in two or three functions, with clear metrics and named business owners. Some early wins can be tracked to outcomes, demonstrating the potential impact of AI adoption.

While the impact is real, it often remains siloed, with each team building its own context layer. Delivery also continues to scale with headcount.

Stage 3 - Operating system

Work is re-engineered around AI, with agents handling both routine and complex tasks autonomously. Output scales independently of headcount, and governance is embedded in the architecture.

Reaching this stage depends heavily on the architecture decisions made in Stages 1 and 2, which either build toward this outcome or make it harder to achieve.

As a healthcare data leader, Arcadia used AI to turn its biggest operational challenge into its most powerful competitive advantage.

Background

Arcadia is the data platform of choice for over 160 leading US healthcare organisations, serving providers, payers and life sciences companies. With over USD 132 million in subscription ARR and a strong track record of retention, Arcadia’s platform is well established in healthcare data management.

Situation

Arcadia’s leadership recognised a clear challenge: as the platform scaled, the complexity of customer environments grew with it. Keeping pace with the technical support demands of a growing enterprise base while maintaining the quality and responsiveness customers expected required a fundamentally different model.

Approach

CEO Michael Meucci led the transformation personally. Working with a focused task force of 20 of Arcadia’s most experienced technical specialists, the team built Aria, an AI agent with deep knowledge of Arcadia’s platform architecture, customer environments and resolution history. Every interaction was routed through Aria from day one, creating a self-reinforcing system where each resolution made the next one faster and more accurate.

Result

Within 15 days, Aria was resolving complex technical issues at a 97 percent success rate. These were issues that had previously required multi-team coordination over days. The transformation freed Arcadia’s most experienced specialists to build deeper customer relationships and deliver value proactively. The technical operations function has evolved from a reactive support model into a consultative, forward-deployed capability.

As a global banking software leader, Zafin moved beyond AI tools to build an AI operating model, doubling its engineering output in the process.

Background

Zafin is a leading banking software platform trusted by financial institutions worldwide to modernise their product and pricing infrastructure. A pioneer in enterprise AI adoption, Zafin gave 98 percent of its workforce access to AI tools already from 2022, one of the earliest and most comprehensive deployments of its kind in the financial technology sector.

Situation

By 2025, Zafin had reached a point that many AI-enabled companies will recognise. The tools were embedded and the gains were real, but growth still scaled with headcount. The next competitive step would require rebuilding the operating model itself, making AI the primary delivery mechanism rather than an enabler of the existing one.

Approach

Zafin committed to an AI-native operating model across its three highest-impact functions. In engineering, the team adopted fully agentic development workflows, moving from AI-assisted coding to AI-led delivery. In sales, Gong-powered AI coaching transformed every rep’s workflow with real-time deal intelligence. In sustainability, the ZaRA custom AI assistant turned a time-intensive RFP compliance process into a near-instant one.

Result

Engineering output doubled. Sales productivity reached levels that had previously required significantly more headcount to achieve. Zafin’s sustainability team reclaimed hundreds of hours annually for higher-value work. Zafin has demonstrated that the transition from AI tools to an AI operating model is replicable, measurable and directly linked to business performance, a pattern now informing AI strategy across the Nordic Capital portfolio.

As a leader in digital accessibility, Siteimprove used AI to expand well beyond its original product category, redefining what its platform can do for customers.

Background

Siteimprove is a global digital experience platform helping thousands of enterprise organisations manage web accessibility, SEO, content quality and digital performance. Trusted by leading brands across every sector, Siteimprove has built a reputation for turning complex digital compliance and optimisation challenges into manageable, measurable programmes.

Situation

Siteimprove recognised that AI represented both a competitive challenge and a strategic opportunity. Its leadership chose to use the moment to expand what its platform could do, rather than simply defend its existing position, reaching customer segments it had not previously been able to serve.

Approach

Siteimprove launched a comprehensive agentic platform in August 2025, embedding AI agents directly into the product to perform strategic advisory work, including audits, prioritisation, content optimisation and AI search visibility, that had previously sat outside the software. Selected to present at AWS re:Invent in November 2025, it was recognised as a leading example of enterprise agentic AI.

Result

Win rates rose by 16 percentage points as sales teams focused on higher-quality opportunities. The agentic platform has repositioned Siteimprove from an accessibility and analytics tool into a comprehensive content intelligence platform, opening up a substantially larger addressable market and attracting a wider range of enterprise buyers.