AI and technology driven transformation | Nordic Capital
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AI and technology driven transformation

Across Nordic Capital's portfolio, AI is changing how companies compete, how work gets done and where value is created. The companies capturing the greatest value are treating AI as a shift in their operating model, not a technology project. Those moving furthest and fastest are seeing stronger topline growth, a more efficient cost base and an expanded addressable market.

Nordic Capital works as an active partner in this process. We work with boards and management teams to develop and implement AI strategies across the portfolio, providing frameworks, proven use cases, expert resources and a peer community that help accelerate adoption, build lasting capability and avoid common failure patterns.

Selected results from Nordic Capital’s portfolio companies, 2024 to June 2026

+107%

developer productivity increase at a Nordic Capital banking software platform company

+74%

new ARR per quota rep increase at a Nordic Capital financial technology company

5-15%

cloud cost savings through AI-enabled optimisation across Nordic Capital portfolio companies

How we drive success together

No two companies are at the same stage in their AI journey. Some are deploying their first use cases; others are scaling proven workflows and beginning to rebuild core processes around AI. Nordic Capital’s starting point is always an assessment of current maturity, benchmarked across the portfolio, followed by a co-developed AI strategy that identifies the highest-impact opportunities, sets measurable targets and builds the governance foundations that turn ambition into results.

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.

The goal is to build the internal capability and momentum that makes AI transformation self-sustaining. Nordic Capital’s role is to support, accelerate and challenge management teams, not to run AI programmes on behalf of our companies.

The journey from tools to transformation

Nordic Capital puts AI transformation on the board agenda from day one of ownership and supports portfolio companies through the full journey, from first use cases to operating model change. Our experience points to a consistent pattern where adoption moves through three stages, and the decisions made in earlier stages shape whether later steps become easier or harder to reach.

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.

AI in action

The most valuable AI transformations across our portfolio share a common thread. The leadership teams behind them treated AI as a fundamental question of how their business creates value, not as a technology initiative to be managed separately. Three of those stories are below.

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