AI Value Driven Model

Use AI to be more efficient, improve customer service, tighten cooperation with partners or other value driving use cases.
Move beyond isolated experiments and AI pilots. Our AI Value Driven Model helps you identify the right use cases, connect them to your business strategy and turn promising initiatives into solutions that create value over time.

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Start with value, not AI

AI initiatives are growing across organisations, but turning experimentation into measurable business value is a different challenge. Research from MIT's NANDA initiative found that 95% of enterprise generative AI pilots showed no measurable impact on P&L. 

Projects remain stuck in pilot mode. Initiatives emerge independently across departments. Employees gain access to new AI tools without the processes around them changing. And without the right data, governance or connection to business strategy, promising ideas can become difficult to scale.

 

The challenge isn't simply adopting AI. It's knowing where AI can make a real difference – and building what works to last.

Does this sound familiar?

  • AI initiatives are happening - but the value is unclear

    You've invested time and budget in AI, but struggle to connect the results to measurable business outcomes.

  • AI is growing across the organisation - without a common direction

    Different teams are experimenting independently, making initiatives difficult to govern, maintain and scale.

  • You see the potential - but don't know where to start

    There are countless AI possibilities, but it's unclear which use cases make sense for your organisation.

  • Data, security or existing ways of working are holding you back

    Your data, processes or requirements around privacy and control make it difficult to move from ambition to implementation.

From AI opportunity to business value

The AI Value Driven Model gives your organisation a structured way to identify where AI can create value, validate the most promising opportunities and turn successful initiatives into solutions that work in production.

IDENTIFY
Find business challenges and opportunities where AI could create meaningful value.

PRIORITISE
Focus on use cases with the strongest combination of business potential and realistic implementation.

VALIDATE
Start small and test the opportunity before committing to a larger investment.

IMPLEMENT
Bring successful initiatives into production and integrate them into the organisation.

SCALE
Develop and maintain what works so it can continue creating value over time.

The right AI isn't always an LLM

Generative AI and large language models have opened up new possibilities, but they are only part of the AI landscape.

The right technology should follow the use case - not the other way around. Depending on the problem you're trying to solve, another AI approach may provide greater value, better control or a more efficient solution. And technology choice also affects your data. Where information is processed, who has access to it and which providers are involved should be considered when evaluating an AI solution.

AI in hologram

A clearer path forward with AI

Prioritized use cases

Identify where AI has the strongest potential to support your business goals.

From idea to production

Move promising initiatives beyond experimentation and into solutions that can work in the real organisation.

Value that can scale

Build with the long term in mind, so successful initiatives can be maintained, developed and scaled over time.

Think big. Start small. Scale fast.

You don't need to transform everything at once. Start with the opportunities that matter, prove the value and scale the initiatives that work.

FREQUENTLY ASKED QUESTIONS

Start with the business challenge rather than the technology. The right AI use cases are those that connect to real organisational goals and have the potential to create measurable value. They should also be evaluated against factors such as available data, technical feasibility, security and the ability to implement and scale the solution.

Not every possible AI initiative deserves the same investment. Prioritisation helps organisations focus on opportunities with meaningful business potential while considering feasibility, data, risk and the resources required to bring the solution into production.

The business value of AI should be measured against the outcome the initiative is designed to improve. Depending on the use case, this could include reduced costs, time saved, increased revenue, improved quality, reduced risk or better customer experiences. Defining these measures before starting makes it easier to evaluate whether an initiative is worth scaling.

A successful technical experiment doesn't automatically become a successful business solution. AI initiatives can struggle to scale when they lack clear business ownership, access to the right data, integration with existing processes or a plan for how the solution will be operated and maintained over time.

Start with clear business objectives and success criteria, validate the opportunity on a manageable scale and plan for production requirements from the beginning. Data, integrations, security, governance, operation and maintenance all need to be considered if the solution is going to create value beyond the pilot.

No. LLMs are one type of AI technology and aren't necessarily the best solution for every use case. The technology should be selected based on the business problem, available data, requirements and desired outcome - rather than starting with a particular model.

Ready to move from AI ideas to business value?

Find the AI opportunities worth pursuing

Whether you're just getting started or already have AI initiatives across your organisation, we can help you identify where to focus, validate the opportunities and create a path towards production and measurable value.

Talk to Fredrik

Fredrik Svensson
Chief Business Development Officer