A people-centric framework for the successful integration of artificial intelligence.

Successful AI initiatives start with a sound strategy

Artificial intelligence, and in particular generative AI, provide significant opportunities for businesses. However, unlike other advancements in technology, this new technology also raises ethical and cultural challenges, as well as new risks, governance and regulatory considerations. 

Here at Fifty One Degrees, we believe that developing a well-considered, people-centric strategy for AI adoption will greatly enhance the success of your initiatives. As an AI Consultant, we will produce your strategy, roadmap and policy for you; an example of our work is below.

How to write an AI strategy

Artificial Intelligence Strategy

For this example AI strategy, roadmap and policy, we have created a fictional client, called River Bank. River Bank is a mid-market UK bank, that has a culture of striving to deliver outstanding customer experience to its consumer, business and vehicle finance customers. There are 2,000 team members across several offices, and it has interest-bearing assets of £7bn, revenue of approx £1bn and makes a reasonable profit. It has grown organically but also by acquisition, and therefore has a complex and, in places, outdated technology stack, making product releases difficult.

River Bank intends to complete a comprehensive Generative AI integration within 12 months, including a full operational-level deployment (see below for more information on this). Your initial Generative AI strategy might be narrower, but we believe you must have a well-considered strategy, irrespective of how broad it is.

How to create an AI roadmap

Artificial Intelligence Roadmap

How to write an AI policy

Artificial Intelligence Policy

Strategic AI considerations

When creating a strategy and roadmap for generative AI, there are several strategic questions to consider. You don’t need to answer them all on day one, but it is worth giving them thought.

1. Where do I start with AI?

This is an understandable question that we are asked regularly. Generative AI can be deployed in countless ways, big and small, across every function and every team. So where do you start? 

In the most simple terms, we recommend the following process:

  1. Plan – Create an AI strategy, roadmap and policy.
  2. Launch – Launch pilot or Proof of Concept (PoC) generative AI initiatives using MVP generative AI infrastructure.
  3. Scale – Scale initiatives across the organisation while simultaneously expanding generative AI infrastructure.


However, we believe the adoption of AI will have similarities to the adoption of the internet over the past 25 years, and so the reality is you will cycle through these steps again and again. This mindset is important, as knowing you will repeat the steps makes it easier to get on and complete your first iteration. 

2. Which generative AI use case should be my first PoC?

There are now many fantastic examples of deploying generative AI to make businesses more efficient and effective. When considering your first PoC, we believe it is worth considering two well-publicised use case examples.

Klarna’s AI Customer Assistant
Generative AI in financial services

  • The AI assistant now manages two-thirds of all customer service interactions for Klarna, handling 2.3 million conversations in its first month.
  • It achieved performance comparable to 700 full-time human agents and matched human agents in customer satisfaction scores.
  • Significant efficiency improvements were noted, with a 25% reduction in repeat customer inquiries and a decrease in resolution times from 11 minutes to under 2 minutes.
  • The assistant supports more than 35 languages and is available 24/7 across 23 different markets.
  • It’s projected to contribute $40 million USD in profit improvements for Klarna in 2024.

Moderna’s Internal AI Assistants
Generative AI in operations

  • Moderna has significantly increased the productivity of its team members by giving them generative AI tools to support their work. 
  • Partnering with OpenAI to integrate AI technologies into Moderna’s operations, Moderna has launched its version of ChatGPT, known as mChat, built on OpenAI’s API. 
  • The key statistics are compelling:
    • 750 custom Assistants built across the company within 2 months.
    • 40% of weekly active users created their own Assistants.
    • Each user has 120 mChat conversations per week on average (that’s 3 per hour!).
    • Various business functions are using mChat, including legal, research, manufacturing, marketing and business development.

These two approaches are very different. Klarna’s approach was to take one major function and use generative AI to automate two-thirds of it. Whereas, Moderna’s was to democratise generative AI across their business and encourage team members to use the tools to improve their work and increase their productivity, resulting in 750 incremental improvements rather than one big one.

Both of these approaches have merits, but at Fifty One Degrees we’re advocates of starting with Moderna’s, where firms start by using generative to solve many small challenges, as opposed to one major challenge. We recommend this approach for several reasons:

  1. Minimise deployment cycle – It is significantly faster to deploy, therefore shortening the feedback loop cycle and reducing the investment required for your first PoC. 
  2. Empower your team – It encourages a people-centric approach to generative AI, as your team is empowered by the use of the tools, as opposed to being replaced by them.
  3. Reduce risk and increase success – For the reasons above, it significantly reduces the risk and increases the chance of success for your initial AI projects.

3. How deep can I integrate generative AI technology?

To maximise the benefit of generative AI technologies, you will need to empower your team to use them to their fullest. In turn, this increases the investment required by firms to enable enterprise features, such as:

  • Automated prompt and response testing
  • Programmatic measurement of response quality 
  • QA functionality
  • Role permissions 


The impact of generative AI will increase exponentially with depth of integration, so firms should aim for full ‘Operational Level Deployment’. For your initial AI projects, though, you will likely stop at ‘High-level Deployment’.

Operational Level Deployment gives any team member the tools required to build their own automation and Assistants, whereas with ‘High-level Deployment’ the team responsible for AI will scope use cases, deploy solutions and monitor performance. 

Fifty One Degrees - Generative AI Deployment Level

Discuss AI strategy with our team.

Frequently asked questions

What is an AI strategy in business?

An AI strategy in business outlines the systematic plan to implement artificial intelligence to boost operational capabilities, competitive edge, and market adaptiveness. This strategy details the integration of AI technologies to optimise processes, enhance customer interactions, and innovate product offerings, ensuring these elements align with the broader corporate goals and values.

What are the key components of a successful AI strategy?

Essential to any successful AI strategy are a clear vision and well-defined objectives that align with the organization’s overall mission. This involves integrating ethical AI practices, thorough technology assessments to identify and address infrastructural gaps, and robust measurement frameworks to track the effectiveness of AI initiatives against set performance indicators.

How can I align my AI strategy with business goals?

AI strategies are specifically designed to support and enhance business goals such as enhancing operational efficiency, improving customer engagement, and fostering innovation. By aligning AI objectives with these goals, businesses can ensure that their AI initiatives not only add value but also propel the company towards long-term success.

What ethical considerations are involved in deploying AI?

Deploying AI involves critical ethical considerations to ensure that the technology is used responsibly. These include ensuring transparency in AI processes, maintaining accountability for AI-driven decisions, safeguarding privacy and data protection, and actively managing biases to promote fairness and non-discrimination in AI applications.

What training is required for employees in an AI-enhanced working environment?

Comprehensive AI training for employees encompasses basic understanding of AI principles, ethical considerations, and hands-on interaction with AI tools relevant to their roles. For those in more technical or decision-making positions, advanced training in data analysis, system maintenance, and troubleshooting is crucial to maximise the benefits of AI integration.

How does an AI strategy benefit from a technological assessment?

A technological assessment in an AI strategy identifies existing capabilities and gaps, providing a roadmap for infrastructure enhancements necessary for effective AI implementation. This assessment helps tailor AI solutions to the organisation’s specific needs, ensuring optimal integration of AI technologies into existing systems and maximising the strategic benefits of AI investments.

What should I include in my AI roadmap?

Your AI roadmap should include a phased implementation plan that outlines capability building, knowledge enhancement, and structured deployment of AI technologies. It should detail the establishment of an AI leadership team, talent acquisition strategies, data infrastructure assessments, and initial AI training programs. Additionally, the roadmap should prioritise AI projects that align with strategic goals, assess technical feasibility, and evaluate risk, ensuring ongoing assessment and adaptation based on the AI’s impact on operations and customer satisfaction.

What does my AI policy need to cover?

An effective AI policy should govern the development, deployment, and management of AI technologies across your organisation. It needs to outline the purpose of AI use, its scope within the company, and the responsibilities of various stakeholders. The policy should include standards for ethical AI use, ensuring transparency, accountability, fairness, privacy, and security. Additionally, it must address data handling practices, external partnerships, and compliance with regulatory requirements, while setting up mechanisms for monitoring, incident management, and disciplinary actions in case of policy violations.

Ready to adopt AI? Check out our guides