How to Build a Game-Changing AI Strategy 

How to Build a Game-Changing AI Strategy 

With the global AI market projected to reach $190.61 billion by 2025, growing at a seemingly unstoppable compound annual growth rate (CAGR) of 36.6%*, we all know that AI has the potential to revolutionise industries, streamline processes, and unlock unprecedented value.

But with one in three CIOs surveyed** saying that they’re accountable for AI within their organisations, and many of those approaching it cautiously, it’s challenging to know how best to approach an AI strategy in the most intelligent and responsible way.

How organisations are using AI in 2023

In the IBM Global AI Adoption index 2022, 35% of companies worldwide reported using Artificial Intelligence in their businesses, and an additional 42% reported they are exploring AI.

And in a hot-off-the-press survey of CIOs around the world by Info-Tech Research, artificial intelligence was cited as the number one technology for new spending planned in 2024. Drilling down further into that research, the fastest growing use cases for AI were:

  1. Automating repetitive, low-level tasks (39%);
  2. IT operations (38%);
  3. and conversational AI or virtual assistants (36%).

One in three of the CIOs surveyed said that they were currently either running AI pilots or were more advanced with deployment.

Artificial intelligence is therefore not just a planned activity or set of buzzwords, it’s clear that it’s already disrupted entire industries and is generating huge amounts of value for its early adopters. Amazon’s recommendation system, powered by AI, contributes to a remarkable 35% of its revenue***. Indeed, a study by Salesforce found that 61% of consumers are willing to share data with companies in exchange for personalised product recommendations, underlining the immense value of AI-driven personalisation in customer engagement.

Click the button below to download our full AI Analysis Report for more examples of how AI is currently being used by organisations in diverse industries around the world.

Identifying AI Opportunities and Creating an AI Strategy

So where do you start? Building an effective AI strategy begins with identifying the areas within your business where AI can enhance operations, improve efficiency, and drive innovation. To consistently and successfully achieve this, you should follow a structured process like this one:

  1. Understand the Technologies

Before diving into AI, it’s crucial to understand the various AI tools available and their specific strengths and limitations. A great way of gaining this understanding is to leverage the expertise of employees with domain knowledge and/or a willingness to learn about new technologies. 

These technologies usually have the most impact in “cognitive applications”, often in these three key areas:

  • Automating business processes (back-office administrative and financial activities)
  • Gaining insights through data analysis
  • Enhancing customer and employee engagement
  1. Prioritise Projects

Through workshops, technology assessments, and business analysis, create a prioritized portfolio of AI projects and use cases. Consider factors like potential impact, feasibility, and alignment with business goals. Useful outputs from these could be problem statement canvases leading to a prioritised map of pilot projects to prototype.

(i) Identify Opportunities

The next step is to determine which areas of your business could benefit most from AI; typically, areas where “knowledge”—insight derived from data analysis or a collection of texts—is at a premium but for some reason is not available:

  • If the lack of cognitive insights is caused by a bottleneck in the flow of information; so knowledge exists in the organization, but it is not optimally distributed. That’s often the case in health care, for example, where knowledge tends to be siloed within practices, departments, or academic medical centres.

  • If knowledge exists, but the process for using it takes too long or is expensive to scale. An example of this is the knowledge developed by financial advisers. That’s why many investment and wealth management firms now offer AI-supported advice capabilities that provide clients with cost-effective guidance for routine financial issues.

  • If an organisation collects more data than its existing human or computer firepower can adequately analyse and apply. For example, a company may have massive amounts of data on consumers’ digital behaviour but lack insight about what it means or how it can be strategically applied.

You should also review peer case studies and contextualise them to generate ideas within your organisation.

(ii) Determine Use Cases

Identify then evaluate potential use cases using the Desirability, Feasibility, Viability (DFV) framework*. Ask critical questions about the:

  • D – Problem’s significance (does anyone really want it?)
  • F – Technical feasibility (can we do it well?)
  • V – Potential return on investment (will it make money?).

Another useful idea evaluation framework is to generate problem statements for each use case, asking questions with key stakeholders about the ideal solution, current challenges, process changes, performance metrics, and data elements involved.

Consider prioritising use cases on a 2×2 graph of benefit vs effort.

The innovation sweet spot is in the centre of desirability, viability and feasibility

(iii) Select the Right Technology

Choose AI tools that are fit for purpose for each identified use case. Ensure that the selected technologies align with your business objectives, whether incremental or transformative change.

  1. Prototype With Pilots

Launch proof-of-concept pilots that can be holistically controlled, before integrating into wider parts of the business too early. Remap workflows and business processes to incorporate the new functionality and be realistic about the division between human and AI labour.

If looking at multiple projects, consider creating a cognitive centre of excellence to manage AI pilots. This approach helps build necessary technology skills within the organisation and facilitates the transition from small-scale pilots to broader applications.

  1. Scale Up

Scaling AI technologies often involves integrating them with existing systems and processes, requiring a high level of cooperation between tech experts and business process owners. Focus on improving productivity and achieving growth, not just efficiency gains.

It’s always helpful to see these processes working in practice, so we’ve mapped it onto a construction company’s AI discovery journey. You can download it below along with our AI Analysis Report.

You can probably tell that this is a subject we could write endless posts about (without Chat GPT of course!), but do visit some of the resources below to support your AI strategy.

If you need to find budget to support these initiatives, look at our recent post about converting technical debt and IT optimisation to fund innovation and AI  


ImprovIT are an independent business and technology consultancy. We were founded by former colleagues of Gartner, IBM and HP to help senior IT leaders make the critical decisions that will maximise their technology investments. We’re completely independent and impartial specialists in the use of IT Measurement, Modelling and Benchmarking.
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