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The potential of Artificial Intelligence continues to be discussed across organisations, yet it is concerning that despite all this discussion only 35%[1] of organisations know how they will drive value from it.

The term ‘AI transformation’ is one you will hear increasingly and is synonymous with offering a phenomenal chance to innovate and compete with new vigour. It offers previously unimaginable opportunities, and Microsoft is highly focused on the aim of placing a copilot on every desk, every device and across every role in support of the mission to empower every person and every organisation on the planet to achieve more.

Microsoft has identified four areas of opportunity for an organisation to drive their AI Transformation[2]:

Enrich employee experiences.
Reinvent customer engagement.
Reshape business processes.
Bend the curve on innovation.

Sizing the AI Transformation Opportunity

The promise of AI transformation is truly phenomenal. McKinsey have commented that “AI could add the equivalent of $2.6-4.4 trillion annually …. by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.”[3]

Automation is likely to play a key role in this with McKinsey suggesting that, boosted by Generative AI, 30% of hours worked could be automated by 2030.[4]

While these might seem fanciful because they are several years away, early results are hugely promising. It is also tempting to think of the value of AI Transformation lying solely in the saving of time – but this is only part of the story. Studies are already showing significant value being derived from not only reducing costs, but also increasing revenue and reducing risk through improved quality of decision making.

Highlights from key studies include benefits of:

Delivered 25% increase revenue through enhanced efficiency.[5]
Increased customer satisfaction by 12%[6]
Increased revenue growth by 4% through improved strategy and engagement[7]
Reduced costs of 10%[8]
Completion of tasks 25% faster[9]
Reduced total expenditure by 0.7%[10]
Reduced risk through a 40% improvement in quality of decisions[11]

Yet despite this early success and reasons to be optimistic, organisations are struggling and need help.

Trouble Ahead

Research shows that 60% of companies are looking specifically at Generative AI as a top priority – but only about 35% know how it will create value.[12] This is not good news for them and suggests there could be a danger that the focus and effort may not be as fruitful as anticipated.

This would not be the first type of transformation that organisations struggle with. McKinsey’s experience suggests that 70% of transformations fail[13] and there are few business leaders who would dispute this number.

The reasons for this are complex, but McKinsey suggests there are three root causes:

Failure to set fact-based aspirations. In other words, being unclear on ‘WHAT’ to focus on.
Failure to attach a ‘Why’. Teams need to buy into the transformation at both the ‘big picture’ and the individual initiative level. Having a clear ‘WHY’ is fundamental to attaining that buy-in.
Poor execution. While program management is critical to manage a meaningful change or transformation, working out ‘HOW’ to ensure success with such innovative technology can be daunting for even the most progressive of organisations.

In short, organisations are struggling with the ‘WHAT, ‘WHY’ and ‘HOW’ they need to ensure success with AI Transformation.

A Helping Hand?

What if there was a way to rapidly identify, validate, and prioritise opportunities which are aligned to business strategy and then use these to create a clear roadmap driving business value?

An approach that would deliver the ‘WHAT’, ‘WHY’ and ‘HOW’ for AI Transformation – specifically:

WHAT to start on.
WHY start there.
HOW to succeed.

This would provide you with a sound foundation for AI Transformation success and is an approach I have been involved in adopting at multiple customers.

Based on lessons learned through multiple engagements over the past two years an there is an approach that can be executed quickly, to help define elements necessary to underpin success in AI Transformation.

Rapidly Ideating on Value

The approach we have taken is primarily a workshop driven involving industry and technology specialists alongside business and technology focused teams from the customer organisation. It is divided into three parts – Industry Insights, Opportunity Identification, and Prioritisation.

Industry Insights. In this stage, a high-level understanding regarding concepts and capabilities related to AI and Generative AI is provided, alongside a perspective on relevant industry-specific use cases where the technology is delivering results. The outcome of this is to ensure a collective understanding of capabilities and context in preparation for the next stage.
Opportunity Identification. Facilitated using design thinking techniques, this stage involves the mapping of ideas and concepts learned about in the previous stage to opportunities in the organisation’s value chain. This can be the value chain for the whole organisation, or the value chain for a specific function or department. In organisations who have been through this process, this step has resulted in some unexpected opportunities for the technology to deliver value – ideas that would not have occurred without collaboration between the multiple parties in this manner.
Prioritisation. The final stage is to evaluate each of the concepts or ideas established in the prior stage based on effort versus impact. A remarkably simple and effective approach here can be to use an Impact/Effort matrix - first evaluating impact on the Y-axis and then considering effort on the X-axis. An example of such a matrix is shown below.

Once prioritisation is complete, other criteria can be considered. These criteria are typically mutually agreed beforehand but focus on such elements as alignment with corporate strategy and ensuring consideration of any other factors specific to the organisation.

Delivering Valuable Output

The outputs of the have defined the ‘WHAT’, ‘WHY’ and ‘HOW’ for AI Transformation and are highly valuable as a foundation for subsequent progress. The key outcomes include:

The priority initiative, or initiatives, which define ‘WHAT’ to start on.
A view of the value-drivers from where the benefit of the initiatives will be derived. This forms the basis of ‘WHY’ you should start there.
A ‘solution route’ and approach highlighting the technology needed to deliver the solution. This provides a key part of ‘HOW’ to succeed.

Priority Initiatives

The selected initiatives function as the foundation and are selected based on delivering maximum value for the organisation. These will align to one or more of the four AI opportunities: enrich employee experiences, reinvent customer engagement, reshape business process, or bend the curve on innovation.

Value Drivers

Getting a detailed picture of potential value at this stage is difficult, however the specific drivers, or levers, which will deliver benefit will be highlighted. This will expand beyond simply the saving of time – which is often the case for AI initiatives. Identifying how that time saved can be used to add additional business value will also be a priority.

Solution Route

With such a rapidly changing technology landscape it is easy to get lost, therefore defining clarity on the ‘solution route’ is paramount. This will define the most appropriate combination of technologies to be applied, to deliver the value case. As an example, Copilot Studio, Azure AI Studio and Power Automate, when combined together, form a very powerful combination to support copilot delivery especially where there are requirements around information-extraction, knowledge mining, and process orchestration.

There is no ‘one size that fits all’ so other approaches may be suggested.

Building on the Outputs

The approach here finds the right problem - or problems – to focus on, this lays a foundation for future stages.

A solution route provides a basis but defining the right solution is best done through the process of envisioning. Again, this can utilise a workshop driven approach – potentially spanning several sessions – involving all relevant customer stakeholders, to determine details of the solution to be delivered.

Architectural elements, interaction & integration points, value potential and data requirements are all typically evaluated alongside a perspective of the business process impact to build a value case.

An additional key output to complement the value case is a comprehensive view of the investment required and the way in which the delivery can be structured to maximise return on investment and deliver using an Agile delivery approach.

Delivery could be either a proof of concept (POC) or a minimum viable product (MVP). Whereas a POC demonstrates that an idea or use case is feasible through the delivery of a specific set of capabilities. It is used to illustrate and prove a concept and is usually self-contained in that it does not connect to live data or other systems. Consequently, it is of limited use beyond demonstrative purposes.

By contrast, an MVP is deployed into production and integrated into existing systems so it can offer immediate value to the business and end-user while requiring limited effort to deliver. It can therefore become a foundation for further development and enhancement adding capabilities based on prioritisation using Agile development principles.?

Driving AI Transformation Success

Determining the ‘WHAT, ‘WHY’ and ‘HOW’ is fundamental to success of any transformation and this approach has shown to be a powerful approach to deliver these for AI Transformation.

By rapidly identifying, validating, and prioritising opportunities aligned with business strategy, you will gain a clear roadmap for driving value. The three key elements of Industry Insights, Opportunity Identification, and Prioritisation ensure a comprehensive understanding of AI capabilities, mapping ideas to organisational value chains, and evaluating concepts based on effort versus impact. This approach not only helps in setting fact-based aspirations but also ensures buy-in from teams and effective execution, ultimately laying a robust foundation for successful AI Transformation.

Transform your business with AI solutions from Microsoft

Visit the Microsoft Industry Solutions website to learn more about how AI and generative AI capabilities are helping organisations across multiple industries transform. Learn about Microsoft’s commitment to making sure AI systems are developed responsibly and in ways that warrant people’s trust while keeping security at the forefront.