March 6, 2025
From Media and Entertainment to Healthcare and the Law, from synthetic special effects to automatically generated outreach emails, customer support chatbots and dynamic, personalised e-commerce sites, industries are full of ideas for applying generative AI to accelerate business transformation. These ideas are both visionary and easy to visualise. They fire the imaginations of business leaders, end-users and the media because they put generative AI in a starring role.
In our conversations with technology leaders we find most are dealing with already complex technology landscapes and in many cases AI is being proposed as a solution to cut through the complexity of legacy systems. However it’s important to ensure that a proper Use Case discovery is carried out and AI doesn’t become a solution looking for a problem and its use is aligned to business outcomes.
This article will provide an approach for considering how to experiment with AI or other technologies to accelerate transformation whilst remaining focused on delivering value
Many businesses that grow rapidly and responsively develop diverse, fragmented internal architectures that reflect their legacy of chasing new opportunities or testing new ideas. Other fast-growing clients have often followed an acquisition path to grow market share. Both paths can leave a legacy of both complex team structures and software systems.
Organisational fragmentation makes building more powerful and compelling product portfolios, creating cross-selling opportunities and enabling enhanced customer experiences harder. Incompatible systems get in the way of innovation. Customers churn, opportunities are missed, revenue suffers, and new ideas become too difficult to execute. The difficulties of digesting one acquisition or new product make future bold moves less likely.
Organisations across all sectors currently find themselves in a race to find the applications of AI that can most enhance their competitiveness. AI tools are now being strongly touted as a solution to complex legacy code bases that need integrating or refactoring. The promise is that AI can speed the process and unlock strategic business value at scale.
Keeping focused on the business outcome is the best way to identify the applications of AI that deliver real value and is the key to unlocking its transformative potential – whilst avoiding being drawn down expensive and time-consuming cul-de-sacs.
We believe the best way to realise this promise isn’t to throw AI at every task and problem. Going all-in on a technology that’s still emerging can be an expensive way of failing to deliver the end-result that people are looking for. It can also add further confusion and complexity to the tech estate.
The investment choices you make should initially be seen as calculated bets, not dead certs. Recognising that this is the case, and taking a strategic approach to how you place your bets, helps to make sure that you are investing time and resources in the right way.
Equal Experts has developed an approach that we call the Bet Canvas, which allows you to formulate each innovative idea as a bet. Each bet is based around a business improvement hypothesis. The hypothesis is aligned to a specific objective, generates quantifiable results that can prove or disprove it quickly, and involves clarity about what’s delivering those results.
The Bet Canvas approach is inherently empirical. It enables us to make decisions based on visible results rather than extensive planning and analysis. At the same time, we can place multiple bets in promising areas without going all-in too early on any of them. The canvas positions us to stop failed experiments quickly while doubling down on success. It’s biased for action and yet directed by strategic criteria, which make it ideally suited to the pressures involved in integration projects.
The value of the Bet Canvas starts with picking which hypotheses are worth pursuing. If you can’t describe the problem that your bet would solve and the metrics that would show you’ve solved it, then you need to consider a different bet. In the case of an integration project, this focuses attention on ideas that will contribute to the overall objective in a meaningful way. In most projects like this, that means delivering a single view of the customer across the tech estate and doing so with minimal interruption to the customer experience.
The Bet Canvas is particularly useful when it comes to planning a strategic approach to deploying AI, because it focuses attention on the solutions that you know will deliver valuable outcomes. The alternative is to start with an impressive-sounding tool, and then search around for a worthwhile job it can do. Plenty of organisations are discovering that this isn’t the most efficient approach.
The current state of AI models means that they are often best suited to summarising a lot of information very quickly, and synthesising what’s already in a database. Those are capabilities that can be valuably applied to many different points in the software development lifecycle, and so there are likely to be a range of different bets that you could place on the technology. The Bet Canvas exists to enable evidence-based decisions about which of these are delivering in the way that you expect.
Let’s look at testing and quality assurance, as an example. We could identify potential roles for AI in synthesising test data, writing test frameworks, analysing code and writing tests based on it, running the tests themselves, and more.
Using a Bet Canvas, we can identify three or four such ideas that we want to place bets on, including any AI solutions those ideas involve. We then define the impact that we expect each idea to have and the metrics that will tell us whether that impact is happening. We set a timeframe over which that outcome should materialise, and we place our bets. Once the relevant timeframe has elapsed, we commit to acting on the data in deciding which bets to expand, and which to call a halt to.
With all of the attention currently focused on AI, it’s tempting to assume that every bet we place should feature an AI component. That’s missing the bigger picture. The real opportunity is to compare AI investments to other types of solutions, and different types of problems. Through the canvas, you can analyse scientifically which are most impactful for your overall objective. This is why we often recommend that at least one bet doesn’t involve AI at all. Ideally, you would create a bet canvas with a mix of different types of innovations and engineering approaches. That’s the real opportunity for AI to prove its value to a business.
There are few more valuable projects than integrating different technology estates. It’s the type of work that creates lots of starring and supporting roles for different tools and innovations. To get the most from AI, we need to know with confidence which of these roles it best fits. The Bet Canvas is an ideal audition process.
If you’d like to talk to Equal Experts about building a bet canvas for streamlining technology infrastructure or any other major transformation project, get in touch with us here.
All business models have their pros and cons. But, when you consider the type of problems we help our clients to solve at Equal Experts, it’s worth thinking about the level of experience and the best consultancy approach to solve them.
If you’d like to find out more about working with us – get in touch. We’d love to hear from you.
