When it comes to developing a strategy for AI implementation there seems to be a lot of rabbits in a lot of headlights right now. How do you make sense of all the potential that AI brings to so many areas of the business? How do you understand where the real value is? The myriad possibilities are almost paralysing to consider.
I don’t claim to have all the answers to these pretty significant questions (unlike some of the LinkedIn gurus) but there’s a really useful way of thinking about this which has worked well for me in my work with leadership teams. Simplistic value vs impact frameworks are usually not that helpful. Value to who? What does impact actually mean? How do you even know how to measure these things?
But there is one model which I think is particularly useful in helping us to make better strategic decisions about AI, and avoid some of the pitfalls and assumptions that often get embedded without us even realising it. The Desirability, Viability, Feasibility (DVF) framework has been around since the early 2000s. Popularised by IDEO, it was conceived as a filter or lens through which new product ideas, services, or features could be evaluated, and whether they are worth pursuing. It was born from Design Thinking and I think part of the reason I like it so much in the context of AI is that it applies a human-centred approach to solving potentially big problems and evaluating value. It aligns business and customer needs.
Desirability – is it a solution to a human problem that is worth solving?
Desirability can relate to solutions for employees as much as it does to services for customers. AI is most effective when it augments human decision-making, or enhances a user experience, or alleviates a pain point that people actually care about. It guards against developing AI solutions simply because the technology exists, without a clear understanding of the purpose and user benefit of the idea. This becomes crucial for adoption and acceptance.
Questions here may relate to whether the solution addresses a real need, or whether it aligns with values, or meets and exceeds expectations, or (in the case for product innovation) whether there is a market for it. It’s very easy to get carried away with the potential benefits of AI to the business but if we don’t work back from real customer needs or real employee needs the risk of baking in assumptions and being ‘dangerously efficient’ are significant. Maybe AI isn’t the right solution to this challenge at all. As an example of this I once ran a session with a leadership team where the exam question they wanted to answer was how they could apply AI to enhance a particularly important customer journey. The trouble is that in framing the question in that way they were already making an assumption that AI was the answer. Maybe it is but equally, maybe it’s not. Desirability forces a focus on the relevance to humans, whether they be staff, customers or key suppliers.
Viability – does it create or protect real value?
Viability examines both direct returns (revenue, efficiency) and strategic returns (competitive advantage, risk reduction, learning). This considers what the business will gain. It asks the question about whether the AI initiative is aligned to business goals, profitability and potential, and whether it’s sustainable to maintain and improve. Viability can compound over time, so it may be important to consider longer-term strategic benefits and not just short term efficiency gains.
Another example…working with another leadership team in a recent session some of the team began to talk about the different ways in which AI could enhance their customer experience and the CEO made the (very good) point that before starting to originate ideas about AI the team actually needed to align around what their vision was for customer experience. Again, it’s very easy to do things with AI that look and sound great but which are not generating real value for the business.
Feasibility – can we build and scale it successfully?
Feasibility looks at technical and operational aspects, considering not just whether the tech can be built, but whether it should be built given the current picture of talent, skills, data infrastructure, technology. Questions focus on capabilities, resources, technical maturity, timing, constraints, scalability, build, buy or bolt on.
DVF is rooted in an understanding of how it benefits users, not just the business. It can be applied at multiple stages of a design, innovation or strategic process to reevaluate, realign and refine. Early on, it helps filter broad concepts to inform a strategy, and later it can assess the value of more specific use cases or detailed opportunities. It provides a structured way to move beyond the hype and looks at the opportunities from multiple angles, not just the efficiences or benefits that AI can bring to the business. It can be an excellent way to develop a common understanding of value, supporting better cross-functional working, less competing agendas, and greater agility. It stops AI being a solution in search of a problem.
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