The majority of organisations are bursting with all kinds of innovative ideas.
Yet—more often than not in my experience—they have no reliable method for deciding which ideas are worth pursuing and then turn them into valuable business outcomes.
In this blog, I’m going to explore how we can redress this imbalance through the idea of the digital incubator: a reliable and repeatable method for turning these ideas into concrete value.
A digital incubator (DI) is a methodology for rapidly testing hypothetical solutions to key business problems and opportunities with minimal investment of time and resources.
The core enterprise problem is that with so many business ideas (and so many ways of executing each one), businesses get caught in a kind of analysis paralysis.
They find it very difficult to go through the process of determining which of these many ideas are valuable and feasible. And they lack a reliable methodology for testing the hypotheses in a structured way.
The purpose of a DI is to cut through that paralysis, providing a repeatable method for turning all those vague ideas into concrete hypotheses and then systematically testing them—rapidly and cost-effectively.
We regularly take our clients through the Mesh-AI Digital Incubation process to help them kickstart their digital innovation process.
There are three major steps:
In the bootstrap phase, our goal is to gather the longlist of business ideas and opportunities and determine which opportunities hit the sweet spot of value and feasibility to take further into the process.
After gathering the main ideas from key stakeholders, we use value mapping to determine which ideas have the most business potential and then cross-reference that with the feasibility. We ask whether the required data exists, whether we can connect the data sets together and so on.
We then whittle down the list to around three ideas to take into the next phase.
In this phase, we dive more deeply into the value and feasibility of the business opportunities, making predictions about their ROI.
Specifically, we work with data engineers and data scientists to dive deep into the available data, exploring what insights we might be able to unlock in order to make the business opportunity maximally valuable.
We examine internal data quality, volume and potential third party data sources candidates and see how they could be combined to unlock more opportunities to reduce cost, increase profits etc.
This phase is all about the unbelievable power of bringing data sources together to turbo-charge insights: that is the true enabler of business value!
Two weeks are spent evaluating the potential impact of each of our three business opportunities before presenting our findings back to the business.
As part of our Design Sprint playback to client we outline the investment required, it’s potential to reimagine the way and organisation operates and ROI payback periods using low-, medium- and high-return scenarios.
This information is critical to supporting the client's decision to proceed to our lighthouse phase: building out a minimal viable product (MVP) to further validate the hypothesis.
Once the most valuable business opportunity has been identified, we bring it to life with a 16-week lighthouse project—creating an initial MVP that we take to production.
The purpose is to further prove the value and feasibility of the use case with minimal upfront investment, in order to provide a mandate to scale it further (if it works) or to fail fast (if it doesn’t).
In this phase, we are asking ourselves: what is the least we can do to learn the most?
In other words, what is the core functionality we can develop in 16 weeks that will show an organisation that if they continue along this path they will be rewarded?
But we’re not just building a product. At this stage, we are also thinking ahead to the longer-term roadmap. In what ways can we use the changes and learnings from the MVP to reinvent parts of the client’s organisation?
The obvious reasons for going on such a journey are that you are able to develop new products that will be valuable to the business.
But there are plenty of broader and deeper reasons that go beyond the immediate benefit of the new digital application.
Here are the main benefits of setting off on the digital incubator journey:
Do you need to know whether an idea will be either valuable, feasible or—ideally —both? Or which of several different solutions will be the most appropriate for your business?
A digital incubator is a fantastic process for rapidly prioritising and testing whether a given technology or approach is right for your needs.
The Mesh-AI team are—above all—change agents.
The actual MVP that results from the incubation process isn’t the point, as such, but rather what we learn from building the MVP about how the business needs to change to align more closely with value creation.
In this way, the digital incubator process doesn’t just incubate products, but transformation!
Most businesses try to learn everything about their circumstances before making any moves.
At Mesh-AI, we only learn as much as we need to get to the next stage. We build risk and assumption mapping into the process so we can move forward at speed.
In this way, we learn to thrive in uncertainty and can be agile in uncertain conditions.
The power of AI is all about making predictions and recommendations in conjunction with the experience of the business leaders.
We don’t use the design sprint process to produce facts, but rather predictions that can then be tested and tweaked in the lighthouse project phase.
This enables a key shift towards becoming an AI-assisted business that is able to combine human and machine intelligence to optimise business decision-making.
Many organisations have been collecting data for a long time. But they aren’t sure what to do with it and they aren’t set up to take the leap into AI and ML.
There’s a hesitation due to the uncertainty and the high level of skills and financial investment.
We help them to overcome that fear factor by using the digital incubator process to help them bring their models to production, taking them from siloed data science teams to commercialised AI products that drive real organisational outcomes.
We help our clients to go on the journey of taking their creativity, insights and ideas and rapidly transforming them into feasible opportunities using product techniques.
This is not only a rapid, low-cost way of making high-quality business decisions, but also of deriving a whole range of broader insights about how you can rethink how you work at a fundamental level.
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