While news about AI projects and applications is dominated by digital giants like Google, AI has huge potential for pre-digital businesses. These businesses often own data sets collected over decades from their R&D and industrial processes and represent a real business opportunity. There is much to learn from the tech giants but trying to cut and paste their approach is a route to AI failure. Below I outline Tessella’s experiences on how to create value from AI for the data rich, pre-digital enterprise.
Always quantify your goals and success criteria
As with any new project, businesses must define clear measurable goals and KPIs for each new AI release. Has customer engagement increased, or the quality and efficiency of the production line improved? Businesses must use these proof points to demonstrate success. Whether this is to financial backers, or other stakeholders. Any success stories or failures should also be fed back into the company’s AI strategy. This will help ensure AI delivers.
Build AI momentum
Our experience shows that starting too big too early undermines effectiveness of AI projects. Companies should start by building a roadmap that identifies the business decisions that AI can inform best and focus on the well-understood opportunities that can be executed quickly. This will build critical momentum for additional AI programmes of work in-house.
Explore multiple AI projects in parallel
Accelerate by running multiple AI projects in parallel, which will help to ensure the best ideas are progressed rapidly. This agility is how ‘digital native’ companies deliver innovation, but many pre-digital organisations have failed to do the same. Rapid prioritising of resource into the delivery of the most successful ideas demonstrates an AI strategy focused on the realisation of tangible value within the business, which is crucial for building trust and support in the AI project.
But don’t be afraid to fail fast
AI expertise comes only from having many different experiences. Sampling widely and failing fast and early leads to a far better trained AI, dependent upon fewer misplaced human assumptions and more open to innovation. Agility in your approach to delivering AI is therefore crucial if you want your digital transformation programme to release business impact.
Mix people up – the importance of cross-functional teams
AI should be designed by people who understand the problem, the underlying data, and what it represents in a real-world context. This means the best teams will include representatives from IT, operations, business, as well as AI and data analytics experts. It is critical to include people who can translate between these different roles to ensure the technical expertise is present, but also that the team communicates effectively, and members involved are fully aware of the goals and the underlying business value.
AI is about talent as much as technology
All businesses should look to find the right people for the job, rather than simply relying on technology. Even a non-digital company can look to achieve this by investing in AI talent, which includes experience, problem-solving abilities and technical expertise, and combining that with a clear vision for the AI solution.
Look outside your organisation
Specialist AI skills rarely exist within pre-digital organisations. Seek them out externally and embed them within business teams. Don’t be afraid to look further than your industry as the same problem might have been solved in a different field. New thinking and fresh perspectives can lead to game-changing innovation and a competitive advantage.
User experience is key
AI interaction must be intuitive, or it will not be embraced by your user communities. Businesses can learn from the ‘digital natives’, the likes of Google and consider how to create better user experiences. Take Google Photos, built upon complex neural networks, image analysis, and natural language understanding. All the user needs to do is master a search bar and grid of their photos – no technical knowledge is required. Your AI implementation needs to be the same; simple, intuitive and natural to interact with.
Maintain expert oversight
AI is good at automating routine tasks but cannot deal with situations outside its training. To avoid AI failure, businesses must continue to routinely cross-reference AI decisions with human expertise and develop a contingency plan for human intervention when unexpected events occur.
Original source: Enterprise Management 360°