As someone who has been working with Artificial Intelligence for nearly ten years, it’s amazing to find so much interest and excitement for this technology all around us. It feels like AI has hit the mainstream and it’s easy to understand why. Neatly packaged and made into products anyone can interact with, tools like ChatGPT and DALL-E represent huge technical advancements in AI. This has pushed AI to the top of the agenda for CTOs and CIOs, as well as CEOs. But amidst all the hype, I wonder if the real practical applications of AI are truly understood and if businesses are moving fast enough to adopt them?
The case for action: AI will be ubiquitous
Because my kids are exposed to and use the internet every day I doubt they will turn to me with the question “What is the internet?” For them, the Internet is not a thing they can or need to easily conceptualise but instead is ever present and constant, like the air we breathe. My kids see me use my phone to add items to the grocery order, they turn the lights on with their voices and they watch family photos whizz across a display as if every moment we live is captured and replayed automatically. For them and indeed for most of us, internet connectivity is ubiquitous.
It’s been nearly 30 years since the ‘web’ was invented and 16 years since the iphone was launched. We’re now at the precipice of a technology revolution that will similarly become ubiquitous, ever present and constant, and in a few short years will defy easy categorization or explanation. That technology is Artificial Intelligence (AI) and it is transforming the world around us. As the internet has, over time AI will fundamentally change how we live and work. It will make us more productive and efficient while ushering in a generation (or three!) of innovation in the form of not just technology but new business models, ways of learning, communicating, and sharing information.
In the future, only businesses with AI at the heart of their operations will be successful.
Organisations that don’t start building an AI adoption strategy today will be relegated to the history books along with once-iconic brands like Kodak and Blockbuster that missed the digital revolution. More important than simply adopting AI is the need for it to be customised for each business. Every business will need its own AI, specific to its unique business model, processes and culture.
Putting your data to use – it’s easier than you think!
Every AI journey should start with an assessment of the opportunities and weaknesses of your business. It’s only from this vantage point that you can see which areas should be prioritised and know what AI solutions might fit. Rather than tinkering with AI for the sake of it, consider the business outcomes you are trying to achieve and identify AI use cases that can add real top-line growth or bottom-line value. Such an opportunity may fall within the scope of much-hyped generative AI but more than likely your best bet for getting value from AI will be to apply it to aspects of your day-to-day operations such as how you manage inventory, customer data or pricing.
Many businesses assume the next step on their AI journey is to modernise their data infrastructure. Data has been the beating heart of most businesses for a very long time. As far back as the days of pen and paper recordkeeping, business people have poured over sales transactions and customer records looking for patterns that could help them be more successful. As technology made the storage and retrieval of vast amounts of data easier and faster, the term “big data” took hold, with countless articles encouraging technology leaders to invest in capturing data so it could be mined for value. Now it’s estimated we collectively produce 2.5 quintillion bytes of data every day! The challenge is no longer about having lots of data, but rather about having the right data and putting it to use for competitive advantage.
Peter Thiel describes the potential for data to drive competitive advantage brilliantly in his book, Zero to One. To paraphrase, companies that harness data to gain a competitive advantage create a flywheel effect, where their ability to service customers is enhanced. This leads to them winning more customers, generating more and more data as they grow, which, in turn, reinforces their advantage. This data network effect can be used to capture the majority of a market’s economics (profit) and see companies go from – you guessed it – zero to one.
With this in mind you may assume your data quality is too poor to be AI-ready. But data infrastructure projects can be costly and take years and, with AI competition heating up, most business can’t wait years. The good news is you don’t have to. Your data doesn’t have to be perfectly organized in a data warehouse to be AI-ready. There is a common misconception that data has to be perfectly formed, de-dupe’d, and pristine to get value from AI but it’s actually the opposite: AI is the perfect way to start working with anything from a single dataset to a fully-featured data warehouse.
Taking the first step with AI
Putting artificial intelligence at the heart of a business is the next chapter of the data revolution. Rather than passing data from team to team over the course of months, AI allows unparalleled leaps in efficiency and productivity by converting raw data into better decisions, and allowing those decisions to feed each other. Over time, the decisions and the outcomes they drive will improve over time creating an AI flywheel. The ability to leverage data into a flywheel will separate the haves and the have-nots.
The problem at the moment is that most enterprise software – from customer relationship management, to warehouse management, to supply chain optimisation – is not designed to use data in this way. These systems are often silo’d and lack any true predictive or prescriptive decision-making capability. AI changes this relationship with software and systems. It can allow diverse systems to communicate, and for them to each become key inputs that ultimately feed a central hub for insights and connected decision-making. That hub can be accessible to all teams and to other systems simultaneously. This way of working has the potential to expose immediate clarity, pace of action and visibility of outcomes across the organization.
You can start small with pre-built AI applications, and add use cases over time. For instance, many businesses get started with AI-powered demand forecasting or customer segmentation. From there, they add additional AI capabilities like AI-driven recommendation tools, pricing optimization or more sophisticated inventory management.
The really cool thing is each application enhances the others, and combines into a single connected AI that levels up every part of their business. To do this well requires commitment to both technology infrastructure and new ways of thinking and working. There’s a real opportunity to democratise AI, allowing organisations of all shapes and sizes to harness this emerging technology and build an AI at the core of their operations. AI will ultimately power decision making at every stage of a business’ value chain.
No doubt about it, it’s a journey and there will be ups and downs. But it’s critical to take the first step and start thinking about that first AI use case. Jump in head first before your competition beats you to it.