Despite the hype around Artificial Intelligence today, the concept of AI has been around for decades. The Logic Theorist, the first computer programme to replicate human problem solving skills, was introduced in the US back in 1955.
And yet, until recently we treated AI as the stuff of science fiction, referencing it in futuristic films or pitting computers against chess grandmasters to entertain ourselves.
This has all changed in the past few years. Artificial Intelligence has suddenly emerged as one of our most transformational technologies, with businesses in every industry investing in AI to get closer to customers and gain more control over their data.
This second point is crucial. The reason AI has finally hit the mainstream is that brands now collect so much data that they need intelligent software to help them make sense of this information. The digital age is relatively young, and even 10 years ago companies were barely collecting enough data to justify investing in AI or Machine-Learning algorithms. Today, businesses live and die by the billions of data points at their disposal and the insight they can draw from this information.
Computing power has also caught up to the promise of AI. It’s no small feat to analyse huge volumes of information in real-time, especially when it’s collected in various forms from an endless array of sources. Only recently have we developed technologies that allow us to manage all this data effectively and bring context to our decision-making.
Brands are feeling a real sense of urgency around AI. Research by Adobe reveals that 88% of companies are on track to use AI for customer or business analytics by 2020. This is an aggressive timeline for implementation, which shows businesses appreciate that they have entered a fourth industrial revolution where mass personalisation will be an important differentiator.
This sense of urgency is amplified by the success of disruptive companies such as Spotify (in the music space) or StichFix (in the fashion sector). Both companies are using AI to re-imagine the dynamic between customers and their offering, and turning two well-established industries on their head. Leading businesses are thinking more like subscription providers, putting people at the centre of their decision-making and delivering experiences in a way that promotes loyalty.
This raises three challenges for heritage players, who must find a way to innovate more quickly while also ensuring their approach is sustainable:
- Challenge 1: Having access to the RIGHT data, and the RIGHT to use that data
Brands need access to information that will tell them something valuable about customers, which is why many are taking back control of their data. The race for differentiation combined with a regulatory push for greater transparency into data practices will force brands to reconsider what data sets they really need and see ownership shift back in-house over the next few years.
- Challenge 2: Gaining a single customer view
Companies understand that a key step in making the most of AI is to integrate all their data onto a single central platform. After years of relying on disparate processes and systems, it has become clear these barriers need to come down if they want to develop complete customer view. The more data AI software has to work with, the more informed its analyses and the more accurate its recommendations for how to personalise experiences for individual customers.
- Challenge 3: Building an AI-centric skillset
Without the skills to work with data and AI, companies have little hope of succeeding. Brands need to build data literacy across their organisation so people understand what’s possible with AI and speak in a common language. Encouragingly, they are hiring and training staff in equal measure to close their skills gap. Data scientists will help with leading-edge technology needs, but it is the internal teams who best understand the business and what customers want.
AI is bringing about rapid change and will increasingly enhance the way we work. It’s not a case of people vs machines, as many have speculated. When done right, AI augments our potential. Take the healthcare sector, where it’s been found that machines are highly adept at identifying tumors in patient scans while humans are much better at determining whether these are cancerous. This is a win-win scenario, not only making diagnoses more efficient but also ensuring patients receive a higher quality of care.
The same is true in the world of marketing and advertising. AI helps brands innovate and work more efficiently, which in turn makes more space for greater creativity. This means they can deliver a better quality of experience to their customers, and continue to improve as people’s needs evolve. The race is on, and early adopters are already beginning to pull ahead.
Check out my blog to learn more about AI and Machine Learning and see how leading brands are adapting to this new industrial revolution, and check out Adobe’s AI readiness tool to see how you compare to your industry peers when it comes to being AI ready.