AI, machine learning, big data â€“ is your head spinning from these topics popping up everywhere, yet you know too well you should â€śdo something with itâ€ť? To support business leaders who want to get started with data, but struggle with the â€śhow, what and whereâ€ť, we developed a second edition of our well-received masterclass â€śMake Disruption Workâ€ťÂ â€“ one that centers on exactly these topics.
The worldwide lockdowns due to the Corona virus have caused a huge surge in digital adoption which is believed to be permanent, thereby quickly increasing the need to accelerate AI capabilities to support those digital platforms. With a decade of experience in effectively digitizing companies, we as SparkOptimus see it as our duty to support as many businesses as we can in facing these unprecedented times. One of these supporting initiatives is hosting a condensed virtual version of our full-day AI masterclass once every two weeks, to accommodate as many of our (potential) clients as possible. In this 2.5-hour crash course, our data team discusses four key themes:
We kick-off this masterclass by explaining how the fundamental driver of disruption is always fulfillment of unmet customer needs. Over the last decade, weâ€™ve all seen how the universal shift towards digital led to the first wave of disruption. With digital channels serving as a platform for AI services, AI technologies are now kickstarting the next wave of disruption. AI-driven digital business can therefore be seen as the next evolution of digital business. While an online fashion retailer used to be on par with an easily browsable assortment and quick home delivery, the world of AI enables â€“ and will eventually require â€“ AI-driven product and size recommendations, and AI-driven fulfillment and packaging. In this first chapter, we discuss how data is relevant for any business â€“ also yours.
Next, we explain how in an AI-driven world, value moves from asset-first companies to customer-data-first companies. This is why Booking.com forced Hilton to invest massively in D2C, and Netflix left Disney no option but to launch its own streaming platform. The main driver of this value shift is that data can be leveraged to optimize relevance â€“ and thereby provide more value â€“ to the customer. The good news is that you donâ€™t have to be Amazon to do this! We discuss a four-step-approach to get you started.
The possibilities of data seem endless, but the fancy yet confusing terms used to describe the associated concepts can sound like black magic spells. Luckily, working with data is a lot less complex than the buzzwords might suggest. In this part of the masterclass, we demystify the terminology around AI and discuss practical business applications. The easiest way to unravel the seemingly limitless range of AI applications is by realizing that AI automates decision-making, just like IT automates manual labor. As a result, your business decisions become better, faster, and cheaper through an algorithm that is scalable, self-learning and consistent. For example, an algorithm that counts the people entering your store â€“ in order to adhere to Corona measures â€“ frees up human brainpower to interact with your clientele.
As the offer of off-the-shelve solutions like these continues to grow, data services are becoming a commodity, and taking steps towards becoming an AI-driven business can be reduced to strategic implementation of existing solutions.
Ultimately, all companies will need become AI-driven. In this final chapter, we look at the steps you need to take to get there and derive value from data. We discuss the biggest pitfalls and how to avoid these. For example, setting up a team of developers in a basement somewhere â€“ as many businesses do just to â€śget started with AIâ€ť â€“ is not a recipe for success. Data must leave the basement to become fully adopted in daily operations and have impact. We conclude this virtual AI masterclass by sharing important doâ€™s and donâ€™ts when successfully implementing data strategies, based on our work with over 100 companies.
SparkOptimus supports businesses in building this new AI-driven competitive advantage with a three-step-approach:
Interested in working together to become an AI-driven digital business? Please donâ€™t hesitate to get in touch with us!
The history of retail is, in one sense, the history of data technologies. From the ancient clay tablets first used to tally grain, to standardised account ledgers, to digitised computer spreadsheets; commerce has driven the development of technologies that enable the collection and use of data. And as each new technology has emerged, businesses have adopted it across the board.
There are two important points to draw from this:
AI is the most recent data technology in this lineage, and somewhat similar patterns can be expected. The biggest risk businesses face today however, in relation to AI, is thinking of it as somehow separate, or worse, still not relevant to their main operations. This attitude can be characterised as, â€śOh, AI is that misty thing that does self-driving cars and robots that fall in love.â€ť It is in fact much broader and far less mysterious.
Itâ€™s about serving customers
AI actually follows naturally from digitisation. Digital processes have massively increased the scale, speed and availability of data that businesses collect, and this in turn has created the need for automated processes to be able to use all this new data, and generate better decisions. The decisions are themselves commonly the ones that businesses were already dealing with â€” albeit suboptimally â€” and the AI improvements are neither rabbit-out-of-the-hat solutions, nor futuristic products, but specific, tangible, business-positive optimisations. These optimisations feedback into the business, and ultimately to the customer, because being better, faster and cheaper for the customer is always how data-enabled businesses outcompete the rest.
To sketch a few specifics, AI may enable a business to be:
In this, the impact AI is having now, is analogous to that of â€śonlineâ€ť was ten to twenty years ago. Then the shift was from physical stores to omnichannel sales, but was likewise driven by customers taking advantage of new ways to get served better, faster and cheaper. This time the shift is an internal one, as businesses move from manual, judgement-led decisions to more data-driven, automated ones, but with the same essential effects. And similarly, the shift will apply across all retail sectors â€” not just tech â€” and be at least equally disruptive, because better-faster-cheaper are areas in which all businesses can improve and out-compete each other.
Making AI work for you
Companies that adopt AI successfully always take the question â€śHow can we serve our customer better, faster and cheaper?â€ť as their starting point for continuous improvement of their current business. They target a selection of the KPIs they are already using (as these are material to their business), deploy AI on the relevant data to generate better decisions, and â€” importantly â€” they define, quantify and monitor success. For example: Company A may decide it wants to reduce customer waiting times. Having assessed that each minute saved is worth â‚¬1,000, it invests â‚¬2m in AI software to optimise decisions, and achieves a saving of 3,000 minutes, thus yielding â‚¬3m in value and so a net gain of â‚¬1m. Or Company B may target customer conversion rates, and using AI to identify the most promising leads, achieves a conversion improvement of x% on y euros of product to yield a value of z.
So the key question for businesses looking to adopt AI therefore becomes, â€śHow can we serve our customer better, faster and cheaper, and which KPIs are relevant to do so?â€ť. This is a much less misty problem than, â€śShould we create a brand new AI department?â€ť or even, â€śWhat should we ask our brand new AI department to do?â€ť But it is hard nonetheless, and this is where SparkOptimus, as Europeâ€™s leading consultancy with a 100% digital focus, can help. We work with boards and company leaders to understand their businesses, and consider potential AI applications in two ways. Firstly, we look at the customer needs and the processes to fulfill these. We map out where decisions are being made, and which ones are amendable to AI optimisation. We can then find the right software for the job, select the right KPIs to monitor progress, and calculate costs against anticipated, trackable benefits. And secondly, we look beyond â€” to other companies in the same sector, as well as other sectors where AI adoption is more advanced, and ask of both: where and what is AI being used to do, what results is it yielding, what barriers is it breaking, at what kind of cost, and what are the operational implications?
Business leaders need to embrace a more data-driven way of working, and be willing to implement the changes the data indicates. At the same time, when selecting where to focus, and how to quantify improvements, businesses have to ask themselves on a deep level what customer needs they really serve, what value they bring, and indeed what kind of business they want to be. They might also ask, given the history of non-adopters of new data technologies, whether it is even a choice. Instead, the real question is not if, but how to make AI work for you, which is exactly what SparkOptimus will help discover and implement.
Interested in working together? Have a look at our Advanced Data Analytics/AI service line and get in touch!