The Chief Digital Officer of a prominent retailer was on the phone with me catching up after his visit to our labs along with his business leaders. He sounded excited. “We are ready to dip our toes into this AI stuff,” he said. He had two big business problems to solve – How to dramatically automate his customer service operations and make it more customer centric? And then, how to make his customer targeting more precise and effective? Now these were both areas with lots of historical data, clearly identifiable hypotheses and sponsorship from business leaders. This has not been the only time I have received such a request; he is not alone in his desire to take advantage of Artificial Intelligence (AI) on behalf of his enterprise to drive purposeful outcomes.
AI is here in the enterprise
Another innovation leader from a large CPG company is making machine learning a strong basis for executing on their company strategy. The first few problems were identified using a design thinking workshop – one of them included the ability to better predict the outcomes of clinical testing for new skincare product launches. A large industrial company with fairly complex labor contracts is working with Infosys leveraging natural language programming to digitize their labor contracts and create a much more user-friendly and accurate HR and employee experience in complex circumstances. A large energy major is leveraging machine learning to predict fraud in their purchasing processes which are significant and material for them. A global fashion major is working with a few of my more inspired team members to pinpoint and predict the specific factory, bill of materials and the cost that will be involved to build a new piece of apparel, compressing or crashing traditional concept-to-shelf apparel cycle times. We at Infosys are in the middle of at least 60 exciting machine learning projects with the top enterprises around the world, across their business and IT landscapes, attempting to solve the toughest challenges they have. AI has significantly dominated the conversation in recent times, but now it is becoming a mainstream strategy with several early projects in practically all enterprises we work with.
AI is Hard
So did the prominent retailer or the CPG company or the energy giant solve their problems with AI? Yes, and no. Some problems have clearly been easy to identify and solve. Others have required more sophisticated approaches. For example, in the instance where we are crashing apparel industry cycle times the treatment needs a lot more contextualization – we need machine learning experts steeped in the company’s unique business model, unique problems and unique data/KPI reporting structures for the effort to be truly effective. Finding the right data from the right data sources has sometimes taken weeks instead of days. Most large global organizations also have “data empires” guarded fiercely by different functional owners, all passionately trying to drive great optimization within their silos. Having the right clear thinking business leaders working with machine learning engineers asking the right questions, driving the right hypothesis is always tough. The accuracy of any machine learning algorithm even after some work is usually 60-70% in the first pass – this has to be followed by fine tuning and further work. But most of our clients are getting there. They realize that this is an important and powerful tool and initial problems will likely be tougher to find and solve. Like for anything new in the enterprise they often face teething problems when introducing a new AI system, but these aren’t anything that can’t be solved with the steadying influence of a seasoned partner.
AI is easy
The good news is that almost all exercises that we have experienced have been quick. For every use case, each iteration has been delivered under 12 weeks thanks to some great thinking by our Infosys Nia™ architects who can today ingest data from practically any source and drive quick intelligent patterns, knowledge ontologies, insights, decisions, and then automate the actions needed. So what this means is corporations don’t have to “wait and watch” until AI technology becomes mainstream, possibly after months and years. They can take advantage of AI now. The learnings, the early failures, the likely successes are all happening now – in a matter of weeks and days. This approach to AI taken by Infosys has been the posterchild illustration of failing fast and cheap.
AI is ultimately human
So where lies the magic? It’s not the technology alone. Yes, Infosys Nia makes it easier because it is a unified, flexible, modular platform with wide-ranging capabilities. But the use of such a platform can only be completely successful when it is combined with the right actions, and in the right environment, led by dynamic leadership. It’s the business leader finding that new problem to solve (hint: use design thinking), it’s about asking the right question. It’s about driving a culture that allows access to different types of data, driving a culture of cleaner data, and driving the right hypothesis. It’s about being patient with the outcomes and iterating till we make the prediction, the insight, truly useful. These human acts of patience, of constant experimentation, of curiosity and of creativity are perhaps needed in far greater measure with AI than with other projects. AI is ultimately more human than we have thought it to be.
What experiences are you seeking with AI? What are the use cases that have you concerned today? Come join the conversation here or simply reach out.