Creating Superior Customer Experiences By Leveraging Data & AI
By Gregory Verlinden, AVP AI and Analytics
By intelligently combining AI technology and human science, businesses can reinvent their decision-making processes to excite customers and grow revenue.
Many missteps have been made with transformation initiatives over the past decade, partly because businesses struggle with data sourcing and data management. Data reliability is paramount to AI success. Moving forward, then, depends on data-driven digital transformations. The main difference between AI leaders and non-leaders is strategic methodology, so, this article (in part 3) closes with practical actions to achieve sustainable success with AI.
A Clear Trajectory
More than ever, AI is primed to deliver exceptional customer lifetime value. Cognizant research conducted during the COVID-19 outbreak confirms that intelligent decision-making is an area of high ROI. In examining our findings, a major difference between AI leaders and non-leaders emerges. It’s apparent that leading businesses embark on the AI path by improving internal functionalities. Then, as their data maturity grows, they turn their attention outward.
AI leaders recognize that value lies in carving out greater market share and developing new products and services. By improving internal functionalities before diving into outward facing projects, leading businesses are accelerating value-creation through modern decisioning. Conversely, we found that non-leading companies are mainly focused on increasing productivity, profitability, employee engagement and customer retention.
Clearly, the route taken by leading businesses offers large-scale payoffs. Companies seeking to leverage AI should be meticulous with systemizing their growth strategies. Cognizant strongly recommends the following protocols.
Establish Trust
Many people don’t fully understand what AI does, leading to misconceptions about the technology. Transparency is a large part of trust-building. Explain how the technology works, and communicate your ethics and monitoring processes (if you don’t yet have a governance structure in place, start there).
Preparing staff for profound changes in how they work is a marker of a purpose-based organization, and of being regarded in talent markets as an employer of choice. With growth in data maturity, companies begin to use AI within their entire ecosystem, partnerships included. Building trust is therefore of utmost importance.
Cultivate Collaboration
Companies undoubtedly need data and IT talent. Yet executives and other decision-makers must be included on AI projects, especially when it comes to defining a business case. Break down walls between functions. Eliminate separation between sales and customer service. Multidisciplinary teams can see an initiative from all angles, as they form a collective brain pool. Collaboration helps mitigate risk, since it keeps channels of communication between data and analytics staff open. Executives often cite access to AI skills as a key challenge. Partner with vendors that can fill those gaps.
Demonstrate Good Design
You want people to use your solution. Before building anything, center human beings in your design principles. Take into account how they will interact with and benefit from an experience. Will this add value to their day? If not, don’t spend resources on designing and building it. Beware of unintentional constraints — every decision should accelerate innovation, not stifle it. Digital slowdowns will occur from roadmaps that won’t fit every context. Proceed with caution when committing to applications.
The Promise of AI
While we are in the earliest stages of what will be a multi-year, or even a multi-decade, journey towards AI maturity, the technology is advancing significantly. AI capabilities have far surpassed searching through historical data to predict the future. Businesses can now perform highly complex, multivariate analyses to discover entirely new ways of producing and delivering value.
The race towards AI maturity is on. Businesses seeking to accelerate growth cannot afford complacency. At a global level, companies are investing in AI to improve productivity, profitability and customer satisfaction. Now is the time to look beyond pilots and prepare for wide-scale, revenue-generating AI deployments.
Along with establishing strong processes internally, businesses must recognize and address challenges with transitioning. Many companies still measure productivity in terms of output rather than outcome. The world is becoming more complex. Following yesterday’s delivery models will not achieve. tomorrow’s desired results.
Steadily progressing in AI maturity requires three key disciplines: understanding the business context in which real-time decisions will be made; realizing how to harmonize data sets with machine learning; and applying insights within a specified set of rules, particularly where pattern recognition is concerned. Companies in the Benelux region are making great strides in AI maturity. What can be learned from their successes?
AI in Benelux
We see two clear trends with companies in Benelux, the economic union comprising Belgium, the Netherlands and Luxembourg. One is that they are forging ahead with data modernization, which is integral to making intelligent decisions. The other is that these businesses invest in the right resources to monetize their data. They recognize the return on hiring multidisciplinary talent, and they grasp how to optimize platform-oriented processes.
Companies based in the Netherlands stand out for surpassing their European neighbours in AI maturity.
According to a survey we conducted in partnership with ESI Thought Lab, 36% of Dutch companies continue to advance with AI adoption, and 68% expect to mature further over the next three years.
“How can we do the next big thing?” clients often ask Cognizant. Businesses naturally want to capture value by innovating. With today’s volatility, it is virtually impossible to break new ground by forecasting alone. Businesses need to broaden their scope. Leading companies in Benelux, for example, perceive themselves as more than suppliers.
They are building their own data-driven, customer-centric marketplaces. Such a systemic approach to innovation allows companies to massively scale and produce. By investing in the right tools and processes, businesses in the Benelux are efficiently mitigating risk while ensuring transparent models and ethical standards.
On the Horizon
In the past, data was more afterthought than primary consideration. It is no surprise, then, that many AI experiments over the last decade have largely been marked by failure. Future business successes will be based on data-driven digital transformations. COVID-19 jolted businesses into further uncertainty. Yet AI presents countless opportunities to overcome challenges with intelligent data uses. Throughout 2021, companies must explore how to make a difference in the market, especially where possibilities for creating customer value comes in. Those that define strong business cases will gain serious ground in data maturity.
Leaning into the future of work also influences digital maturity. Businesses must enable non-data scientists with AI skills, and gather, integrate and format data for AI. We will witness a shift from larger hierarchical team structures to smaller teams. Repetitive tasks will be left to machines, while humans specialize in applying judgment, creativity and empathy.
In the next edition of this article, we will address how organizations are harnessing AI to leverage exemplary customer service with AI, businesses need to see the human side of individual customers. Follow-us on Medium and be one of the first to know. You can download the ebook from here.
