AI has been around long enough that it should no longer be considered novel, yet some companies still struggle to unlock value from it. The problem? Many organizations treat AI as a technology, as a simple tool that can be applied to isolated problems to predict a stated set of outcomes, or to automate processes as point solutions, or to reduce costs. There is clearly some value in that approach, but not enough given AI’s vast potential.
Instead, companies need to stop thinking about AI as a technology and start thinking about how they can leverage AI to create an “intelligent enterprise,” one that is empowered to create new kinds of customer interactions and unlock new revenue streams. In other words, the “I” in AI is critical.
The Evolution of Enterprise Computing
In the 1990s, enterprise technology mainly focused on computing. The 2000s represented an explosion in terms of data. The 2010s went one step further by extracting insights from that data for specific applications, largely through analytics. In the 2020s, we will experience the era of intelligence, a time when insights are woven together to crate a fabric that unites functions and business units to not only reduce costs, but also to create new value.
Moving forward, AI will improve performance by functioning less like a computer and more like the human brain. A computer can sift through a set of data and highly differences, discrepancies, and other patterns. Contrast that to the human brain, which recognizes patterns, recommends actions or responses, and — critically — learns over time. That kind of intelligence is where companies are moving today. When business intelligence incorporates information decisions, changing behaviors and interactions, and learning, it is a capability not in isolated applications, but across the entire enterprise.
Viewed through this prism, intelligence fostered by AI is a means of connecting with customers in a different manner, creating new digital experiences that unlock new frontiers of growth. The lighting industry offers a good example. Rather than sell bulbs in the traditional manner, the industry is moving into offering “light” as a service. The infrastructure would be maintained by the company, and it would use intelligence arising from consumer data and IoT devices to determine consumer needs and preferences at a granular level (e.g. the lighting required based on time of day or environmental conditions, or the number of people using it).
Similarly, manufacturers in the aerospace industry have moved from selling jet engines to selling jet-engine “service” based on how long the engines are running. This is enabled by real-time operational intelligence data from embedded sensors in each engine that also monitor engine performance, how it can be improved, upcoming repairs, and other factors.
To be clear, the “as-a-service” business model is not new, but enterprises can better apply this model if they have intelligence about customer needs and operational performance, enabling them to dynamically tailor their offerings in response to changing markets. As a result, the service model can be applied across a wider range of industries, unlocking new revenue streams.
Four Levers for Leadership Teams to Address
Given the scope of potential transformation from becoming an intelligent enterprise, AI needs to move beyond the realm of the CIO and cost reductions to a key strategic consideration for the entire C-suite and Board.
Specifically, leadership teams should look at the impact from AI — the value being generated by operating as an intelligent enterprise — across four levers:
Rather than addressing these elements one by one, the C-suite must look at all four simultaneously. Companies also need to assign a numerical score to all four areas, combining them into a single number that we call the enterprise intelligence quotient (E-IQ). Only in that way can leaders get a clear sense of how the company is using AI to create new business value, and how it is improving in this area over time.
The goal is to use AI to develop intelligence that enables companies to interact with the world much like people interacting with their own environment. An intelligent enterprise can gather information from internal and external sources, then use that information to frame decisions, take action, assess results, and learn over time.
For example, corporate finance functions have led the way in terms of implementing AI, with the goal of generating more-accurate financial forecasts. The next step is to move beyond predictions to leverage AI to shape business performance. That entails pulling data from a wider range of sources — not just internal, but also external information such as market trends, regulatory changes, competitors’ performance, and other inputs — then on an ongoing basis determining whether the company’s strategy is optimal for the environment and ecosystem in which it operates. The finance function can propose a specific action, such as shifting production from one line to another, or reallocating resources to a fast-growing market, then gauge the impact of that action to improve over time. Such an adaptive approach has become a particular necessity in the wake of the pandemic, but it requires real enterprise intelligence.
Other clear use cases involve reimagining any processes involving paper (such as compliance), voice input (call centers), or IoT data (manufacturing facilities and logistics fleets). Still others are domain-specific applications for individual industries: financial services, health care, aerospace, consumer goods, and so on.
These wide-ranging use cases have one aspect in common: they look beyond AI as a technology and instead use intelligence as an underlying fabric to actively improve business results. That represents a big step forward for many organizations, but one that is necessary to create a truly intelligent enterprise.
VP of Intelligent Enterprise and AI, Wipro Digital
Mukund and his teams collaborate with leading companies across industries to deliver experiences aligned to emerging digital behaviors, achieve competitive business insights, and drive efficiencies by leveraging technologies such as Artificial Intelligence, Machine Learning, RPA, and Big Data. He has more than 21 years of experience in these areas.