INSIGHT

AI in the Data-Driven Enterprise

The character of the digital enterprise is distinctly different from one that is not. Where the traditional organization employs technology mainly within its operational core, the digital one takes it to its outer frontiers with the primary aim of creating consumer delight. It does this by maximizing the potential of its data by discovering nascent opportunities, hidden risks, emerging customer expectations and competitive moves. These insights are gathered with agility and ease, and within context, thereby enabling the enterprise to respond quickly and on target. As the digital organization is increasingly driven by data, so are its decisions and actions.

If data is the lifeblood of the digital enterprise, Artificial Intelligence (AI) technology is its pumping heart. AI, especially its subsets including machine learning, deep learning and advanced analytics, can automate much of the insight gathering and decision making in a data-driven enterprise, and amplify the value of data many times over.

But merely bolting on the latest AI solutions does not make a data-driven enterprise. Most incumbents need to take several measures before they are ready for transformation. Unfortunately, they face many obstacles, including, but not limited to, an inflexible core trapped in legacy technology, outmoded processes, and shortage of digital skills.

However, given the right approach and partner, successful transformation is within the grasp of every organization. Infosys has helped many clients traverse this journey using the following strategy:

With a goal of shifting the client away from a conventional use case or point solution-led approach towards enabling them to monetize data on an industrialized scale, we start by drawing up an opportunity blueprint for creating value through data. From there, we chart a roadmap for building the capabilities required to realize the opportunities listed in the blueprint. Broadly, the roadmap calls for:

  • Modernizing the enterprise’s core of legacy systems so it can undergo digital transformation
  • Establishing intelligent, cognitive systems that discover hidden or unknown signals and correlations in data
  • Employing Artificial Intelligence technologies to create a learning, adaptable organization that evolves very rapidly

Here is a quick explanation of each:

Modernizing the enterprise core: There is a wealth of data and insight trapped in silos within the organization’s legacy core that must be freed to create a flexible suite of foundational services. This foundation can be broken into several components, dynamically organized, and automated to deliver against an evolving context. But to do that, the legacy systems at the enterprise core must first be modernized or divested. This is what we did in a retail mortgage bank to enable them to generate credit scores for prospects, and process applications in real-time. Specifically, we reengineered the bank’s credit acquisition decision engine and transformed their legacy mainframe to build agility, after which they could generate applicant credit scores in under 50 milliseconds.

Establishing intelligent, cognitive systems: Once the data is freed from the legacy core, it is on to digitizing the data supply chain for cognitive interpretation and for making data-driven decisions throughout the organization. We have helped many retailing businesses make sense of their vast structured and unstructured data pertaining to consumer actions, market response to campaigns, customer requirements, pricing considerations and so on. Here, machine learning models were key to evolving recommendation logic to promote products in real-time.

Employing AI technologies: This step involves leveraging a number of AI models to almost entirely automate the resolution of business problems. This allows for continuous learning and continuous improvement to be factored into both validated and new models. A financial services client is experiencing this in real life. The organization uses AI techniques to detect inconsistencies and other issues in data values and transaction volumes that might be an indication of suspicious activity, and alerts decision makers if need be. It also studies these data patterns to predict and prevent unfavorable events.

The above approach helps organizations weave a critical data fabric that informs and drives decisions, as well as provides information access to all parts of the enterprise. Armed with the right data, as and when they need it, employees become empowered to achieve unprecedented results. Data allows them to see insights that were hitherto invisible, discover new problems, build innovative solutions and take their innate creativity to new highs.

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