A leading global financial services company used Artificial Intelligence (AI) successfully to improve fraud detection by 25 percent. By using AI in its predictive maintenance routines, an energy company also has already begun to save millions of dollars annually per oil rig. Meanwhile, a leading insurance provider registered a USD 30 million increase in revenue and 20 percent improvement in conversion rate by implementing an AI-powered customer activity hub. There are numerous examples of substantial improvements in business results from AI infusion.
AI is slowly but surely becoming a fundamental capability for organizations. Thanks to fantastic breakthroughs and effective demonstrations of its successes, AI is not like other nascent technologies such as quantum computing or blockchain.
Every day, businesses are learning to infuse AI in everything that they do, from chatbots to neural networks to better customer experiences, create intelligent products, and to design smarter and faster business processes. Though the path to AI seems long and winding, with different tools and algorithms for each different application, the bottom-line would be how companies develop a fresh perspective and approach for every use case, and not adopt a plug-and-play approach.
A top biotech company was keen on improving health outcomes by staying in close contact with patients over phone to ascertain a drug’s efficacy. The usual method was to have a call center to call all of its registered patients and note their experiences and analyze the data. When the process was automated with the help of an AI layer to take notes and analyze phone recordings, the results were substantial. The automated note taking had become so accurate that it helped zero-in on patients with risks of non-compliance, and opened up opportunities for proactive intervention. More importantly, the company realized that AI not only helped them gain better, sharper and more powerful insights, but also taught the humans to include ‘more empathy’ in the way they talk to patients! The revised training methodology with these inputs improved health outcomes for patients and created greater job satisfaction for the managers.
The above example clearly indicates how a company can directly translate vision to value by using AI as the cement for the organization’s strategy. Incorporating AI into any business systems and processes is thus a conscientious journey unlike any other digital technology implementation. To genuinely tap the advantage of AI to achieve desirable business outcomes, here is a five-step process:
Educate: Understanding AI is the first step. It is not enough to have AI in your annual to-do lists. There should be concerted efforts to ensure that all team members understand what AI is, and what it is not. There is still widespread confusion across industries, and lackadaisical grasp of how AI fits business requirements. AI is practically a continuum of constantly improving tools and techniques that help solve business problems and improve processes. Teams should therefore grasp the fact that AI is merely a way of mimicking human intelligence by analyzing and acting on structured data generated by machines and applications.
Experiment: Open all minds. Fail fast. Learn faster. AI needs willingness and trust to pursue. Look before you leap! What are an organization’s expected outcomes, available ecosystems, and accepted tolerance levels? Experimentation in AI begins with holistic viewing of data and the willingness to go that extra mile with boldness, to find out whether it works to fix a persistent business issue.
Evaluate: Pilot first, pivot later. The pilot results have to be analyzed thoroughly. Today, companies the world over are figuring out the immense potential of AI by evaluating pilot projects. Leaders should ensure that all stakeholders agree to experiment with business-outcome-focused use cases. Successful pilots will then decide bigger implementations. Companies should aim to create nimble organizations with empowered stakeholders who ask the right questions for implementing AI.
Establish: The bang for the buck. Prioritize and establish which AI technology to pursue, backed by learnings from the pilot experiments. Move from ascertaining the business value of a certain AI technology to the actual feasibility of its implementation. Does the prospective AI system have enough bias-free data to learn patterns and arrive at solid decisions? The answers to such questions should determine the implementation.
Explore: Finally, where all can AI fit to derive maximum value? How can a team better organize itself around AI? What processes can be created to apply AI learnings to other parts of the organization? Such an open-ended exploration would also spring surprises on the way, and unlock the huge potential of AI.
At the end of the day, any business is, and always will be human. Companies wishing to implement AI should therefore not forget the attitude factor in driving AI adoption. To drive an effective AI culture, you need all of the following: Skilled teams, faster iterations of pilots, more learning, and above all, the human factor: The need to keep humans at the core of it all, earn their trust and respect, and the leadership’s commitment to continuously communicate about AI to all of its workforce.
A “We are in it together” spirit is vital for effective infusion of AI. Companies that succeed in instilling an empowered mindset and a positive attitude in their workforce to embrace change, and possess enough technological expertise to craft their own AI journeys would taste success sooner than their competition.
Poornima Ramaswamy, is the vice president of Cognizant, AI and Analytics.
Disclaimer: This article is published as part of the IDG Contributor Network. The views expressed in this article are solely those of the contributing authors and not of IDG Media and its editor(s).