We are firmly in the fifth industrial revolution, a revolution driven by AI. First came mainframe computing, then personal computers in the 80s followed by the internet and then the agility and power of cloud computing in the 2010’s. This fifth wave of computing will be the most transformative yet, with capabilities promising to advance human endeavour and exploration.
However, there is a problem. Though AI is enterprise ready, many businesses have not been able to take full advantage of its promise. Innovation is happening so fast that enterprises just can’t keep up. In 2015, researchers published 10,000 scientific papers on AI. In 2019, that number had grown to 25,000 in the United States alone. This is worrying for many executives I speak to, especially given that good use of AI is projected to increase enterprise profits by 38% and will help deliver $14 trillion of gross added value to corporations by 2035.
Laggard firms would do well to learn from visionary AI organizations, then. In our research, we found those leading in AI put the technology at both the center of their business and operating models, using it to discover, democratize and further de-risk AI adoption. Such firms make AI a central part of their DNA so that they can deliver better products, increase customer sentiment and derive more value from their partner ecosystem. Conversely, laggard firms mainly use AI to increase efficiencies, rather than change the way the business makes money. To get a leg up on their journey, those firms that are behind should re-skill employees, ensure that AI adoption isn’t fragmented, develop strong ethical and governance frameworks, and nurture leadership in the risks and opportunities that serious AI adoption entails. Doing so can increase operating margins by as much as three percentage points. For a financial services firm operating with revenues of $10 billion, this means an added $300 million of additional revenue – no small figure.
With much of the best data science talent going to big tech firms, the ability to reskill the workforce sets leading AI financial services firms apart. Such firms use digital platforms and auto-modelling tools to ensure always-on learning is available. This is important. Now is the time for companies to train in-house talent – those people that know the business inside out and can spot impactful use cases for AI faster than any newcomer. At Infosys, we are leveraging our WingSpan education platform for just this purpose. Clients are given a roadmap to get their employees to full AI-readiness, with additional training sessions powered by Infosys Nia, an enterprise grade AI platform that simplifies the AI journey and industrializes AI deployments to accelerate business outcomes.
Reducing fragmented consumption of AI
Bringing teams together under one AI umbrella across the organization is also critical. In fact, getting AI operations right can increase operating margins by as much as six percentage points (that’s $600 million extra cash for a $10 billion revenue company). Those visionary firms that do so often ensure technology, finance and business teams work together on AI projects across the AI lifecycle, often in a Center of Excellence construct. Employees are trained faster, good change management processes are stitched together so that AI solutions move quickly from pilot to scale, and AI within the organization lives up to the hype. To deliver business outcomes and unlock intelligence at scale, developing a holistic corporate AI vision built upon enterprise-grade AI platforms is necessary. Further, leveraging visionary product, domain and consulting expertize ensures that the organization delivers exponential benefits over time.
Ethics and governance
Strong ethical and governance AI frameworks must be built from the very beginning. Doing so ensures that AI deployments are fair, equitable and unbiased. In our reading, those firms that have built such frameworks outperform laggard firms by as much as 25% on a number of business KPIs. However, getting data cleaned and swept of any unconscious bias isn’t easy. This is where having an organizational mandate for effective and explainable AI solutions come in. Employees who use AI, in both back office and customer facing roles, will need to be brought up to speed on ethical practices. Machine learning algorithms must be able to explain the decisions they made, in a way that is understandable by AI authorities, including regulators. Without this AI pillar in place, flawed AI ethics can destroy a financial company’s reputation in an instant – both with partners and consumers.
Finally, AI leaders must understand the technologies impact across a number of dimensions, including the business model, staff, partners, customers and society at large. Many leaders struggle in these areas, with just a fuzzy impression of how AI can help improve margins and keep them ahead of the competition. If 2020 is the year of AI, then 2020 also has to be the year when finance business leaders bootstrap their AI knowledge. At Infosys, we have developed training to bring business leaders up to speed across these areas.
With AI touching every aspect of our lives, the time is ripe for organizations to accelerate at full speed towards visionary AI status. From prescriptive AI used in working capital strategies and reduction of operational risk to natural language processing used in extracting sensitive contract attributes, innovative applications of AI are making financial services firms better, faster and more agile. To implement AI at scale, FS firms need to look after in-house talent, ensure ethics is front-and-center, and develop an enterprise-wide strategy for the technology across the value chain. In doing so, our clients will find themselves as heralds of the fifth industrial revolution.