The AI revolution is here and is rapidly transforming both business and personal ecosystems. AI has become commonplace across many aspects that we interact with every day. The power of AI is not just changing the way businesses function, but even how governments operate. As organizations continue to make AI a strategic priority, the last few years have seen the adoption of AI growing exponentially. Gartner estimates that the enterprise AI adoption grew by over 270% in the last 4 years. Today, AI is a key boardroom topic. There are many ways how AI is becoming a competitive advantage for organizations across the value chain — foreseeing and unifying the omnichannel customer journey for superior experience or predicting the needs across a supply chain to optimize inventory and maximize revenue.
In one of my recent discussions with a few financial sector CXOs, one of them remarked that the first wave of digital delight is over. While the context was from a banking viewpoint, that statement rings true across industries. Consumers have embraced the digital revolution and have become tech-savvy digital natives with higher expectations. Hence, organizations need AI — now more than ever — to be digitally competitive.
Organizations today are using more data than ever before. They are looking to transform these data-points into insights and business opportunities to stay ahead of their competitors. As AI enables organizations to understand data, enhance processes, improve decision-making and create better offerings, the opportunities this unfolds are limitless. Yet, they find it tough to scale their AI initiatives. A recent study from PwC states that 73% of organizations are still in the planning/experimentation phase in their AI projects.
Implementing and scaling AI has many practical challenges — here are three barriers in scaling AI across the enterprise:
AI has demonstrated the potential to address business pain-points and is helping organizations transform to create new business and revenue models. AI has matured significantly and is offering plug-and-play solutions that can offer tangible, real-time business outcomes.
A leading bank in the US wanted to modernize its collection process to reduce delinquency rates. We suggested a ready-to-use AI product that not only improved process efficiencies but also made it intelligent by effectively mapping customer segments to customize collection strategies. The result was not just a reduction in delinquency rates, but also a significant enhancement in customer experience. This enabled the bank to kick-start their AI journey in less than 3 months.
Retail is another sector being transformed by AI since there is a significant flow of data across the demand value chain. AI provides a competitive advantage by contextualizing external business data and making it insight-ready, thereby providing varying go-to-market scenarios that help retail enterprises scale by adding new distributors, improving retail execution and reaching new markets faster. There are many such examples of AI powering transformation in areas like fraud detection, contracts analysis, and procurement effectiveness across industry sectors.
The next phase of business transformation through AI will be when there is visibility into customer journeys across value chains and stitching the insights together. This can power the ability of an enterprise to predict future outcomes, highlight likely issues and suggest suitable actions. Businesses must accelerate their AI strategy and leverage it for business growth.
AI is enabling organizations to work better and is fast becoming a competitive advantage for organizations that are able to leverage it effectively. Here are a few ways to embrace AI:
Rapid experimentation for AI innovation – AI should be viewed as a tool with the power to solve business pain-points that seemed unsolvable in the past. This calls for an agile way of experimenting on the many enterprise use-cases that can help initiate the AI journey.
Scale across the enterprise – It is imperative to have a clear enterprise-wide AI strategy. The success of this strategy will depend on identifying the right areas for AI to scale and stimulate growth.
Identify the right technology building blocks – With a focus on solving specific business problems, AI technology should be easy to deploy and use. A buy-and-build approach, a robust enterprise-grade AI platform, and plug-and-play business applications have enabled organizations to roll out strategies in a faster and effective manner.
I believe that through a series of experiments and innovations, AI will continue to grow as a competitive advantage for organizations. Success will depend on leadership vision, AI strategy and a sustained commitment to getting it right.
AI has truly arrived and is presenting businesses with endless new possibilities to leap ahead!