Editorial
Lost amid the weaknesses the pandemic has laid bare is a much bigger and longer-term story: the strengths it has also revealed. Why are companies like Amazon, Apple, Zoom, Microsoft and Netflix thriving while others struggle? Obviously, they provide services that are vital right now, but there’s far more to it. The biggest underlying factor in their success is not what they sell or that they leverage the cloud. What sets these companies apart is that enterprise-wide intelligence underpins everything they do.
Amazon, for example, looks at uncertainty in an entirely different way than companies whose data and intelligence are isolated in silos. Customers have no idea what clicking the “buy” button on an Amazon checkout page sets in motion. Underneath it is an intelligent enterprise seamlessly managing a global supply chain, autonomous warehouses and billions of data points that enable Amazon to deliver more than 2.5 billion packages per year.
Rather than being paralyzed in a crisis, an intelligent enterprise can almost instantly ramp up, restructure supply lines, and deliver what customers want now and will want next.
How Can Companies Become Intelligent?
Even companies just starting their journey that feel hopelessly behind can advance faster and better by applying a few lessons from the leaders.
Most organizations start their artificial intelligence (AI) experience with quick wins to build executives’ confidence. The first step for most organizations is to automate specific, siloed processes in a very targeted way that leverages turnkey AI. But that narrow, short-term focus can lead companies to miss out on the far larger benefits of enabling an intelligent enterprise.
Which would you rather have: a company that is more efficient at a few tasks, or an intelligent enterprise that makes fewer big mistakes, course-corrects faster and spots opportunities earlier?
For instance, we worked with a global company to leverage AI to predict revenue. However, its enterprise-wide sensing system knows what’s happening across all its revenue streams, what’s happening in its supply chain, and what’s happening on the street and in its stores. An organization that knows this much can go beyond predicting revenue to actively shaping it. The faster it knows shirts are outselling pants because Zoom meetings can be done in shorts, the faster it can react.
Banks, historically very paper-centric, are also rapidly shifting to becoming intelligent enterprises. Hyper-automation has enabled them to better collect and store information, yet they’re also using everything the enterprise knows to analyze and shrink regulatory risks, produce insights and manage costs.
All of those benefits, and more, can be derived from AI.
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This is why today’s enterprises — already accelerating their embrace of technology amid the pandemic — should make AI a critical part of their company’s future. Many leading companies have thought hard about their goals for the overall enterprise and what they’ll measure to ensure they can achieve them. Other companies can follow this model, conceiving of it as a coherent system rather than a set of individual parts. Every transformation journey is faster and less frustrating when a complete roadmap for transformation exists right from the start.
Related Article: AI and the Year Ahead: What Now?
3 Challenges to Overcome on the AI Journey
As companies begin to map the structure of their intelligent enterprise, it’s important to understand three key challenges they’ll face.
1. Silos
Every roadmap for transformation needs to recognize that the nature of corporate divisions is to remain divided. The result is organizational silos and their corresponding data silos. Strategy will need to evolve from meeting the needs of one individual business unit to the whole enterprise. In turn, this means thinking carefully about how to collect underlying intelligence along the way. All of this is easier to do right with a proactive plan than it is to patch it retroactively.
2. Culture
Should companies believe only in the decision-making power of humans, or can we believe in machines, too? A shift in individual mindset is required as well. Is the individual executive or line worker prepared to accept help from a digital asset as readily as he or she would accept help from a colleague?
Related Article: Before You Deploy AI, Ask These Questions
3. Ethics
If AI is a black box, will biases get built in, whether intentional or not? There are many trust factors, questions of democratization and empowerment, to be considered.
I expect the shift to the intelligent enterprise that accelerated so much in 2020 will be at the root of the biggest successes to come in the early 21st century.
Exciting, and undeniably interesting, days are ahead.
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