Accenture’s recent intelligent operations research highlights an important trend: the growing acknowledgement that artificial intelligence will have a major impact on companies’ business. Nearly 90 percent of executives in our research believe the application of automation, analytics, and artificial intelligence (AI) will be critical to business and process transformation. Furthermore, just over 60 percent said the application of automation and AI to business processes will be important to achieving their business goals.
Yet despite acknowledging AI’s significant promise, most organizations to date haven’t fully benefited from the technology—largely because they have a far too narrow view of it and what it can do.
Many companies equate AI with traditional automation or Robotic Process Automation (RPA), which they’re using to achieve incremental performance improvements—typically, headcount or cost reduction. In fact, according to Accenture’s Process Reimagined research, while many companies are employing some degree of automation, only 9 percent are using the full force of AI. At one level, that’s understandable. Business leaders are challenged to reduce costs, and that’s the easiest benefit to quantify and measure. They often struggle to believe that other value levers can have a big, quantifiable impact.
A great example is in finance and accounting: What AI can achieve in working capital improvement dwarfs the benefits a company could achieve from pure labor cost reduction. Think about how most companies close their books—typically on a quarterly or maybe a weekly basis. Doing so still leaves a lot of uncertainty about the company’s working capital position. Ideally, a company could do continuous closing so its books reflect the company’s current reality in real time. That’s now possible with AI, as it can handle instant validation of the who, where, when, what, and how of a limitless number of transactions. As a result, every transaction is precise and instantaneous, which gives the company a complete, accurate, real-time picture of its financial state—which, for a typical large company, can free up potentially millions of dollars in working capital position.
In other words, automating existing processes and repetitive tasks can free up employees for more valuable work and reduce costs, but simple automation, in most cases, shouldn’t be the end goal.
The key to getting the most from AI is thinking much bigger: generating strategic and higher-value business outcomes by using AI to completely transform a process, function, or enterprise.
Thinking small is hoping to shave a few minutes off an existing process or reduce costs by a few percentage points. Thinking bigger is determining how AI could fuel process transformation to drive much bigger outcomes and create differentiated, more personalized experiences for the people and entities a company deals with: its customers, suppliers, and employees who use a specific process. That’s where AI’s true value lies.
“The key to getting the most from #AI is thinking big: high-value outcomes that completely transform a process, function or enterprise.”
Think about invoice processing. You could certainly use AI to process a supplier’s invoice with a lighter touch or even no touch. But take a step back: Maybe that invoice should be paid early to take advantage of an early-payment discount. Or, perhaps you don’t want to pay it early because there’s no discount and you want to maximize working capital.
Take another step back: Is there an opportunity to improve your terms with that supplier? And another: Are you sourcing too many similar items from too many suppliers? AI can illuminate opportunities to rationalize the supplier base to further reduce spend in that category.
Applied with such thinking, AI can drive more business value beyond just processing the invoice at a lower cost. It enables you to better delight suppliers, because they're getting paid faster; and to more effectively serve the buyer, because they're getting what they need without hassle. This helps transform an internal center of cost to a center of service and, ultimately, to a center of real business value.
Another example is the process to onboard new employees. Thinking small is looking to bring aboard an employee faster by a couple of minutes or days. Thinking bigger is striving for a more impactful outcome: Creating a compelling and differentiated experience for every new joiner, one that creates greater excitement, engagement, and momentum and sets the right tone for employees’ careers with the company. For instance, AI can help new joiners find the most appropriate mentors by modelling joiners’ personalities and behavior traits against those of existing employees and simulating how those matches would turn out. AI can also use a similar approach to help new joiners more quickly build a broad and deep network within the company that will best support their career advancement.
The key point is that AI shifts the focus from the “how” of a business process to the “why” and “what,” which opens the door to innovative, transformative approaches that don’t present themselves when following rigid processes based on best practices. AI can enable companies to automatically reconfigure certain processes on the fly—proactively avoiding exceptions and other obstacles by finding new routes to the specific outcome the technology is told to achieve. That’s critical, for instance, in companies that must continually update business processes to handle fast-changing legal, environmental, or accounting requirements.
AI is different in a couple ways from the long line of technologies that have promised to “transform” companies. We're now seeing revolutionary, not incremental, change, and it’s coming so fast it’s increasing pressure on companies to act or risk getting left behind. Consider Amazon, whose persistent adoption of AI has helped propel it from online book seller to purveyor of everything, and who now owns more than 90 percent market share for online purchases in five categories.
This change also can be more overwhelming because it involves so many different capabilities, technologies, and players in the ecosystem, further adding to the level of complexity. And the sheer number of potential applications for AI is far bigger than for past technologies—we’ve yet to even scratch the surface.
So how does a company determine how to use AI and where to begin? Read our next post in this series to find out.