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Technology

Cognitive Operations: CFO’s Secret Recipe for Success


by Ashok Pai

A cognitive operations transformation realigns enterprise processes, technology and infrastructure to deliver better business outcomes.

It was a major international merger, and the clock was ticking. The CFO had to determine if the framework and operating model they developed would ensure the pending merger and acquisition (M&A) is a success.

This scenario is an indication of the CFO’s changing charter. Tacking onto their traditional focus of managing financial health, compliance and risk, the modern CFO’s responsibilities is focused on growth and efficiencies, identified through new business models and revenue streams. 

CFOs now play an increasingly pivotal role in strategy formulation and execution, including M&As, which have grown exponentially over the last two years. We already know that enterprises have long been using M&As as a strategic lever for accessing new markets, products, technologies or for capacity absorption. However, these come with significant risks – some research puts the failure rate above 75 percent. That’s why CFOs are adopting a new strategy to make more informed business decisions, ensuring that, in today’s hyper-competitive business world, they distinguish themselves as disruptors rather than the disrupted.

When a big European life-sciences company agreed to pay tens of billions of dollars for a U.S.-based multinational, the companies set an aggressive timetable. The schedule called for completing the deal within two years of the announcement, and for integrating business operations, including the information technology systems.  

Missing the two-year deadline would force the would-be acquirer to pay a $2 billion penalty – or even risk have the merger called off. To complicate matters: To approve the deal, antitrust regulators required the companies to divest various overlapping business units. 

Not so many years ago, the merger might have fallen victim to delays from these challenges. But through the strategic harnessing of “cognitive business operations,” the deal closed on time. The buyer avoided financial penalties. Better yet, it even made a tidy profit selling off those business units. 

The effort’s success made a hero of the life-sciences company’s chief financial officer. It was the CFO who helped orchestrate the strategy, communicating the importance of the cognitive-business approach to the entire C-suite.

Cognitive business operations involve embedding intelligence into enterprise business processes, networks and IT infrastructure through the latest digital innovations. That includes AI, machine learning, analytics and the cloud. What distinguishes cognitive operations from digitally enabled processes and systems is that instead of focusing simply on improving speed or efficiency, the transformation realigns enterprise processes, technology and infrastructure to deliver better business outcomes. 

The use of intelligent automation can significantly impact an enterprise’s operations. That might include using AI to not only streamline but transform global supply chains. Or, to cite another example, it may involve using machine learning to anticipate – and avoid – problems that could disrupt a large retailer’s point-of-sale network. 

Such issues matter to an organization’s entire C-suite, of course. But typically, it is the chief financial officer who has the broadest view of the value that cognitive business operations can bring to an enterprise.  That’s because today’s CFO plays a strategic role across the enterprise,  giving the executive a broad view of how the organization can use automation, machine learning and data analytics to transform the operating model.  

In the merger case, the cognitive approach greatly streamlined and sped up integration of the IT and business operations. It also enabled a quick sale of those relevant business units. How? Rather than try to simply sell off the units, the companies gave each one a cloud-based  intelligent automation-led make-over, integrating operations before putting them on the market. The approach was analogous to getting a house ready to sell by renovating it, rather than selling it “as is” and leaving the rehab to the buyer. 

The various units, ready to operate as digital and stand-alone businesses, found buyers, quickly after, being put up for sale – and received a much higher valuation, considering the reduced transition risk for the buyer firm.

It goes beyond M&As. In a supply-chain case, a major oil company with worldwide operations was struggling to keep tabs of the tens of thousands of products it procures from thousands of vendors. The CFO spear-headed an effort to adopt artificial intelligence software to give the oil company a new, big-picture view of its purchases – and began to discover anomalies in the welter of invoice data that even the most eagle-eyed human accountants could not be expected to spot. 

That’s how, for instance, the company identified huge variations in prices – from two-times to five-times – it was paying for life rafts on its offshore oil rigs. By discovering many other examples of such “maverick spends,” AI vigilance enabled the oil company to save millions of dollars a year in its global supply chain. 

Beyond high-velocity data mining, cognitive operations can also put machine learning to work. That’s what happened with a very large retailer that operates dozens of high-end department stores throughout the United States. 

These days, as more retailing moves online, a luxe retailer’s ability to provide a superior in-store service that exceeds the online experience is more important than ever. A merchant can ill afford delays or outages in its point-of-sale terminals or networks – particularly during high-volume holiday shopping periods. In a CFO-led effort, the big retailer deployed AI software that provided a digital, real-time overview of the entire point-of-sale network. 

In monitoring when and where disruptions cropped up, the data forensics system quickly began to learn from its observations: When did that Severity 1 trouble ticket occur? Was an outage the fault of a down server? A glitch in the database? A memory utilization issue? Something else?
 Before long, the machine learning system was not only analyzing problems but also anticipating them – and preventing them. Last holiday shopping season, the national retailer’s entire point-of-sale network did not incur a single outage.  

In each of the cases described, the company’s chief financial officer played a key role in championing the adoption of cognitive business operations – helping the CEO see the wisdom of the digital transition and finding the resources to support it. 

It wasn’t so many years ago that if you talked to CFOs about using IT to serve their business needs, they focused mainly on saving money. 

Lately, the conversation has changed. Costs still matter, of course. But now, the CFO is increasingly also focused on identifying new revenue opportunities and unlocking business value.

So, he or she is likely to say something like, “My company has grown so much through mergers that we no longer have single-view of the entire operation.” Or the CFO might say, “We’re drowning in data. And the market is changing so fast around us, we’re having trouble recognizing new opportunities and responding fast enough to take advantage.”

That’s when I say, “Let’s talk about the value of cognitive business operations for your enterprise.”
 

Ashok Pai is vice president and global head of cognitive business operations at Tata Consultancy Services.