Think bold, shed technical debt

Digital transformation is a fundamental reality for businesses today. Organizations of all sizes realize that to delay it further is to risk obsolescence. And it's up to a company's leadership team to commandeer this evolution while ensuring business continuity, and maximizing value from new technologies. But that takes new ways of thinking about technical debt, the human experience and much more to derive the full benefit of a built-to-evolve organization.

Two Deloitte transformation specialists provide their thoughts on what industries are being pushed to transform, where the pull is coming from, the human element, and unlocking the true potential of a kinetic enterprise transformations.

The “Why not now?” mantra

While some industries are experiencing disruption more rapidly, none are immune, says Gautam Mylavarapu, senior manager, Deloitte Consulting LLP. And industries that have a high barrier to entry are often lulled into a false sense of security: Disruption may come in through the side door in ways completely unrelated to the industry. Consider automotive. Essentially heavy, manufacturing, yet it’s on the forefront of innovation, and not because of the car per se, rather because of what’s happening with transportation in general–think: Consumer demand for electric vehicles and driverless cars.

In the experience of Paul Khanna, principal, Deloitte Consulting GmbH, many organizations struggle to make the case for change, grappling with perceived high risks of failure, only to wait too long and have change forced upon them. Then, having gone through the experience, find themselves on the other thinking: That wasn’t so bad. We should’ve done this sooner. “The opportunities are so great; innovation is so amazing. There is a why-not-now? mantra. The risks are so different.”

Bold thinking and the pull to evolve

Building a case for bold thinking around proven technologies, Mylavarapu calls the pace of tech change “phenomenal.” He points to the speed of development from typewriter to computer, then from PC to internet (200 and 30 years, respectively, by his estimation), and then “three years for a company that did not exist to map the human brain completely.” He cautions against the tendency to treat technology “as a science lab experiment.” Instead, he advises, think boldly and focus on what’s already proven for other industries and competitors “to understand the value of every technology out there.”

The big pull for built-to-evolve transformation isn’t just coming from bold-thinking executives either, says Khanna. Customers and employees are significant influencers as well, and the application of disruptive tech like machine learning and RPA have reached a point where they are part of the everyday, and we’re not even aware of it. “With machine learning, RPA (robot process automation), intelligent enterprise all coming together, you're getting a lot of new ideas, particularly from some of our younger folks who are coming in and saying: ‘Why not look at this way? Why aren't we really pushing the envelope around what's possible versus what we know we've always done before’.”

First: Shed technical debt

Technical debt remains a significant hurdle to transformation, especially when so pervasive it’s in the company’s DNA. The good news is today’s technology can easily replace a myriad process right off the bat. And whether it’s removing archaic accounting principles or ‘that’s-how-we’ve-always-done-it’ processes, the point, Khanna says, is moving away from a “one and done” mindset towards a much nimbler approach. “But first, tackle the issue of technical debt. If you don’t, you end up bringing all that stuff with you and you don’t get the gains expected as part of a big transformation program.”

The human element–a case study

Transformation rooted in disruptive technology give companies the tools to build agility into their approaches and the freedom to think differently about how to solve problems–like taking into account the human element. Case in point, Khanna offers, is a customer who wanted to optimize their production line, repairing persistent delays and inconsistent quality issues. Starting with the production schedule, the team then examined employee demographics.

What they discovered was surprising and inspiring. “By having machine learning develop an algorithm to develop the production line based on location, traffic patterns, we immediately saw a 22 percent productivity boost in the first month right into 36 percent just right after. And that's all because we looked at the human element, not just the production element.”