Robotic process automation – or RPA – has been in the headlines a lot lately in the media, analyst blogosphere (such as it is), and the cognoscenti continue the debate as to the future of process services and jobs in the future. We at the Center for the Future of Work just published our take (“The Robot and I”) on the impact of intelligent process automation, with some ground-breaking research showing the impact of people working WITH – not against – automation.
Yet there may be a gnawing concern that applying a robot as a dance partner to an “as-is” process, for instance, still leaves organizations woefully short of the truly differentiated ballet of today’s high-flying, competitive outliers that have disrupted entire industries through process digitization.
Process digitization can also radically accelerate and transform data analytics — and business models. Nearly one-third of respondents in our sample base of 537 executives in the US and Europe cite improved quality/consistency/believability in the data they’re getting from digital process initiatives, and 27% report easier integration across processes.
What’s also changing is the impact of digital processes on value chains and operating models. Each industry and its processes — whether claims management in insurance, or reconciliation or mortgage processing in banks — is swiftly adopting new process models. Relationships that were traditionally transactional are now “interactional;” that is, rather than being “once-and-done,” they involve multiple interactions, and the more you can learn about a customer, supplier, partner — or even employee — the more meaningful each subsequent transaction can become.
By the same token, value is more aligned with process data than with the process itself. As operational models focus more on services or outputs, they enable organizations to build new, truly flexible and adaptable process models that can be quickly piloted and refined — or allowed to “fail fast.” As a result, digital processes are being re-formed in new, cost-effective and powerful ways that unlock meaning through analytics.
Digital technologies and digital processes can only be effective if the data source is credible and usable. However, it’s not as simple as it sounds. When asked about the biggest challenges associated with efforts to digitize processes, executives said that data security “is, will be and shall remain” the biggest issue they confront, now and in the future. Looking at these and other top challenges demonstrates that — despite current excitement for future process innovations — leaders are proceeding with “eyes wide open” to the potential risks:
- Data security tops all challenges related to digital processes. Fifty-two percent of respondents cite data security as the chief issue today. As digital processes proliferate, and as leaders see the value they create, an entirely new ecosystem of value-added services will develop to ensure the security, risk, privacy and compliance of the value chain of information these processes generate.
- The issue isn’t likely to go away. Forty-five percent of respondents continue to foresee data security as the defining digital process challenge, even three to five years out.
How will you respond? Scan your process topography and target processes (or fragments or pieces of sub-processes, say, auto-adjudication in claims management) that might lend themselves to being low-hanging fruit for automation. Consider the following as a simple, yet effective checklist to begin the assessment:
- Analyze your company at the process level. Review in detail your processes as they exist today (new product/service development, sales and customer relationship management, operations, etc.). Infuse a digital process plan by re-imagining moments of customer engagement or constituent journeys. Target tangible process metrics: cost-per-claim, clinical trial yield, healthcare unit cost, fraud prevention rates, etc.
- Help humans evolve toward the work of tomorrow. Start by giving employees access to digital processes and machines that help them do their jobs better, smarter and with more meaningful impact to the business. It’s not about the number of people tied to “doing the process;” it’s about outcomes and making smart people even smarter.
- Create, educate and inculcate “the vision.” Move from recognizing that something “needs to happen” to “making something happen.” Business processes — automated, digital or otherwise — are useless if they don’t support a business strategy. That means helping smart people make smarter decisions in support of differentiating activities. Get true alignment and buy-in to design, develop and deliver — and move fast to get “runs on the board” to maintain and sustain interest.
- Make “meaning-making” mean something powerful — fueled by process data. The imperatives to “do analytics” or “use big data” are just too broad to be meaningful. Instead, focus on a specific business process. Whether it’s your underwriting process, clinical drug trials, wealth management service, supply chain or customer relationship management process, focus on work that shapes at least 10% of your costs or revenues. To seize competitive advantage, look at the data that is — and could be — exchanged and used for value.