Intelligent automation depends on these 4 cornerstones
March 7, 2021, 12:00:00 AM
There has been more than a modicum of buzz around what IDC calls intelligent process automation and what Gartner calls hyperautomation. In both cases, these terms refer to the integrated deployment of digital technologies such as robotic process automation (RPA), intelligent business process management suites (iBPMS), artificial intelligence, process mining, etc. Integrating digital technologies is far from a new concept. MIT and Deloitte advocated this approach back in the day when everyone was focused on social, mobile, analytics, and cloud (SMAC).
Digital transformation is a complex undertaking. Frankly, the track record on digital transformation success is dismal, with as few as 30% of such initiatives succeeding. The integration of digital technologies may just be the lever that leads to more success. So it’s not surprising that a number of vendors have promoted this theme, including Appian, Automation Anywhere, Bizagi, ProcessMaker, and UiPath to name a few. However, no digital transformation effort will be effective if an organization doesn’t first have four key cornerstone practices nailed down: sequence, collaboration, scope, and mindset.
Strategy is more central to digital success than technology alone, yet many firms don’t have a digital strategy. In part, that’s because there is a long history of jumping to a technology solution — often involving just one tool for the sole use of just one department — instead of creating the context to identify business opportunities. There’s a long history of department heads dictating their demands to the IT department – resulting in the sub-optimal implementation of technology, the development of difficult-to-bridge data silos, and the creation of significant technical debt. For greater success with digital transformation such behavior must cease. Digital needs to be driven by the specifics of the company’s strategy, and strategy needs to be driven by customer experience.
Strategy formulation is no longer just about products and competitors. You also need to consider complementary services and network effects and this often involves a shift in business models, revenue streams, and how your end-to-end processes work. So your digital program needs to be driven from the top. By putting strategy before digital technology, your organization can think through what’s needed to scale RPA and machine learning. Then, data transparency and customer experience can begin to take center stage in senior leadership team discussions. Compare this to the not-so-intelligent approach to digital transformation where individual departments select individual technologies to solve small problems within departmental boundaries.
Another critical success factor is to redesign a process before automating it. Otherwise, you could be automating a broken process. That’s why it’s important to establish end-to-end process context using iBPMS or process mining before jumping to technology solutions. Similarly, change management focus needs to come before — not after — your implementation of integrated technology.
These tactics call for an unprecedented level of cross functional collaboration and a shift in management attention or mindset, which brings us to the next cornerstone.
When departments do not collaborate, transformation efforts suffer. Given the amount of rhetoric dedicated to breaking down departmental silos, it was astonishing to learn from a recent survey that 75% of 1,500 global senior and C-level executives saw business functions competing against each other instead of collaborating on digitization efforts. This lack of collaboration contributed to 64% of companies failing to see revenue growth from their digital investments. It’s hard to focus on customer experience and end-to-end process performance when departments don’t collaborate. Historically, individual departments have been motivated to focus on their own core duties and functions to the exclusion of others. Departments — and department managers — can view resources protectively and hoard data rather than sharing and collaborating.
You also need to collaborate across internal centers of excellence, technology vendors, and department heads if intelligent automation is to reach its true potential.
While cross-team collaboration might appear simple in theory, in practice it is challenging as the teams responsible for special skills — such as process improvement and customer experience — often sit in different parts of the organization and report to different executives. Further, the methodology that customer experience teams use is different and sometimes difficult to bridge with that of process improvement teams, which can impact their ability to collaborate. Similar challenges apply to collaboration across vendors due to the proprietary platforms that vendors have worked to develop. That has led to some vendors, including Pegasystems, Appian, and Telus International, to acquire complementary technologies instead of making the effort to work closely with others.
No one can see the big picture when individual departments just look at their own technology needs. The recent book Intelligent Automation outlines the importance of scope and context through a banking example: In a credit card fraud management case, a bank was able to deploy one digital tool and realize a 30% improvement in resolving fraud. However, when the bank looked at the end-to-end process and integrated the use of multiple digital technologies, it was able to solve 70% more instances of fraud and saved $100 million.
Both process mining and iBPM suites can give organizations the needed scope and context to optimize the transformation effort. That’s important because one of the common and persistent challenges is scaling digital technology. This is particularly evident with RPA, where Forrester found that a majority of organizations implementing RPA have fewer than 10 bots in production. Brent Harder, head of Enterprise Automation at Fiserv, has led the major automation initiatives at two financial services organizations. He told me that, in order to scale, it’s important to become really good in the discovery phase and get better at identifying the right metrics. The right context will serve to reinforce RPA’s goal of augmenting human workers — making processes faster and better, not just cheaper. Harder also emphasizes that the initiative must have strong champions and a solid delivery team who are intent on building in-house capability.
The right scope matters for digital transformation. Frankly, if the digital effort is not cross-functional — then it’s not transformational. If the effort is not focused on customer experience and targets only cost reduction, then it’s not transformational. According to recent research on AI, organizations that viewed and changed large business processes in integrating AI solutions were five times as likely to realize significant financial benefits.
For many organizations, taking a big picture approach demands not only focusing on the right scope, it also involves a fundamental shift of management attention – a new mindset.
Shifting management attention from a traditional static, hierarchical view of business to a customer centered, agile, business process-based view of performance involves a new mindset. First, instead of just thinking about what is good for the company, leaders need to focus on what is good for customers. Second, instead of thinking just about what is good for their individual departments, leaders need to shift their attention to what is good for the entire organization. Then, instead of just thinking about deploying one discrete technology for the benefit of an individual department, leaders need to think about deploying multiple digital technologies in an integrated, agile manner for the benefit of the business.
All of the above requires a significant change in mindset. It must overcome decades of silo behavior conditioning. It’s shocking to observe that some experts in intelligent automation still focus on use cases by department instead of value stream or end-to-end process.
A big-picture view also calls for a management mindset where there’s explicit attention across customer experience, employee experience, and company profits. And not just at the start of a transformation effort. It’s all too common for leaders to be enthusiastic and visible in the early stages of a transformation and then be drawn to other issues and leave things to be run by the project team.
Implementation skills such as change management are comparatively underemphasized in many organizations, leaving many leaders and information systems teams underprepared for this important part of the process. When there is a focus on change management, all too often it’s limited to stakeholder analysis and a communication plan. In many instances, that’s just not enough. Paul Fjelsta, an expert in behavior management who I’ve known for years, says we need a new approach: Instead of just focusing on the technical side of change, leaders and project teams need to understand the specific behaviors that need to be changed — and how to change them. Researchers at MIT also emphasize the importance of leadership capability, as a key enabler in driving systemic and systematic organization change.
In a nutshell
The integrated deployment of digital technologies such as RPA, iBPMS, AI, and process mining can lead to higher success rates in digital transformation efforts. But to effectively deploy these technologies, you need to have the four cornerstones in place I’ve described above — sequence, collaboration, scope, and mindset. If you lack any of these cornerstones, you risk hitting a number of pitfalls, such as deploying a technology for the benefit of just one department, not tearing down data silos, failing to focus on customer experience, failing to view the business in the context of end-to-end processes, and holding onto detrimental interdepartmental behavior.