Rethinking Automation

Firms may be tempted to rush to automation as the ultimate solution for operational efficiency. Those who embark on their effort with a programmatic framework to audit processes and employ Lean methodology before automating are likely to find greater success in their robotic technology transformation. For those already finding success with Robotics Process Automation (RPA), aligning a data strategy with automation initiatives positions the firm to take advantage of the latest advancements in cognitive technologies that can enable efficiencies further up the value chain.

The Nuts and Bolts of Automation in Asset Management

Rising operational costs and ongoing fee pressure make the use of digital labor a compelling way for firms to reduce expenses and discover efficiencies. Automation supports these goals by creating a virtual workforce with various benefits that augment existing human capital and reduce the need for human labor in repetitive, routine tasks.

Expense Reduction Potential: RPA technology across industries usually costs one-third the amount of an offshore employee and one-fifth of an onshore employee Gartner reports, which has led firms to quickly realize that a number of tasks should be turned over to machines.

Relatively Rapid Deployment: RPA sits on top of legacy systems, making it easier to deploy than re-architecting underlying technology. This is especially relevant for firms who have come together via a merger or acquisition and who may have complex and differing core platforms. Further, bot software can go live quickly, often within weeks, if the underlying processes are already running efficiently.

Scalability: Since bots do not require the training time necessary for human labor, they can be scaled up quickly with minimal marginal cost. This means that periodic tasks, such as quarter end or ad hoc reporting can be more easily managed. Bots also can run 24/7, at a higher speed and with greater accuracy than a human performing the same routine rules-based tasks.

Maximize Human Talent: As a result of automating certain tasks, human capital can then be repurposed to higher value-add tasks within the firm.

Automation is not without its challenges. First, as some tasks are removed from certain job functions, human capital needs in certain areas might be reduced. This means that it could be helpful to plan for messaging and retraining around any employee reassignments or reductions. Second, firms are likely to find that they need to add additional technical expertise, which can be costly. Dedicated hardware and personnel must be brought on to support bot security monitoring, development, and production which can offset some of the top line cost savings. Accordingly, firms may discover the best use cases for implementation of robotics have measurable economies of scale to offset these necessary investments.

Slowing Down to Quickly Benefit from Automation

Firms will see the greatest returns on their automation efforts if they apply Lean principles—a systematic process improvement methodology that uses the lens of the customer to improve value. Broken processes should be re-engineered before they are automated, but the greatest success comes from taking automation initiatives a step further and connecting them to the firm’s strategic goals. Even as some of the best ideas for operational efficiency come from the people closest to the tasks, firm leadership plays a critical role in ensuring business priorities define automation investments. Acting on automation can begin with a business process optimization audit, bringing together cross-functional teams with stakeholders at all levels to define use cases and work through the following questions.

Should this process be completed in-house?

Ideally, firms should only retain the functions that support their core competencies. Lower value activities that do not tie directly to market differentiation may be candidates for outsourcing. Value stream mapping—a Lean technique used to analyze and improve the flow of information required to produce a service or product—can assist firms in identifying which processes support their core competencies and can ultimately reveal which processes are candidates for some degree of automation or outsourcing. This effort ensures that talent can be deployed to the highest value-add tasks at the firm.

Which steps in this process are necessary?

Teams should create process maps that outline every individual step, person, and system required to complete a task. Then, ask if the procedure in question still requires all steps that have been identified. Changing regulation, internal reorganizations, and systems migrations can often leave steps in a process that are no longer necessary but have simply never been questioned. Identify the pain points, empowering teams to question why a step exists at every stage in the process. Visual displays of an end- to-end process mapped as a requirement originating from a customer or regulator can make it easier to spot unnecessary steps and inefficiencies.

Can this process be executed in a repeatable, consistent manner?

RPA tools can come online more quickly if initial inputs are standardized. This might look like centralizing data for ease of standardization or creating smart forms to capture all front-end inputs. In this part of the process, teams should also consider how to orient a process so that it can be executed in a repeatable, consistent manner. Through this step, firms may also start to identify what exceptions look like and where human intervention or judgement is required outside of a rules-based construct.

How will we measure the value of automating this task?

After a rigorous examination of tying procedures to their ultimate business value, firms are better prepared to automate processes. Conducting this evaluation within the context of overall firm strategy makes it easier to measure the true ROI of an automation initiative. Firms should not only consider the fixed and variable costs of any selected initiative, but also the added opportunity costs related to how IT and business experts’ time could otherwise be used. This effort provides a consistent framework through which to prioritize competing automation requests throughout the firm.

Comparing Automation Technologies

Earlier iterations of RPA have been deployed through many back-office processes; however, the newest technologies are expanding the universe of automation’s applicability throughout the firm.

RPA vs. CA – The Data Difference

Once firms have identified high-ROI candidates for automation, they can determine whether rules-based or CA is needed to complete the task. CA, sometimes called “intelligent automation,” is an evolution of robotic technology where advanced algorithms function more like human decision making capabilities. For example, CA bots leverage advances in computing power and high volumes of data to teach themselves how to continually improve a task, or can even make decisions based on machine- learning outcomes. This makes it best-suited for tasks that are slightly higher up the value chain and are already supported by high volumes of consistently available data.

Enterprise investment in CA is estimated to reach $232 billion by 2025, up from just $12 billion today suggests KPMG, highlighting the increase in data-driven technologies. More processes can now be automated because of this shift; however, leveraging this capability requires a robust underlying data strategy. These sophisticated data requirements mean that some firms may find they are better positioned to keep adopting earlier RPA practices until their data strategy and infrastructure advance. They may also find utility in evaluating their data strategy alongside their automation efforts because the most sophisticated solutions increasingly rely on these linkages. The business need should drive the technology’s application; however, firms should have an understanding of how these two generations of automation bring different outcomes to related processes.

Becoming a Data-Driven Organization

If RPA is already a mature capability across the firm, shoring up data strategy to shift new initiatives toward CA is an important next step. Becoming a data-driven organization requires various considerations from the top down. C-suite level leadership should be involved in data-related efforts to create alignment throughout the organization and ensure that initiatives map backward from a customer need or regulatory requirement. Consolidating data is also a critical step. Because of the massive volume of data required to implement cognitive strategies, firms should consider data lakes—capable of scaling rapidly and holding both structured and unstructured data—for their primary data storage. Lastly, a hub and spoke model can be an effective organizational design to advance data strategy. Embedding data experts in line with the business as well as providing senior technical guidance and centralized utilities combines the necessary experts to accelerate these initiatives.

RPA and CA replicate human actions—and sometimes judgement—at impressive speed, quality, and scale which can offer unparalleled operational cost savings. However, these benefits will only be realized if leadership takes a strategic approach to evaluating a process in its entirety, connecting its purpose back to a fundamental business need before embarking on an automation initiative. Firms who are not yet efficiently adopting CA should consider how they can begin integrating their data strategy with automation efforts. This dual-track approach including both process improvement and data management will help future-proof automation efforts today, better-positioning the firm for future challenges.