How to choose the right automation project


Intelligent automation is the fastest growing area of ​​business technology investment. The potential for improving the performance of a typical business with these tools is both wide and deep. But when companies look for places to apply these tools, they often fall into two common pitfalls: looking for quick and easy wins that won’t have much impact, or big, ambitious projects that will deliver major strategic benefits. What they should focus on, however, is a third option: projects that build their capacity.

Every time a new wave of technology hits the scene, managers are faced with the same questions: where to start applying it? Are we going for the “low hanging fruits” that will produce quick wins and build the case for more ambitious projects? Or should we strategically focus, without delay, on the applications that will give us a decisive advantage over our competitors?

Today, with the arrival of a revolutionary set of technologies to automate knowledge work – artificial intelligence in particular – we see teams wrestling with these questions at high levels in organizations. Intelligent Automation (the term commonly used for robotic process automation, machine learning, and artificial intelligence in organizations) brings unprecedented speed, accuracy, and pattern recognition power to business processes that require decrypting information on a regular basis, from answering customer questions to complying with government regulations to detecting fraud and cyberattacks. Because it describes so much of the activity of modern workplaces, the deliberations of where to start and how to proceed are different than with other technologies. The same old answers don’t apply.

The potential for improving the performance of a typical business with these tools is both wide and deep. In one company we know of, a team was assembled to study all of its operations, find areas where people’s time was being consumed by repetitive information processing work, and come back with candidate tasks for automation. The list stretched to hundreds of things an intelligent machine could do to leverage worker creativity, speed up decision-making, improve accuracy, or improve service to customers.

There are also strong competitive incentives: because of this potential, companies are investing in these tools at a breakneck pace – according to Gartner, intelligent automation is the fastest growing area of ​​business technology investment. The pandemic has given the toolbox a giant boost, as businesses suddenly had to find new ways to run critical processes.

Whether driven by opportunity or competitive pressure, your organization will likely soon be using intelligent automation in so many aspects of your operations. So where to start ?

Instead of framing your goals in terms of quick wins (which won’t really move the needle) or major strategic applications (which require skills and foundations you don’t yet have in place), focus on how your first steps will advance capabilities – building in your organization. You must sequence the projects you take on – knowing that you will eventually take on hundreds of them – so that the first develop AI talents and set up the AI ​​technology infrastructure for the projects you will undertake next, and then, and then.

Map where you want to go

Capacity building – building the strength of an organization to solve a class of problems it will continue to face in the future – is a challenge you might have faced in other areas. In areas ranging from strategy formulation to project management, teams recognize that they can and should improve by learning from experience. And because there are fundamentals that must be mastered before they can move on to higher-order abilities—they must walk before they can run—teams often draw inspiration from so-called maturity models, described by experts who have seen others walk the same path before. Since your employees will constantly face the challenge of implementing intelligent automation solutions, this is the approach that makes sense, but thinking about the best sequence of steps will be up to you.

Planning this journey requires determining how your team or organization will deliberately transition from novice to expert.

The first step is usually an assessment of existing capabilities: the challenges your people already know how to overcome and the sophistication of the tools they have to solve them. Perhaps you already have strong data analytics skills on staff, for example, or people who have been involved in RPA installations elsewhere.

Your next step is a gap analysis. This details the difference between your current capabilities and the requirements of the most difficult solution you can consider adopting. It could reveal that your current IT infrastructure is simply not up to the task of a wave of upcoming applications that will need to interact with disparate data sources. Or that much more effective collaboration will be needed between software developers and business process owners than in the past.

Finally, with start and end states clearly articulated, you can then specify a step-by-step journey, with projects sequenced based on who can do the most in the early days to lay the essential foundation for later initiatives.

Here is an example to illustrate how this approach can lead to better choices. At a construction equipment manufacturer, there are three tempting areas to automate. One is the solution offered by a vendor: a chatbot tool that can be implemented quite simply in the internal IT help desk with an immediate impact on wait times and staffing. A second possibility relates to finance, where sales forecasting could be improved by predictive modeling enhanced by AI pattern recognition. The third idea is important: if the company could use intelligent automation to create a “connected equipment” environment on customer jobsites, its business model could shift to new revenue streams from digital services such as remote monitoring and control of machines.

If you’re going for a relatively easy implementation and quick ROI, the first option is a no-brainer. If you’re instead looking for big publicity for your organization’s bold new vision, third is the ticket. You can create a separate tiger team or organization and give it full license to disrupt existing activity. But note that none of these approaches really sets the stage for intelligent automation to spread to other applications through the existing organization; they don’t make people in your organization generally more interested, receptive, or able to apply smart technology elsewhere. In other words, as an organization, taking these paths doesn’t get you far down the learning curve, toward greater maturity with technology.

Option two would do just that, largely because it would require the company to pull itself together on the data. Without a good enterprise data strategy, people in different parts of the organization lack common standards for what data to collect and how it should be organized, cleansed, and prepared for analysis. This is a fundamental capability that the business will need to have in place to move forward in using machine learning at scale. From a capacity building perspective, it’s easy to see how progress on enterprise data would unlock, say, 10 more projects – which in turn can be prioritized based on what additional capabilities they might add. Our manufacturing company could present a roadmap showing how, five years later, it will not only reap the rewards of specific projects, but also be generally and deeply more ready to take on truly transformative initiatives.

Why automate

Fifty years ago, when the legendary Peter Drucker coined the term “knowledge workers,” he also recognized how their rise in the global economy would challenge organizations. “The most important contribution management has to make in the 21st century,” he wrote, is “to increase the productivity of intellectual labor.” Finally, in the realm of intelligent automation, a powerful toolkit exists to do just that – and the race is on. Avoid the mad rush that drives your organization to seek out possibilities but without collective progress. Choose your locations wisely and your investment in intelligent automation can be a capacity-building journey.


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