K
Kathleen Martin
Guest
Whenever a new wave of technology splashes onto the scene, managers face the same questions: Where do we start applying it first? Do we go after the “low-hanging fruit” that will produce quick wins and build the case for more ambitious projects? Or should we strategically focus, with no delay, on the applications that will give us a decisive edge over competitors?
Right now, with the arrival of a revolutionary set of technologies for automating knowledge work — artificial intelligence in particular — we see teams grappling 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 routinely call for deciphering information, from fielding customers’ questions to complying with government regulations to detecting fraud and cyberattacks. Because that describes so much of the activity of modern workplaces, the deliberations about where to start and how to proceed are different than with other technologies. The same old answers don’t apply.
The potential to boost performance in the typical company with these tools is both broad and deep. In one company we know, a team was assembled to survey 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 a smart machine could do to leverage workers’ creativity, increase speed to decision, improve accuracy, or enhance service to customers.
There are also strong competitive incentives: Because of this potential, companies are investing in these tools at blistering rates — according to Gartner, intelligent automation is the fastest growing area of enterprise tech investment. The pandemic gave the toolkit a giant shove forward as companies suddenly had to find new ways to perform mission-critical processes.
Whether driven by the opportunities or competitive pressure, your organization will likely soon be using intelligent automation in many, many corners of your operations. So, where should you start?
Instead of framing your goals in terms of quick victories (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 capability-building in your organization. You should sequence the projects you take on — knowing you will ultimately take on hundreds — so that the early ones build the AI talents and put in place the AI tech infrastructure for the projects you will take on next, and next, and next.
Map Where You Want to Go
Capability-building — developing the strength of an organization to solve a class of problems it will keep facing in the future — is a challenge you might have tackled in other realms. In areas from strategy formulation to project management, teams recognize that they can and must get better by learning from experience. And because there are fundamentals that must be mastered before they can advance to higher-order capabilities — they have to walk before they can run — teams often take their guidance from so-called maturity models, outlined by experts who have watched others travel the same path before. Given that your people will need to rise again and again to the challenge of implementing intelligent automation solutions, this is the approach that makes sense, but more of the thinking about the best sequence of steps will be up to you.
Continue reading: https://hbr.org/2022/02/how-to-pick-the-right-automation-project
Right now, with the arrival of a revolutionary set of technologies for automating knowledge work — artificial intelligence in particular — we see teams grappling 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 routinely call for deciphering information, from fielding customers’ questions to complying with government regulations to detecting fraud and cyberattacks. Because that describes so much of the activity of modern workplaces, the deliberations about where to start and how to proceed are different than with other technologies. The same old answers don’t apply.
The potential to boost performance in the typical company with these tools is both broad and deep. In one company we know, a team was assembled to survey 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 a smart machine could do to leverage workers’ creativity, increase speed to decision, improve accuracy, or enhance service to customers.
There are also strong competitive incentives: Because of this potential, companies are investing in these tools at blistering rates — according to Gartner, intelligent automation is the fastest growing area of enterprise tech investment. The pandemic gave the toolkit a giant shove forward as companies suddenly had to find new ways to perform mission-critical processes.
Whether driven by the opportunities or competitive pressure, your organization will likely soon be using intelligent automation in many, many corners of your operations. So, where should you start?
Instead of framing your goals in terms of quick victories (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 capability-building in your organization. You should sequence the projects you take on — knowing you will ultimately take on hundreds — so that the early ones build the AI talents and put in place the AI tech infrastructure for the projects you will take on next, and next, and next.
Map Where You Want to Go
Capability-building — developing the strength of an organization to solve a class of problems it will keep facing in the future — is a challenge you might have tackled in other realms. In areas from strategy formulation to project management, teams recognize that they can and must get better by learning from experience. And because there are fundamentals that must be mastered before they can advance to higher-order capabilities — they have to walk before they can run — teams often take their guidance from so-called maturity models, outlined by experts who have watched others travel the same path before. Given that your people will need to rise again and again to the challenge of implementing intelligent automation solutions, this is the approach that makes sense, but more of the thinking about the best sequence of steps will be up to you.
Continue reading: https://hbr.org/2022/02/how-to-pick-the-right-automation-project
