Unpacking the Differences Between Automation and AI in the Enterprise

Unpacking the Differences Between Automation and AI in the Enterprise

In the push for digital transformation, enterprise decision-makers often use the terms “automation” and “AI” interchangeably. But while both automation and AI are poised to transform processes across industries—PwC estimates that AI alone could contribute over $15.7 trillion to the global economy within the next 10 years—they are discrete technologies.

As enterprises strategize the incorporation of automation and AI-driven applications in their technology ecosystems, it’s critical to clarify the distinctions and understand the unique role each technology plays in digital transformation initiatives.

A Comparison Of Automation And AI Technologies

Automation and AI enable enterprise migration from manual routines toward more innovative and efficient digital processes. Although both technologies equip organizations with solutions to the evolving demands of customers and employees, there are key distinctions in their capabilities and limitations, as well as the benefits they deliver to the enterprise.

• Automation is rule-bound; AI is autonomous:From a functional perspective, automation relies on human interaction and preset rules and conditions, established by human employees. In contrast, AI operates semi-autonomously. While humans retain responsibility for decision-making, AI and machine learning (a subset of AI) adapt and learn independent of human involvement—something automated applications simply aren’t designed to accomplish.
• Automation streamlines simple tasks; AI accelerates decisions:Automation’s sweet spot is the completion of time-consuming and repetitive processes that distract humans from their core responsibilities. AI, on the other hand, mimics human intelligence—it recognizes patterns in historical data and generates informed insights that improve decision-making. Although both technologies have the potential to disrupt traditional work routines, concerns about workforce displacement typically focus on AI rather than automation.
• Automation is already entrenched in the enterprise; AI is relatively new:The application of AI—technologies that constantly improve through experience—is relatively new to the enterprise. But enterprise automation has existed for decades. For example, in the automotive industry, the production assembly lines implemented in the early 1900s represented an early application of automation technology.

Robotic process automation (RPA) merges traditional automation with AI capabilities, enabling access to next-level automation technology. Blending elements of automation and AI, RPA moves organizations down the path toward intelligent automation—the ultimate goal for many businesses.

Using RPA, businesses can configure software or “robots” to partially or fully automate manual, rule-based activities. With 43% of decision-makers identifying RPA as a key element in their organizations’ digital transformation strategies, according to a report from my company, RPA is positioned to play a pivotal role in companies’ technology ecosystems.

Key Takeaways For The Enterprise

Traditional automation, AI and RPA all have potential applications in your organization, provided you understand their unique capabilities and align your strategy accordingly. As you move forward with the implementation of these technologies, here are several considerations to keep in mind:

• Engage your front line:Front-line employees are important participants in digital transformation. Solicit feedback from your front line—through surveys, focus groups, one-on-ones and other mechanisms—to identify bottlenecks, broken processes and opportunities for improved efficiencies.
• Target the right processes with the right technology:Not all processes are good candidates for automation or AI. In general, processes that follow clear rules (e.g., managing contracts or system monitoring) can benefit from automation, while processes that rely on large quantities of data to produce insights or recommendations (e.g., claims processing) may be candidates for AI.
• Start small:Rather than trying to transform processes that affect your entire organization, start with low-hanging fruit—smaller tasks with clear goals and outcomes. As you gain experience, gather feedback to improve future implementations and minimize the potential for unintended consequences when you transform processes that impact your entire organization.

According to a different study from my company, a majority of employees recognize the opportunities associated with automation and AI, but they are also concerned that these tools could result in the elimination of their jobs.

As you evaluate the potential of automation and AI technologies in your enterprise, consider how you message their implementation. To ease employee concerns, develop an empowerment narrative around automation and AI, emphasizing how these technologies stand to benefit the organization and augment (rather than replace) human workers.

Source: Forbes
Author: Eric Johnson