The Bot Revolution Stalls As AI Is Difficult To Scale


As Roots Automation’s CEO and co-founder, Chaz is responsible for business development, finance and customer success.

Throughout the economic crisis caused by Covid-19, high-performing companies invested in AI and cognitive automation. Though plenty of corporations survived the pandemic without implementing cognitive automation, the digitization of the workforce has begun. And, just like the Industrial Revolution, this progress toward augmentation will be difficult to ignore.

This isn’t to say that digital transformation comes easily. Even large enterprise corporations with impressive budgets grappled with underperformance last year. Whether it’s due to lack of enthusiasm among employees, difficulty with deployment and change management or a genuine unfamiliarity with process automation, the conversation around AI dominating the workforce is primarily moot because up to 50% of automation fails.

There are plenty of resources, studies and analytical insights into why such a powerful tool can be so difficult to wield, even in organizations with the capital to invest wholeheartedly. Broadly speaking, it’s not the bots that fail to scale. Instead, it’s often an oversight within the deployment strategy, where automation was not properly introduced to the organization.

Engaging The Employees

Change management is such a broad topic that it’s often reduced to a buzzword. Even so, a post-deployment strategy is equally as important as the automation itself. The workforce can’t be augmented while employees remain oblivious.

The profitable deployment of automation is contingent upon the onboarding process. Successful scale is found in a practical approach: introducing and engaging employees throughout the automation journey.

1. Include the workforce in automation discussions.

Ever since Stephen Hawking’s emphatic condemnation of AI, the potential mass displacement of human workers has dominated every conversation about digital transformation. Of course, this ignores the fact that — in the absence of AI and automation — companies have been displacing employees for decades by either outsourcing jobs to other countries or simply eliminating roles to meet expense pressures and hoping the remaining, likely overworked employees will pick up the slack.

Even so, the workforce is incredibly wary of the shift toward digitization, with nearly half of employees believing that AI has directly impacted job displacement in their workplace. This could lead to a reluctance to utilize the bots once deployed for fear of being deposed. Just as common, employees may find it difficult to use a tool as new and unfamiliar as AI if not properly introduced.

To avoid an unsuccessful deployment of costly AI-powered automation, employers should engage their human workforce throughout the acquisition or creation of automation. By using direct communication throughout the adoption process, employees feel engaged, included and even excited at the idea of having a robot among their ranks. Curiosity outweighs fear as they take the initiative to learn more.

Additionally, employees are SMEs (subject matter experts) within their respective departments. Including the workforce on automation conversations will help organizations spot areas where the AI wouldn’t be an effective solution, and then make an informed decision on which areas will have the highest return once automated.

2. Include the workforce in automation evaluations.

The decision to scale automation or implement additional bots shouldn’t be finalized without the input of employees. If there’s a high rate of employee dissatisfaction with the AI in a certain department, there’s a good chance that the automation isn’t as effective for those processes.

Additionally, it’s always ideal for management to investigate frustration. If the AI has not alleviated the day to day of the workforce, it’s likely that the automation isn’t functioning as intended — or, more dangerously, the company hasn’t implemented automation in the best area.

3. Include the workforce in automation expansions.

Employees and middle management may have surprising suggestions for where additional AI could be implemented for maximum impact. As the workforce becomes familiar with the tasks the bot can take on, these SMEs will be able to help management determine the most effective — and likely, the most remunerable — automation journey.

Automating Across The Value Chain

Only 13% of companies are successfully scaling intelligent automation, which could be attributed to a lack of strategic initiatives across the value chain.

Typically, a company that is in the process of scaling automation will have deployed 51-plus bots. These bots would be found across multiple departments and job functions. To successfully scale, it’s unwise to simply automate one entire department while leaving the remaining to function solely on humans. To scale automation, the bots within each department need to correspond with each other and interact seamlessly.

In developing an AI strategy, it’s most effective to start implementing a handful of bots in multiple departments, rather than multiple bots in only one department. This approach to an automation journey will allow for more testing and experimentation, and at relatively lower risk. Additional opportunities for automation expansion will be discovered more easily.

For maximum return, the automation strategy should be measured against pre-determined KPIs and long-term company goals. Two common mistakes that inhibit sustainable scalability are that the effects of automation aren’t measured 1) based on operational improvements (i.e., the leading indicators of success) and then financial benefits (i.e., the trailing indicators of success), and 2) in a manner that encourages clear analysis.

When a company can evaluate and quantify the impact of automation, it can identify why the AI was or wasn’t successful. Often, these corporations find that there would have been a more impactful area to automate first.

Strategy Over Technology

AI is more likely to fail when an organization places more importance on the technology than the deployment. Powerful tools like bots can’t be wielded effectively without proper technique.

As it stands, organizations are failing to successfully scale automation — partly due to failure to communicate with employees, the resulting improper deployment and an overall lack of automation strategy. By knowing the weaknesses of prior early adopters, companies can develop more data-driven implementation practices with a stronger emphasis on long-term goals.


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