Why Automation Projects Fail
(And How to Fix It)

Broken Automation Robot

5 min read

Many businesses are in a rush to deploy automation into their operations and are often even more eager to be seen by customers, analysts and other stakeholders as doing so. There is no question that automation technologies have the potential to improve service quality and reduce costs within service operations. It is also a badly kept secret that many large companies are quietly struggling to get the desired impacts from their automation efforts.

Through our work in implementing data solutions around the globe, we have the opportunity to see many automation programs underway within the world’s largest companies. We’ve developed an empirically-derived view of what works well and what does not—and we’ve observed some common themes across those successes and challenges.

Our core analytics product, TK, empowers automation teams and decision makers to focus investments where they’ll return the most value and equally to catch and correct previous investments that are failing to return value. TK does this by applying advanced data science across a centralized digital footprint of data on how work happens inside the company, with algorithms that automatically identify opportunities for improvement across efficiency, service and risk.

Most businesses are at their most basic level a collection of processes. Maximizing the performance of an operation across efficiency, service and risk tasks leaders with the challenge of fixing the right things at the right time to avoid wasting investments on the wrong things at the wrong time.

Specifically, CKM’s product’s identify opportunities to:

Eliminatewhat you don’t need to be doing (those things that return no value)

Optimize the remaining processes to drive consistency and governance around performance

Automatethose steps within an optimized process that can be logically and efficiently executed in an algorithmic manner

This mindset is founded in over a decade of deploying data science solutions towards operational improvement. Across those deployments, we’ve observed three key trends that lead to failed automation investments:

1. You will struggle to effectively automate processes that are not already highly efficient

This is likely one of the biggest issues with automation applied across operations. Automation is highly unlikely to magically make broken, disconnected and inconsistent processes suddenly perform well.

Too often, operations managers want to skip the basic process improvement steps. Managers may not want to reveal that the status quo of the process is terribly inefficient, or they may believe that ‘rolling out the robots’ will fix everything.

TK’s examination of operational data can reveal the maturity of an operation. Immature operations regularly have inconsistent handling and routing of work. The good news is that this same data then offers a very clear path to target specific areas of the operation for improvement, prior to a strong push to automate these processes.

2. Use data to rigorously guard against “automation for automation’s sake”

Organizations often have a strong tendency to push for the rollout of automation without considering what the automation will do, or what the core problem is. Automation should be a solution to a defined problem. Automation is definitely not an all-purpose solution to whatever your problem happens to be.

For example a program may focus on automating a fix to a recurring issue, such as deleting excess temp files on a disk that keeps filling up, instead of fixing the actual root cause of the problem—e.g., fixing bad code that’s creating that mess in the first place. Automation can certainly help mitigate some short-term symptoms, but it’s not an excuse to avoid old fashioned problem management to fix the root cause behind the symptoms.

We once had an engineer at a meet-up tell us that they felt pressure to avoid traditional problem management because repetitive break-fixes were the easiest to automate—fixing the root causes would lower the number of automated actions in the environment. That in turn would lower the automated event numbers reported to senior management for a global automation deployment effort. Don’t be that team.

Another common trick, especially with automation vendors, is to report ‘success’ on ‘fixing’ problems that weren’t considered problems prior to deploying the automation tool. Yes automatically rotating that log file an extra time a day is now ‘automatically handled.’ Did doing this allow a vendor to say they “increased the amount of automated activities in your environment?” Yes. Did doing this and the cost associated with the effort solve any real business need or generate any real value? No.

If your automation programs focus on reporting an ever-growing number of automated events over solving actual value-add business problems then this is a big red flag that investments might be resulting in “automation for automation’s sake.”

3. Drive to “the money” from the start and make it known

There is a maturity lifecycle that data-driven efforts take within most large corporations. In the early days, money is thrown at hiring talent, building out infrastructure and running new projects. Inevitably, the buzz eventually wears off and people start questioning the value of all these actions. Stakeholders want to see “the money.”

“The money” in this case can be cost savings, increased revenue, mitigated risks, improved customer satisfaction or some combination thereof—but eventually that all distills down into improved financial performance for a business.

Above we cited the case of the engineer who was being pressured to increase automation in order to keep the numbers reported to management looking good. This is also a good example of not driving to “the money.” Having more automation in the environment isn’t inherently a good thing nor will it automatically return the value that sponsors want.

The wrong metrics will drive the wrong behaviors. In our infrastructure scenario, someday someone is going to run the numbers and wonder why—despite extensive automation in the environment—the environments are still seen as broadly unstable, the number of P1 incidents hasn’t decreased and costs haven’t gone down. When that mismatch between action, reporting on that action, and “the money” becomes known, it can be a painful and expensive realization for organizations.

Get ahead of that by rigorously tracking “the money” from the beginning. What are you truly trying to accomplish within the operation and is that what you’re actually measuring?

Measurements like the ‘number of automated events’ or ‘% of activities automated’ might make for good headlines, but are they what you are really trying to influence within the business?

What if those incidents shouldn’t exist in the first place? Are the right actions being encouraged if automating something that should have been eliminated/avoided is counted as a win?

CKM Analytix and TK empower leaders to drive to “the money” from the beginning on a project, and quickly correct course on automation efforts that are failing to deliver. Contact us today for a demo on how we can turn your automation headaches into true value.

Let our TK Intelligence Engine monitor your systems and get a sense of what is happening to your business now. Contact us to help you re-calibrate.

CONTACT: Joe Malone, Chief Revenue Officer
M: +1 215 888 3224
E: joe@ckmanalytix.com
W: www.ckmanalytix.com

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Nicholas Hartman is the Chief Innovation Officer at CKM Analytix