Here is a pattern worth examining: a mid-sized food manufacturer in Konya — around 295 employees, three production shifts, five supplier channels — ended 2021 with nine active bots on their RPA platform. Four of them were not actually in use. Two had been switched off after producing incorrect data. One was still labeled ‘in testing’ after fourteen months. When the management team looked at the numbers, they could not calculate the return on their investment. From the outside, this looks like a technology failure. From the inside, it was a strategy gap — and the distinction matters more than most automation vendors will tell you.When Gartner placed hyperautomation at the top of its strategic technology trends list in 2019, many organizations read it as: ‘Automate everything you possibly can.’ That is not what the concept describes. Hyperautomation is the coordinated use of multiple automation tools — RPA, process mining, low-code application development, business rules engines — working in concert. The word ‘coordinated’ is doing serious work in that sentence. Coordination requires knowing which process deserves priority and why, understanding which tools create value together rather than in isolation, and defining upfront how that value will be measured. Skipping these three steps and calling the result a strategy is one of the more common and expensive mistakes in enterprise technology today.The field pattern repeats itself with frustrating consistency. An automation journey typically begins with a purchasing decision. An IT team or external consultant presents a platform that promises to automate a significant share of existing processes. The decision-maker — often the owner or the general manager directly — scans the list and picks the highest-volume process: ‘Our people spend hours entering orders manually into the ERP. Let us start there.’ The selection may not be wrong. But the reasoning behind it is incomplete. Which process is operationally fragile? Where and why is error actually produced? Is the data sufficiently clean and consistent for a bot to work without constant intervention? What happens to the people currently running this process once the bot takes over? Without answers to these questions, every selection is a short-circuit candidate. In a project I worked on with a textile exporter in Gaziantep, the first process chosen for automation was the highest-volume one — but it was also the one with the highest exception rate. After eight weeks, the bot was generating a log file full of error records for a process that took a person two minutes a day on average but left the bot in a ‘waiting’ state for 43 percent of its runtime. Nobody had analysed those exceptions before the project began.Value measurement gets less attention than process prioritization, but it is equally consequential. Most hyperautomation programs set ‘time saved’ and ‘error reduction’ as their headline metrics at launch. These are not wrong targets, but they are insufficient ones. Genuine value measurement asks the follow-on question: once the bot is running and time is recovered, where does that recovered time actually go? Does redirecting human attention elsewhere create a productivity gain, or does it simply create slack? Returning to the Konya food manufacturer: the estimated time recovered by their bots was roughly 380 employee-hours per month. Nobody had tracked where that time went. One of the managers put it plainly: ‘People are doing less manual data entry, but we do not know what they are doing with that time instead.’ This is the classic picture of absent value measurement. Without tracking what recovered time produces, linking automation to business improvement is impossible. And a program that cannot demonstrate its improvement becomes indefensible at the next budget review.Missing capability planning creates the quietest but most lasting damage of the three. Hyperautomation programs routinely recommend establishing a Center of Excellence — a centralized structure with standard methodology, shared tooling, and institutional knowledge. On paper this is sensible. In practice, it requires specific roles to actually be filled: process analyst, bot developer, maintenance owner. In many Turkish SMEs, these roles collapse into one or two people, or they are handed entirely to an external consultant. When the consultant finishes the engagement and leaves, the organization is left unable to maintain, update, or extend its own bots. The one or two people left behind are simultaneously responsible for technical upkeep, process analysis, and reporting to senior management. Under that load, the program stalls. In a logistics services company in Izmir, I observed exactly this: eleven months after the program launched, three bots had fallen into disrepair and their teams had reverted to the old Excel routines. Staff preferred the spreadsheet over the bot — because there was no one inside the company to fix the bot when it broke.How does this picture reverse? Three concrete steps put the program on strategic ground. The first is replacing the automation inventory exercise with a process maturity map. Document which processes have clean data, well-defined rules, and a low exception rate. The best candidate for hyperautomation is not the highest-volume process — it is the most predictable one. The second step is defining value in two dimensions from the start. For every pilot, track both ‘time recovered’ and ‘where that time was redirected.’ Single-dimension measurement erodes a program’s defensibility within eight months. The third step is building the capability plan before signing the platform contract. Who inside the organization will sustain this system, what training will they receive and when, and at what point does external support end — these questions need answers before the ink is dry, not after the first bot goes live. Answering them after the contract is signed is like asking an architect to produce the foundation plan once the walls are already going up.Hyperautomation does not fail because the technology is immature. RPA platforms are well-documented, operationally tested tools by 2022. Failure comes from deploying those tools inside a strategy vacuum. The Konya manufacturer’s bots were technically functional. Strategically, they were producing noise. If your organization runs an automation program today and you can immediately answer ‘how many bots do we have’ but not ‘what have those bots demonstrably delivered,’ that program is currently moving toward a tool graveyard. Stopping to draw the map is worth more than adding the next bot — and it is the kind of decision that separates an automation program from an automation strategy.
This article was originally published in Turkish by Gökhan MERCANOĞLU on January 24, 2022. The English edition has been reviewed and edited by the author.