AI slopification: The true cost of low-quality AI implementation
AI slopification in business processes creates a costly debt cycle of rework, damaged customer trust and lost productivity that extends far beyond initial model access fees.
AI slopification costs compound quickly. Rushed implementation without proper governance, security and data controls leads to expensive rework, damaged customer relationships and productivity losses that exceed savings.
Quality debt cycle emerges from too much focus on speed. Enterprises prioritizing fast deployment over quality standards produce low-value outputs that require costly fixes, while disjointed AI use and poor vendor choices amplify financial penalties.
CIOs must establish quality frameworks. Quality standards are needed before, during and after deployment to help break the debt cycle and turn quality into a competitive advantage.
Budgeting for AI implementation isn't easy. Enterprises must pay for model access, but that is the tip of the iceberg. The costs quickly add up -- data preparation, integration, governance, security and employee training. Getting each one of those moving parts right takes time and resources. Enterprises that try to move too quickly and cut corners will find themselves mired in slop that comes at a price.
"The mindset of 'We can gain a lot of efficiencies in rushing AI out there' has ended up costing in…the loss of control and having to go clean all that up," Derek Bush, chief information security officer at cloud software company Infor, said.
Those costs can compound, creating a debt cycle. An enterprise spends money on low-quality AI implementation, resulting in quality penalties that accumulate and require additional investment to fix.
What do these "slopification" penalties look like, and how can CIOs help establish a framework for quality AI implementation to avoid a quality debt cycle?
Understanding AI slopification
Rushed AI implementation that results in low-quality output falls under the broad umbrella of the term slop.
"I wanted something that stood out and got engagement. I wanted something that was exceptional. I got something average. Now, that's best case with AI slop," Tim Sanders, chief innovation officer at G2, explained. "Then, the worst case is it didn't have the proper context, and then it confabulated."
Slop is the result of enterprises that cut corners to achieve efficiencies with AI or to embrace labels like "AI-native" before their competitors can. They move fast without considering if that speed will deliver a result with actual value.
"Creating software is now incredibly cheap," JP Beaudry, chief technology officer at online marketplace Pearl, said. "That doesn't mean that the software is functional, safe or that it actually addresses real customer problems."
During the heated hype of the AI experimentation phase, employees were given access to AI tools and told to play around with them. While that kind of learning does have value, it also has its downsides.
"It became a lot of individualized things that people were doing, which was okay, but then it became individualized things that were having impact on how a department operates," Bush said. "That is just not a good implementation of AI."
Disjointed use of AI and tokenmaxxing, completely unfettered from outcomes, is a recipe for runaway budgets with little to show for all that spending.
"We could struggle to manage at scale if we were to create 100 times more software without also learning from customers 100 times faster, testing the quality of the software 100 times faster, and making sure that our compliance systems can scale 100 times also," Beaudry said. "It all has to work in sync."
With the market for AI platforms and tools still in its early days, enterprise leaders might make choices that contribute to low-quality implementation. For example, they might choose the wrong vendor.
"Another way that people can save money in a way that's a little bit clumsy is committing to enterprise agreements that come with discounts before there's enough confidence that these are the right long-term choices," Beaudry said.
Or, lured by the power and promise of AI to make various skills more accessible, enterprise leaders might attempt to handle all aspects of AI implementation in-house.
"If you try and do everything yourself, you're going to end up spending a lot of money in some of the wrong areas," Bush said.
While AI slop is often in the eye of the consumer, it can just as easily be churned out internally at enterprises.
"One of the things I'll be looking at over the next year is the rise of the slopification of corporate work, where people are given tools like Claude," Sanders said. "They're put into these AI adoption programs where time spent and how often you logged in is being measured and rewarded...you're going to produce record levels of slop."
If you care about quality, AI slop has a huge rework price tag that may make you run in the red on AI.
Tim Sanders, chief innovation officer at G2
Financial penalties of AI slopification
Enterprise leaders might see the cost of poor AI quality right away, or it might take time for those costs to become apparent.
The rework
Rework is the easily recognizable, if not easily fixed, cost of low-quality AI implementation. Enterprise teams may need to scrap entire AI projects and start from the beginning to ensure the necessary data governance, security and implementation protocols are in place.
"If you care about quality, AI slop has a huge rework price tag that may make you run in the red on AI," Sanders said. "A lot of times people think about the gross savings…they don't think about the net savings after the rework has been accomplished."
Rework might be needed immediately, or the need might emerge over time if no one oversees ongoing quality monitoring.
"When you don't account for who is ultimately the business owner and who is ultimately going to be responsible for the operational upkeep, they can be very different resources. Then it will fail over time," Bush said. "It's a heftier cost to go start over or to basically go through the implementation again if you haven't accounted for that in the beginning."
Damaged customer relationships and brand image
Consumers are sensitive to what they consider AI slop. If the AI tools and services an enterprise sells to its customers are low-quality, it risks damaging customer trust. A lack of customer trust can spill over onto an enterprise's brand image. AI brand damage costs might not surface immediately, but they can add up.
"The sooner you can get feedback on something, the cheaper it is to remedy," Beaudry said. "And one of the slowest, longest feedback loops is when something is out into customers' hands, then that tends to be the most expensive way to discover that something doesn't meet a quality bar."
Productivity and talent losses
Over time, slop can drive up costs related to productivity and talent losses. Employee morale could become an issue, according to Sanders. "People don't like working at a company that has a mediocre product," he said.
Nor do people like working at a company where they constantly must fix AI slop generated by their managers and coworkers. That kind of work undermines the productivity and efficiency that AI supposedly delivers, and it can drive top talent to leave.
The CIO's quality governance framework
The sheer power of AI hype and the lack of understanding of how to use it make it likely that most enterprises have at least some slop to clean up. CIOs can lead the charge on cleanup and building a framework for quality implementation going forward.
The cleanup process will start with identifying slop and talking with the chief financial officer and other senior leaders. "Let's talk about what the financial impact of us carrying this risk is and then figure out what it takes to clean it up," Bush said.
To break the debt cycle, cleanup needs to take place alongside the establishment of a quality governance framework for ongoing AI implementation.
Pre-deployment. Defining enterprise AI quality standards before rushing into a new initiative can help control long-term costs. These standards include testing accuracy and bias, establishing appropriate data controls, and determining the value the tool is expected to deliver. "For us at Pearl, nothing goes into production without a hypothesis of value and then a measurement that will tell us whether the hypothesis is validated or invalidated," Beaudry shared.
During deployment. As enterprises roll out new AI initiatives, they need to monitor them for quality and have rollback mechanisms in place if quality thresholds are not met.
Post-deployment. Once an AI project has been deployed, enterprise teams need to monitor it for quality on an ongoing basis. Is it delivering value to customers and/or internal business stakeholders? Is the model drifting?
The quality ROI equation
Enterprises that need an AI ROI reality check can start with a simple formula. How much business value is an AI use case or tool generating? How much does implementation cost? Are there quality penalties that could arise if that implementation is done poorly? If the implementation and penalty costs outweigh the business value, the project will have low-quality outcomes that fail to deliver ROI.
As ROI dominates the conversation about AI implementation, CIOs can use simple AI ROI calculators to better understand potential outcomes. Enterprises are also increasingly developing their own internal metrics and dashboards to track AI costs and outcomes.
Quality as a competitive advantage
AI quality versus speed is a delicate balance to strike. But the low-quality AI financial impact emerging for many enterprises suggests that prioritizing speed over everything isn't the best answer.
"There's just no good proof behind 'move fast' with AI. It is an urgency that starts with investors. It enters the boardroom. It jumps from the boardroom to the conference room," Sanders said.
Looking back at the dot-com crash, Sanders pointed to the consequences of moving too fast. "Doing it right was better than doing it fast," he said. "The dot-com crash was filled with a lot of people like Webvan and eToys that did it really, really quick, and they were wrong."
The current era of AI investment might not be a mirror image of the dot-com bubble of the early 2000s, but there are enough parallels that enterprise leaders ignore them at their peril. There is an argument that technology investment with a foundation rooted in quality, rather than a thin veneer of hype over a mountain of slop, is a competitive advantage. CIOs and their executive peers can look for quality indicators in their AI initiatives, assess the rework needed and establish a quality implementation framework going forward.
Carrie Pallardy is a freelance journalist with experience writing in cybersecurity, technology and healthcare. She currently covers a wide range of issues relevant to today's CIOs and IT leaders.
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