Across all industries, the push for AI is leading to investment in platforms and integrations with the potential to completely transform operations.
The less spoken part about this new wave of AI-centric digital transformation, however, is what happens when these investments never leave the ground, and why.
According to Gartner, more than 40% of agentic AI projects will be canceled by the end of 2027, not because of any fault in the technology itself, but due to failures in data governance.
The staffing industry is in a unique position right now; rather than acting with the urgency to adopt agentic AI systems, there is a potent opportunity to turn the focus inward and use AI preparedness as a means to clean up data sets and perfect internal processes.
By learning from the causes of these abandoned or scaled-back projects, staffing firms can build the foundation now in order to reap the benefits of AI tools when they’re ready.
What Staffing Firms Are Getting Wrong About AI Investments
The biggest misconception surrounding AI investment for staffing firms is the idea that it’s some kind of magic bullet, enabling companies to use technology to compensate for inefficient operations.
Unfortunately, AI has a tendency to expose and amplify those inefficiencies rather than correct them.
These AI struggles aren’t unique to staffing, but because staffing requires a lot of precise, personal data inputs to be able to perform many of its core functions, outputs can become incomplete or unreliable.
For staffing, optimal AI usage begins with something simple like generating job descriptions or talent communications, and extends to sophisticated reporting, intelligent candidate matching, and agentic applications like automated recruiters.
When staffing firms are faced with the prospect of deploying AI, they run into many stumbling blocks, including:
- Duplicate candidate records
- Inconsistent skill taxonomies
- Disconnected ATS and CRM systems
- Recruiter-specific workflows or data kept outside of the system of record
- Incomplete client data
- And more
Read more on the issues firms face when they supplement their ATS and CRM with spreadsheets here
When AI models operate from disorganized data sets, the results it produces are incomplete or inexact. In short, AI scales the quality of your operations, for better or worse.
What Staffing Agencies Should Be Doing Before Investing Further
There needs to be a fundamental shift in how staffing firms view AI investments. Rather than asking, “Which AI tools should we buy?” the question needs to be “Is our business ready for AI?”
The generally high rate of AI project failure may seem like a negative – and for staffing firms that have invested in technology they aren’t ready for, it could be – the reality is firms that are evaluating AI tools now can learn from those stumbling blocks early adopters are running into.
In many cases, the staffing industry was forced to tackle their digital transformation efforts in a hurry with the onset of the COVID-19 pandemic; formerly manual and in-person processes needed to be adapted quickly to a remote and digital environment. To its credit, the industry rallied to meet the moment.
The rush to adapt to this new digital-first environment also left many gaps in data governance and created process debt that the AI boom is now exposing. In order to take advantage of this technology, staffing firms can take this opportunity to perfect the digital transformation they started nearly six years ago, with clear-cut objectives.
By focusing on these areas, firms can set themselves up for success when the time to deploy AI becomes imperative:
- Standardize operating procedures for recruiter workflows
- Improve candidate and client data quality
- Connect core staffing systems
- Reduce duplicate records
- Establish data ownership (another benefit of quality SOP adoption)
- Create governance around business-critical information
According to Gartner, 80% of data and analytics governance initiatives will fail by 2027 unless organizations tie these efforts directly to business outcomes. This number is daunting, but it’s important to understanding the stakes of AI investment.
In the past, these data governance initiatives may have felt like a nitpicky IT request; you do it because you’re supposed to, without fully understanding the business value of your efforts.
Staffing agencies that adopt this mandate and execute it successfully will be rewarded; improving data makes every future AI investment more valuable.
What Staffing Firms Stand to Gain From Improved Data Practices
The push towards data readiness unlocks the potential of AI investments, but it also helps organizations improve their day-to-day operations.
Clean, organized data is a necessity for everything from more accurate reporting and improved forecasting to stronger recruiter communications and better candidate matching.
Many staffing firms are sitting on mountains of data that may not be fully actionable because it’s not housed within an interconnected ATS or it’s disorganized within the system.
A major goal of staffing firms is to take their database and leverage it effectively, so every hire isn’t a net-new proposition (which can cost tens of thousands of dollars every year on job boards and the associated time it takes to manage listings). Setting a foundation of sound SOPs and data practices can unlock the potential of the information firms have on hand and have been amassing over years of operation.
Data preparedness isn’t the most exciting initiative, but the outsize benefit it can have to staffing firms really can’t be understated.
And, as Gartner predicts, 15% of day-to-day work decisions will be made autonomously by agentic AI by 2028, and 33% of enterprise software applications will include agentic AI by 2028, compared with less than 1% in 2024.
The reality is that AI is coming, but there is really no rush to invest if there isn’t a sturdy foundation of data for it to stand on.
This first wave of AI investment has been eye-opening in many ways.
Not only was the staffing industry exposed to the potential of AI and how it could impact everything from talent communications to candidate matching and robust business insights, but we learned what roadblocks are to this technology’s success.
The takeaway from AI project cancellations isn’t that businesses should delay AI adoption, it’s that they now have the benefit of learning from the first wave of implementations.
For staffing firms, that means focusing on data quality, system integration, and process consistency before expecting AI to transform their business.
AI isn’t a shortcut around operational excellence; it’s the multiplier that makes operational excellence pay off.
If you’re interested in getting started with AI but you’re not sure if your business is operationally ready, reach out to TempWorks to schedule a consultation