For some IT and engineering staffing firms, the rapid evolution of AI can make it difficult to know where to begin. But adopting AI and automation doesn’t have to start with a complex initiative. Small, practical applications can deliver meaningful value while helping teams build the confidence and experience to take on more sophisticated projects. Database cleanup, duplicate updates, and routine reporting are good places to start. 

Sean Malapurkar, Founder & CEO of Recruit CRM, sees curiosity as an important starting point. His perspective, alongside examples from staffing and search leaders, highlights how focused improvements can reduce administrative work and give employees more time for relationships. 

Recruit CRM is a Silver Sponsor of the 2026 TechServe Executive Summit, taking place November 9–11 at the Hyatt Regency Huntington Beach Resort & Spa in Huntington Beach, California. 

What Staffing Tasks Should Firms Automate First? 

Internal work that happens frequently and follows a reasonably consistent process can be a promising starting point. Repeated copying, checking, formatting, and updating are common examples. 

Mert Mercan, Director, AI & Digital Ecosystem at division one, recommends beginning behind the scenes. 

“Find the work that is high volume, repetitive, internal, automate that first and get a feeling for it.” 

That approach creates room to learn before extending a workflow into client or candidate interactions. Examples include correcting database fields, preparing documents, and keeping related records consistent. 

AI and automation play different roles. Automation follows predefined steps, such as updating a deal when a vacancy’s status changes. AI can help interpret information, such as recognizing a candidate’s name within a database field. A workflow can use both: AI handles information that requires interpretation, while automation moves the result through the process. 

How Can AI Help Clean Up a Staffing Database? 

AI can help identify inconsistent information and support recurring cleanup, reducing manual corrections and improving the records recruiters use. 

Fred Hacking, Group Operations Specialist at Leader Engineering, began with a familiar frustration: candidate names appearing in block capitals or inconsistent formats. Those issues affected database quality and could carry through into candidate communications. 

His team introduced a nightly process to identify and correct capitalization problems. Hacking described the initial setup and refinement as taking only minutes. 

“So that is one issue that we never need to worry about again,” he said. 

The business value extends beyond tidier records. Consistent names help prevent awkward personalized messages and remove a correction recruiters would otherwise have to make repeatedly. 

Hacking also described connecting vacancies with associated deals so status changes could flow between them. Recruiters no longer had to maintain both records manually. An additional monitoring process identified updates that failed, helping surface issues for investigation. 

These examples show how a narrow improvement can address both administrative effort and data quality. 

Do Employees Need Coding Experience to Get Started? 

Coding experience is not always necessary for a focused first experiment. Understanding the task, describing the desired result, and reviewing the output are useful starting skills. 

Malapurkar emphasized that employees do not necessarily need a technical background to begin exploring. 

“You just need to have a curious mind and the ability to go back and forth with ChatGPT or Claude.” 

Jim Bright, Senior Vice President – Sales at GTN Technical Staffing, similarly described learning by building and refining small solutions. 

“It’s an iterative process. You start with a small thing.” 

His experience suggests that a manageable problem gives employees room to learn. Clarifying instructions, uncovering exceptions, and adjusting the workflow are part of that process. 

Starting with a limited task also makes it easier to decide when additional expertise is needed. A useful experiment should help reveal the work involved in maintaining a dependable solution. 

Should Staffing Firms Build Their Own AI Tools? 

Targeted tools can complement an existing staffing system, but replacing the core database introduces a much broader set of responsibilities. 

Malapurkar drew that distinction clearly. 

“I don’t think you’ll build everything yourself because it just doesn’t make sense.” 

He pointed to access controls, data security, backups, and infrastructure as reasons firms may continue buying their core systems while developing focused solutions around them. 

For a staffing executive, this provides a practical boundary. A workflow that prepares a report or checks for missing updates addresses a specific need. Rebuilding the system that stores candidate, client, and placement information requires ongoing ownership of much more. 

How Can Staffing Leaders Encourage Employees to Use AI? 

Employees may be more receptive when an improvement addresses a frustration they already recognize. Practical demonstrations make that benefit easier to understand. 

Malapurkar summarized the approach: “go to them with what’s in it for them.” 

Mercan described testing capabilities with selected users from different teams, then having those colleagues share successful results. That gives employees a practical example from someone who understands their work. 

Hacking described linking contractor schedules with commission projections. Recruiters had a direct reason to maintain accurate information, while finance gained better inputs for cash flow forecasting. 

Faster preparation, fewer duplicate updates, or clearer commission information gives people a reason to change their habits. Those benefits make adoption relevant to their daily work. 

How Should Firms Measure Their First Automation Project? 

Time, accuracy, and usefulness provide a clearer picture of an automation project’s value. A faster process delivers benefits when its results are dependable and the saved time supports better work. 

Robert Thompson, COO of Talance Group, explained the priority: 

“I’m just a big believer that our value comes from the human relationship and human conversations that we’re having with people.” 

Thompson also described an AI-generated report that omitted placements, demonstrating why outputs need checking against underlying records. 

For firms considering a first project, a small pilot offers a way to explore the possibilities. Tracking the time a task takes, the corrections required, and the experience of employees doing the work can reveal whether a change is worth expanding. 

Across the speakers’ examples, progress began with a recognizable problem and grew through experimentation. Firms don’t need to tackle their most complex challenges first. One dependable improvement can deliver immediate value while building the confidence and experience to pursue more advanced applications of AI and automation.