AI Pilot The One Workflow, One Outcome Approach

Recruitment businesses are investing more time and energy into AI, but the commercial impact is not always keeping pace with the level of activity.

New agents are being built, automation tools are being tested and individual processes are becoming more efficient. Yet many businesses still struggle to demonstrate how these developments are improving productivity, increasing placements or contributing to profitability.

In a TRN session, James Osborne, Co-founder of The Recruitment Network, outlined a more disciplined approach to AI implementation: focus on one workflow, establish one measurable outcome and prove its value before expanding further.

For recruitment leaders, the priority is shifting from experimenting with AI to making it deliver measurable business results.

Why AI Activity Does Not Always Translate into Business Performance

One of the biggest challenges facing recruitment businesses is the tendency to introduce AI across multiple activities simultaneously.

Businesses may develop CV formatting tools, candidate sourcing agents, automated communications and reporting systems, all while exploring new AI platforms and capabilities.

Each initiative may have individual merit, but introducing numerous tools without a clear execution strategy makes it difficult to understand which investments are genuinely improving performance.

Three factors are particularly important: measurement, adoption and scalability.

If a business has not established what success looks like before implementing a workflow, it cannot reliably assess the results. If recruiters do not consistently use the technology, potential efficiency improvements remain unrealised. And if a successful experiment cannot be integrated into everyday operations, its value remains limited.

The answer is not necessarily to introduce more technology. It is to become more selective about which processes deserve attention and more disciplined about measuring their impact.

Start with the Workflow, Not the Technology

A productive AI strategy begins with understanding how work currently happens across the business.

Recruitment leaders should examine the activities their consultants and support teams undertake, identify where time and resources are being consumed and determine which processes offer the greatest opportunity for improvement.

These activities can be divided into three categories.

Fully automated activities are processes AI can complete with minimal human intervention. Examples include interview scheduling, reminders, CV formatting and administrative updates.

AI supported activities are processes where technology prepares or develops the work, but a recruiter retains responsibility for reviewing and approving the final output. Candidate shortlists, outreach messages and marketing content are examples.

Human activities are those where direct involvement remains essential, particularly the work that depends on relationships, judgement and personal interaction.

The purpose of automation is to create more capacity for the third category.

Recruiters should be spending more time on valuable conversations, developing client relationships, understanding candidate requirements and generating business opportunities.

An effective workflow removes unnecessary administration without reducing the quality of these interactions.

Prioritise Workflows That Can Influence Revenue and Profitability

Not every automation opportunity deserves the same level of investment.

Before selecting a pilot, recruitment leaders should assess the potential value of each workflow against several criteria:

• How much time could the workflow save?

• How directly could it influence revenue generation or profitability?

• Is the required data available and sufficiently organised?

• Can current performance be measured before implementation?

• What risks or operational challenges could arise?

• Do employees recognise the problem and want the proposed solution?

This assessment helps distinguish commercially valuable opportunities from technology that is interesting but unlikely to influence business performance.

Suitable initial pilots might include re-engaging dormant candidates, following up with prospective clients who have not responded, automating interview scheduling or identifying new hiring opportunities.

These processes are specific, repeatable and capable of producing outcomes that can be measured.

By contrast, attempting to introduce AI across an entire recruitment business as a single project creates unnecessary complexity.

The first objective should be to demonstrate that one carefully selected workflow can deliver value.

The Commercial Opportunity Already Sitting in Your CRM

Candidate database utilisation provides a practical example of how a relatively focused workflow can make a difference.

In one recruitment business, an analysis of the previous 18 months identified 421 placements.

Of those placements, 366 candidates had been sourced through job boards or LinkedIn. Further analysis revealed that 285 of those candidates were already recorded in the company's CRM.

This indicated that recruiters were frequently returning to external sourcing channels to identify candidates who were already available within their existing database.

The business responded by introducing an automated matching workflow.

When a new vacancy entered the business, the workflow searched existing CRM records for potentially suitable candidates before recruiters began looking externally.

The objective was not to replace the recruiter's decision about candidate suitability. It was to improve the order and efficiency of the sourcing process.

This illustrates an important principle: AI does not need to introduce an entirely new capability to create value. It can improve the way a recruitment business uses the information and resources it already possesses.

Define One Workflow and One Outcome

Once a workflow has been selected, its purpose should be expressed in a single sentence.

That sentence needs to establish who will use the technology, what it will do and what measurable improvement the business expects to achieve.

For example:

Our permanent recruitment team will use an AI supported CRM re-engagement workflow to increase interviews generated from existing candidate records from six to 12 within the next six weeks.

This creates a clear commercial objective.

The business is no longer simply introducing an AI candidate engagement tool. It is attempting to improve a specific recruitment performance measure within a defined period.

The same principle can apply to business development.

A workflow might aim to generate 25 qualified leads each week, or free sufficient consultant capacity to complete three additional client meetings.

The important distinction is that the outcome can be assessed against an established baseline.

A disciplined pilot should have one workflow, one platform, one accountable owner, one pilot team, one primary performance measure and one agreed implementation period.

Keeping these elements focused reduces complexity and makes it easier to establish whether the technology is working.

Build AI Around Existing Working Practices

The development of AI skills, plugins and connected applications is creating opportunities to link individual AI capabilities into broader business processes.

Rather than maintaining separate agents for isolated tasks, businesses can increasingly connect activities across their existing systems.

For example, an AI supported business development workflow could review previous correspondence, identify prospects who have not responded and prepare personalised follow-up messages for a consultant to approve.

Similarly, connected calendar and email analysis can help identify missed correspondence, assess how time is being allocated and support more effective daily planning.

The choice of technology should be influenced by where the business already holds its information.

A recruitment company operating heavily through Outlook, Teams and SharePoint will have different practical considerations from one using other platforms and data environments.

Introducing another tool should not unnecessarily complicate an employee's working day.

The aim is to make useful AI capabilities available within established processes, reducing the friction that often prevents adoption.

Design Governance and Human Oversight into the Pilot

Commercial value should not come at the expense of control.

Before introducing an automated workflow, recruitment leaders need to understand where information is coming from, how it is being processed and which systems can access it.

Appropriate data protection and security arrangements must be in place, particularly when handling candidate and client information.

Workflows should also include relevant approval points.

For example, AI generated leads should be checked for quality before being treated as legitimate business opportunities. Draft communications should be reviewed before distribution where human approval is required.

Outputs should be recorded and assessed so the business can identify errors, review quality and make improvements.

There should also be an alternative process available if the automation fails or delivers unacceptable results.

These controls are part of designing a dependable workflow, not obstacles to implementation.

The ability to demonstrate sound AI governance may also become increasingly important when responding to client concerns and participating in tender processes.

Employee Adoption Determines Whether the Pilot Succeeds

Even a technically effective workflow will struggle to deliver value if employees do not use it.

Resistance can arise for several reasons.

Some recruiters may be concerned about how AI will affect their roles. Others may see the technology as another system to learn or an additional task in an already busy working day.

There may also be understandable uncertainty about whether AI generated outputs are accurate or useful.

A focused pilot creates an opportunity to address these concerns before wider implementation.

Testing the workflow with a small group of employees, including both confident AI users and more sceptical colleagues, provides a balanced view of how the technology performs in practice.

The objective is to establish whether the workflow genuinely helps recruiters do their jobs more effectively.

Human review, clear ownership and integration into familiar systems can all contribute to stronger adoption.

The workflow should become part of how the team operates rather than another disconnected tool competing for attention.

Measure Commercial Outcomes, Not Just Time Saved

Time savings are useful, but they do not automatically translate into improved business performance.

A workflow might save a consultant several hours each week. However, if that additional capacity is absorbed by other low-value activities, the commercial benefit may be limited.

The more important question is what happens with the time released.

If automation enables a recruiter to conduct three additional client meetings each week, that represents a measurable change in business development activity.

If a candidate engagement workflow increases interviews generated from existing CRM records, that provides a practical indicator of improved database utilisation.

And if an AI supported shortlisting process improves vacancy fill rates, the business can evaluate its contribution to recruitment performance.

A useful pilot scorecard should capture the starting position, intended target and actual results.

Measures might include qualified leads, interviews booked, client meetings completed, conversion rates or fill rates.

Results should be reviewed weekly throughout the pilot, alongside checks that the quality of work has not deteriorated.

The purpose is to establish whether AI is improving the measures that contribute to revenue, productivity and profitability.

Test, Improve, Scale or Stop

A four to six-week pilot provides a focused period in which to develop, test and evaluate a workflow.

Before launch, the process should be tested against real examples to establish whether it performs as expected. An initial test using 10 genuine cases can help identify practical issues before wider adoption.

Once the pilot is operating, the business should review results against its baseline and target.

There are three possible decisions.

Scale: If the workflow is producing the intended results, introduce it to more employees or teams.

Improve: If the workflow has potential but performance is inconsistent, refine the process and test the changes.

Stop: If it is not contributing sufficient value, discontinue the workflow and redirect attention towards a more promising opportunity.

This approach prevents businesses from continuing to invest time in technology simply because it has already been developed.

It also creates a repeatable method for building a portfolio of effective AI workflows, each contributing to a defined business objective.

What Recruitment Leaders Should Do Next

The immediate priority is to move from broad AI ambitions to a focused implementation plan.

Start by reviewing the processes within the business and identifying the three workflows most likely to improve commercial performance.

Select the strongest opportunity and define the intended outcome in one sentence.

Establish the current baseline, assign an accountable owner and select a small group of employees to test the workflow.

Confirm the necessary data connections, safeguards and approval processes before development.

Test the workflow using real examples, then measure its results throughout the pilot period.

Once the business understands what has worked, the same disciplined approach can be applied to the next opportunity.

The long-term value of AI in recruitment will not be determined by how many agents a business creates or how many platforms it adopts.

It will be determined by whether technology improves the work that matters.

For recruitment leaders, that means building practical workflows that release consultant capacity, strengthen execution and produce measurable improvements in business performance.

One workflow, one outcome and a clear understanding of the commercial value it creates.

Speaker: James Osborne – Co-Founder, The Recruitment Network