Build Your AI Execution Plan 90 Days to Live Implementation

Artificial intelligence is creating significant opportunities for recruitment businesses, but adopting new technology is not the same as implementing an effective AI strategy.

For recruitment leaders, the challenge is determining where AI can deliver measurable improvements, how it should fit into existing workflows and what impact it should have on commercial performance.

In this TRN World session, James Osborne, Co-founder of The Recruitment Network, explored how recruitment businesses can move from experimenting with AI tools to implementing a structured strategy focused on performance, productivity and profitability.

The starting point is straightforward: AI should solve commercial problems, not simply introduce new technology into the business.

Start with profitability, not technology

One of the biggest risks in AI adoption is focusing on what the technology can do before establishing what the business needs to achieve.

Recruitment businesses already have multiple opportunities to improve commercial performance. These include increasing job flow, improving fill rates, increasing average fees, reducing operational costs and generating more revenue from existing clients.

AI should support these objectives rather than operate as a separate initiative.

Consider a permanent recruitment business receiving 80 assignments annually, filling 50% of them and generating an average placement fee of £9,500.

If the business increases its assignments to 100, improves its fill rate to 62.5% and raises its average fee to £10,500, the combined improvements can produce a substantial increase in revenue.

In the example presented, these changes would double operating profit before tax, assuming other financial factors remain unchanged.

The important principle is that doubling profitability does not necessarily require doubling headcount or workload.

Incremental improvements across several commercial measures can produce significant results.

For recruitment leaders, this changes how AI opportunities should be assessed. Rather than asking which tools to introduce, the more valuable question is which areas of the business need to perform better and how AI could contribute.

AI should create more time for human recruitment

The real opportunity with AI extends beyond completing existing tasks more quickly.

Recruiters spend considerable time on administration, information gathering, documentation and repetitive processes. While these activities remain necessary, they can restrict the time available for client relationships, candidate engagement and business development.

AI provides an opportunity to change that balance.

By transferring appropriate tasks to AI assistants, agents and automated workflows, recruiters can concentrate more of their working day on activities where human involvement creates genuine value.

That includes meeting clients, developing relationships, understanding hiring requirements, assessing candidates and winning new business.

The outcome should be a more human recruitment experience, not a less personal one.

This also raises questions about how recruitment businesses manage performance.

If technology removes substantial amounts of administrative work, leaders may need to reconsider consultant expectations, productivity measures and commission structures.

The objective is not simply to save hours. It is to ensure that the time released is reinvested in activities that improve commercial results.

Map your workflows before building AI solutions

A practical AI execution strategy begins with understanding how work is currently completed.

Rather than introducing disconnected tools across the organisation, recruitment leaders should break down their existing workflows into individual tasks.

This applies across recruitment delivery, business development, account management, marketing, management and back office operations.

Once those tasks are visible, each can be assessed against three possible approaches.

Keep it human. Activities that rely on relationships, judgement and meaningful personal interaction should remain with employees.

Automate it. Repetitive tasks that follow a defined process may be suitable for automation.

Augment it with AI. Some activities should remain human-led but can be completed more effectively with AI support.

This distinction is important because not every task needs the same solution.

Automating a process completely is different from using AI to improve the quality of an employee's work.

The workflow mapping process also creates a basis for measuring results. Leaders can identify where time is being saved, where costs are being reduced and where improvements in productivity are translating into greater profitability.

Without this foundation, businesses risk investing in AI capabilities that generate activity without delivering meaningful commercial value.

Build around the strengths of your people

AI implementation also creates an opportunity to reconsider how responsibilities are distributed across a recruitment business.

Employees deliver the greatest value when they spend more time on activities that match their strengths and contribute directly to performance.

For some recruiters, that may be developing new client relationships. For others, it may be candidate engagement, account development or managing complex assignments.

The challenge is that valuable employees often spend significant amounts of time on activities outside those areas.

A useful starting exercise is to examine the regular tasks undertaken by each role and assess which genuinely require that individual's expertise.

Activities that do not make effective use of those strengths can then be considered for delegation, automation or AI support.

This is particularly relevant for recruitment business owners and managers, whose time can be consumed by operational tasks rather than strategic decision-making.

By reallocating work more effectively, businesses can improve individual productivity while giving employees greater scope to focus on the activities they perform best.

Treat AI agents as part of your operating model

As AI becomes more capable, recruitment companies can begin to treat AI agents as functional components of their workforce.

An agent should have a specific responsibility, a clearly defined process and a measurable outcome.

For example, one agent might support CV formatting, another could identify potential new business opportunities, while another monitors candidate redeployment opportunities.

The important consideration is whether each agent is improving performance.

A central AI agent hub provides a way to organise these capabilities and monitor their effectiveness.

Instead of measuring how many agents the business has developed, leaders should evaluate outcomes such as:

• Improvements in fill rates.

• Reduced time to submit candidates.

• Increased redeployment rates.

• Lower cost to serve.

• Additional business development opportunities.

This approach moves AI away from experimentation and towards operational accountability.

It also changes how leaders think about workforce planning.

Future recruitment businesses may operate through a combination of internal employees, offshore teams and AI agents, with each contributing to the overall delivery model.

The objective is to increase the productive capacity of the organisation, rather than automatically replace employees.

Use AI to identify business development opportunities earlier

One of the most commercially valuable applications of AI is helping recruiters identify potential hiring requirements before vacancies enter the wider market.

Traditional recruitment business development often involves approaching organisations after a job advertisement has been published.

By that stage, multiple recruitment suppliers may already be competing for the opportunity.

A more proactive approach involves monitoring hiring triggers.

These are business developments that may indicate future recruitment demand, such as investment announcements, acquisitions, office expansions, IPO preparations or relevant legislative changes.

Different markets and client profiles will have different triggers.

AI agents can be configured to monitor the signals most relevant to a recruitment company's target clients and identify organisations that may be preparing to recruit.

For example, an agent monitoring office lease activity could identify when a target company takes on larger premises, creating a potential reason for a recruiter to begin a conversation about future hiring plans.

The opportunity is to engage clients 30, 60 or 90 days before vacancies are advertised.

AI identifies the signal, while the recruiter uses their commercial understanding and relationships to develop the opportunity.

Recruitment businesses can also create shared lead management processes where AI-generated opportunities are distributed to consultants for follow-up.

This makes AI part of a structured business development strategy rather than simply another source of market information.

Get more value from your existing systems and data

Before introducing additional AI platforms, recruitment leaders should assess the technology and information already available within their business.

The CRM is an important starting point.

Recruitment companies invest heavily in these systems, yet the value of that investment depends on how effectively their capabilities are used.

Understanding existing CRM functionality, including available AI features, should form part of the wider implementation strategy.

AI assistants can also help businesses extract more value from data that is already available.

For example, an assistant connected to an email system can review historic client correspondence, identify contacts who have not responded and prepare personalised follow-up drafts for consultant approval.

This creates an opportunity to revisit conversations that may otherwise have been forgotten.

Similar applications can support calendar management, meeting preparation and daily prioritisation.

LinkedIn data can also provide useful insights. By analysing exported account information, recruiters can better understand previous content performance, connections and conversations.

The underlying principle is that recruitment businesses often have valuable information sitting within existing systems.

AI can help turn that information into more useful actions.

Move from individual AI tools to business intelligence

AI maturity develops as businesses move beyond isolated assistants and individual agents.

A useful progression involves three stages.

Co-pilot: AI assists employees in completing tasks, analysing information and preparing work.

Autopilot: AI operates through scheduled activities and predefined triggers, reducing the requirement for manual intervention.

Mission control: AI brings together information from multiple systems and agents to provide a more comprehensive view of business performance.

At the mission control stage, recruitment leaders can receive consolidated information covering sales pipelines, financial performance, consultant productivity, AI agent effectiveness and new commercial opportunities.

A daily management briefing might highlight deals that have stalled, emerging hiring triggers, revenue opportunities and actions requiring attention.

For consultants, a similar system could provide new leads, relevant market developments and recommended business development priorities.

The value comes from connecting information that would otherwise sit across separate platforms and turning it into actionable insight.

This creates the potential for better informed management decisions and greater accountability across the business.

AI can support new recruitment products and recurring revenue

Improving internal efficiency is only one side of an effective AI strategy.

Recruitment businesses should also consider how AI could improve the services they deliver to clients and create additional commercial opportunities.

One example involves making better use of shortlisted candidates who were unsuccessful in securing a particular role.

These candidates may have completed extensive assessment processes, demonstrated strong suitability and remained open to new opportunities.

Rather than allowing that candidate value to be lost, an AI-supported platform can organise anonymised profiles and make them available to relevant clients.

Clients can review available talent and request additional information where there is potential interest.

For the recruitment business, this creates an opportunity to generate additional placements from work already undertaken.

It can also support a different commercial model, with clients potentially paying a recurring subscription to access a curated candidate platform.

The wider lesson is that AI can help recruitment companies develop services beyond traditional placement activity.

This could create additional value from existing candidate relationships while supporting more predictable revenue streams.

Turning AI strategy into implementation

An effective AI execution plan should connect technological capability with commercial priorities.

The starting point is identifying where the business needs to improve, followed by mapping the workflows and tasks that influence those outcomes.

From there, leaders can determine what should remain human, what can be automated and where AI could improve existing processes.

Development should then focus on agents and tools with specific responsibilities and measurable contributions.

As those capabilities become established, businesses can connect their systems, introduce automated workflows and develop more comprehensive management intelligence.

Throughout the process, AI performance should be assessed against business results rather than the volume of tools deployed.

Recruitment companies should also recognise that AI implementation is an organisational change, not simply a technology project.

It influences how employees spend their time, how responsibilities are distributed, how productivity is measured and how services are delivered.

Ultimately, the success of an AI strategy depends on whether it enables the business to operate more effectively, create greater value for clients and candidates, and improve profitability.

The strongest AI execution plans begin with a clear commercial objective and use technology to help achieve it.

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