The TRN Edge Journey Transforming Your Recruitment Business Through Internal AI Capability Building

AI adoption is accelerating across the recruitment industry, but there is a significant difference between using AI tools and building a business that knows how to apply them effectively.

Recruitment businesses are experimenting with automation, improving individual workflows and discovering new ways to increase productivity. Yet many still lack the internal knowledge, leadership and structure needed to translate these improvements into lasting commercial value.

In a TRN World session, Gordon Stoddart of The Recruitment Network and Paul Smissen, Co-Founder and Director at We Are Zenith, explored why internal AI capability is becoming increasingly important and how recruitment leaders can move from isolated experimentation to a more structured approach to transformation.

The AI adoption gap is widening

Across recruitment, businesses are progressing at very different speeds.

Some have already developed sophisticated automation capabilities, including proprietary tools and systems. Others are taking their first steps, exploring how AI might support existing recruitment activities.

Most fall somewhere in between.

They may have introduced AI into certain processes, improved individual productivity or encouraged employees to experiment with new tools. However, this does not necessarily mean they have a coordinated approach to adopting AI across the organisation.

This distinction became apparent through 22 TRN workshops involving approximately 250 members, where businesses shared their approaches to AI and automation.

While many had made meaningful progress, a recurring gap was the absence of a clear strategy supported by internal skills and accountability.

For recruitment leaders, the challenge is no longer simply understanding what AI can do. It is developing a business that can identify the right opportunities, implement improvements and continue adapting as technology evolves.

Why experimenting with AI is not the same as transforming a business

Individual experimentation can deliver valuable productivity improvements.

A recruitment consultant might find a more efficient way to complete an administrative task. A marketing employee might use AI to support content creation. An operations manager might improve how information is analysed and reported.

These improvements matter, but their combined impact depends on how effectively they are connected to wider business objectives.

Without a structured approach, AI adoption can become fragmented. Different employees experiment independently, opportunities are assessed inconsistently and useful knowledge remains concentrated in a small number of individuals.

This is where organisational capability becomes important.

A business with strong internal AI competency can look beyond individual tasks and examine how its systems, workflows and departments operate together.

It can identify where automation is appropriate, establish priorities and manage improvements as part of its wider business strategy.

External specialists can help accelerate this process. However, relying exclusively on outside expertise can limit an organisation's ability to continue improving independently.

The longer term objective should be to develop the confidence and knowledge required to drive change from within.

Start with business problems, not technology

One of the most important principles in developing an AI strategy is to begin with the business challenge rather than the available technology.

The growing range of AI tools makes it easy to become distracted by new features and applications.

However, introducing technology without a clear purpose does not guarantee better performance.

A more structured approach begins by examining existing workflows and identifying where the greatest improvements could be achieved.

This means understanding how tasks are currently completed, where inefficiencies exist and which activities consume significant time or resources.

Recruitment businesses can then assess which opportunities are most relevant to their commercial objectives.

The enhanced Project Automate framework used within TRN Edge is designed around this principle.

Participants map the tasks and processes within their roles or functions, identify possible improvements and assess their potential impact on productivity and costs.

From this analysis, they select three or four priorities and prepare recommendations for their manager or leadership team.

This creates an important connection between learning and business performance.

Rather than introducing AI because a tool appears interesting, the organisation begins with a defined problem and a measurable reason to address it.

Building an AI blueprint for the recruitment business

A clear AI blueprint provides the structure needed to move from individual ideas to coordinated improvement.

It helps leaders understand where automation can create value, which initiatives should take priority and how responsibility for implementation should be distributed.

Importantly, this should not be treated as a separate technology exercise.

AI and automation decisions need to support the direction of the business.

Before committing to an initiative, recruitment leaders should consider several fundamental questions:

• Will this improvement help accelerate our business strategy?

• Could it strengthen our competitive position?

• Will it increase revenue, reduce costs or improve productivity?

• Do we have the internal capability to implement and maintain it?

• How will we measure whether the change has delivered the expected benefit?

These questions encourage a more commercially disciplined approach.

They also help prevent businesses from investing time in disconnected experiments that generate activity without delivering meaningful organisational improvement.

The objective is not to introduce AI into every possible task. It is to understand where AI can make the greatest difference and build a consistent approach to delivering that value.

The right people matter more than the right job titles

Developing internal AI capability does not necessarily require recruitment businesses to employ software developers or appoint highly technical specialists.

People from across the organisation can contribute to transformation.

Recruitment consultants, sales leaders, marketers, finance professionals and operational managers all possess knowledge of the processes and challenges within their functions.

The most suitable individuals are often those who combine business understanding with curiosity and a willingness to improve how work gets done.

They do not need to understand the technical detail behind every AI model.

They do, however, need sufficient digital confidence to explore solutions, understand existing workflows and help colleagues adapt.

Equally important is the ability to influence change.

An employee may be enthusiastic about AI but struggle to create meaningful improvements if they lack the authority or support to implement new ways of working.

For business leaders, selecting people to develop should therefore involve looking beyond seniority or technical expertise.

The strongest candidates are likely to be those who understand operational challenges, are motivated to solve problems and can encourage others to adopt better processes.

Why developing several AI champions can accelerate change

A single AI champion can provide a useful starting point.

That individual can build knowledge, identify opportunities and begin introducing improvements within their area of responsibility.

However, developing several people across different functions can create a more substantial organisational impact.

When two, three or four employees are building their capability simultaneously, the business begins to establish a network of internal change drivers.

These individuals can share learning, challenge existing processes and support each other in implementing improvements.

This also creates greater accountability.

Rather than depending on one technically confident individual, responsibility for AI adoption becomes more widely distributed.

For recruitment businesses with multiple departments or specialist teams, this can be particularly valuable.

Different functions will have different priorities, and employees closest to those processes are often well placed to identify the most relevant opportunities.

The objective is to create a business where AI capability is not confined to one person, but increasingly forms part of how teams approach operational improvement.

Turning AI learning into measurable business improvements

Training is most valuable when employees can apply what they learn directly to their work.

This is the principle behind the TRN Edge approach.

The programme combines formal learning with practical assignments based on real recruitment business challenges.

Rather than completing theoretical exercises, participants examine their existing responsibilities and develop improvements relevant to their organisation.

This could involve recruitment workflows, sales and marketing activity, operational reporting or the monitoring of business performance indicators.

The programme also encourages participants to measure potential improvements.

By mapping existing tasks and estimating the productivity or financial benefits of proposed changes, individuals can make a clearer business case for prioritising particular projects.

This provides greater visibility for managers and business owners.

It also helps participants understand how their developing skills contribute to wider organisational performance.

The initial assessment is particularly important because it provides a practical starting point for people who understand that AI offers opportunities but are unsure where to focus.

Breaking a broad transformation ambition into a small number of measurable initiatives makes change more manageable.

The real challenge is consistent adoption

Access to AI technology is not necessarily the main barrier to progress.

Many recruitment businesses already have employees using tools such as ChatGPT, Copilot, Gemini or Claude.

The challenge is ensuring these tools are adopted consistently and applied to meaningful business problems.

Without structure, enthusiasm can fade and experimentation may never progress into established working practices.

Developing capability therefore requires more than initial training.

Employees need opportunities to practise, apply their knowledge, engage with colleagues and receive support when they encounter challenges.

TRN Edge addresses this through a 14-month development structure, including monthly workshops and ongoing practical activity.

Participants complete a two to three-hour workshop each month, alongside approximately 15 to 20 hours of AI related activity connected to their everyday work.

This approach recognises that meaningful organisational change takes time.

It also shifts the focus from simply learning about technology to using that knowledge repeatedly within real business processes.

For recruitment leaders, the implication is clear: adoption needs to be managed as an ongoing business priority, not a one-off training exercise.

Building capability that stays within the organisation

Recruitment businesses have several options when deciding how to accelerate AI transformation.

They can recruit dedicated specialists, engage external consultants or develop the capabilities of their existing workforce.

Each approach can contribute to progress.

External expertise can introduce new ideas and help businesses implement improvements more quickly. Dedicated specialists can provide technical knowledge and leadership.

However, internal development offers a different advantage.

It equips employees who already understand the business to identify challenges and create practical solutions.

This can make the organisation less dependent on outside intervention for every improvement.

It can also create valuable career development opportunities.

One example discussed was Niche Recruitment, where an employee undertaking the programme had been appointed to a newly created automation and data executive role.

The appointment demonstrates how developing AI capability can contribute to changes in organisational responsibilities and create opportunities for employees to take ownership of digital improvement.

For leaders considering how to structure their businesses for the future, this highlights the connection between technology adoption and people development.

Understanding the commitment required

Building meaningful AI capability requires an investment of time and organisational attention.

A structured development programme can provide the framework, but employees also need the opportunity to apply their learning and influence how work is done.

This makes management support essential.

Participants need clear priorities, access to relevant business processes and the opportunity to engage with colleagues affected by proposed changes.

Their work should connect to genuine business needs rather than becoming an additional activity with little operational relevance.

TRN Edge combines this approach with a Level 4 AI and Automation Practitioner apprenticeship, integrating formal learning with recruitment specific resources and assignments.

The programme was described as benefiting from government apprenticeship funding, with employer contributions and eligibility dependent on individual circumstances.

For employers, the important consideration is not simply the financial cost of training.

It is whether they are prepared to support employees in dedicating time to improvements that could change how their business operates.

Funding and eligibility details should be confirmed against the current programme requirements before making a commitment.

From AI experimentation to sustainable competitive advantage

The recruitment businesses making the greatest progress with AI will not necessarily be those using the largest number of tools.

The more important distinction is how effectively organisations apply technology to business challenges.

That requires clear priorities, measurable objectives and people who understand how to implement improvements.

It also requires leaders to view AI as part of wider business transformation rather than an isolated technology initiative.

Developing internal capability gives recruitment businesses the opportunity to build on early experimentation and establish a more consistent approach to change.

As employees become more confident in identifying opportunities, mapping processes and implementing solutions, the organisation becomes better equipped to respond to new challenges.

The ultimate objective is not simply to become a business that uses AI.

It is to become a business with the knowledge, confidence and internal capability to keep improving how it operates.

Speaker: Paul Smissen - Co-Founder and Director, We Are Zenith

Host: Gordon Stoddart - CEO and Co-Founder, The Recruitment Network

For more information about We Are Zenith, visit their page here.