Key Stats at a Glance
A leading provider of employability services, with a mission to help people into work, faced a challenge: frontline staff were spending too much time on administrative tasks, leaving limited capacity to build meaningful relationships with clients.
As demand for services grew and competition intensified, the organisation identified AI as a key lever to improve efficiency and strengthen its competitive edge. Hudson & Hayes was engaged to turn this ambition into action.
The key issues were:
Without intervention, the organisation risked reduced job placement outcomes, lower staff satisfaction, and weaker competitiveness in contract bids.
Hudson & Hayes applied its proven approach, moving quickly from feasibility to delivery.
Key solutions delivered included:
By combining feasibility analysis, co-creation, and rapid delivery, Hudson & Hayes enabled the organisation to transform frontline operations with AI. The result: more time for client engagement, stronger job placement outcomes, and a sharper competitive edge in winning new business
Key Stats at a Glance
A PE-backed enterprise data management business, part of Five Arrows and historically founder-led, was entering a phase of accelerated global growth. To deliver at scale, the organisation needed to modernise how its client management and operations teams worked.
Legacy processes, manual workflows, and siloed organisational structures threatened to constrain growth just as investment expectations were increasing. To unlock efficiency, resilience, and sustainable value creation, the business turned to Hudson & Hayes for transformation support.
Key issues identified included:
Failure to address these challenges risked missed revenue opportunities, operational bottlenecks, and reduced attractiveness to investors.
Hudson & Hayes delivered a cross-functional transformation programme integrating process redesign, organisational design, and AI adoption.
The programme delivered significant impact:
Hudson & Hayes enabled this PE-backed enterprise data management business to move from founder-led ways of working to a scalable, investor-ready model. By redesigning processes, embedding AI, and strengthening organisational agility, the company is now equipped to deliver efficiently at scale while sustaining profitability.
The foundation built ensures not only near-term value creation but also long-term resilience, positioning the business for continued growth in competitive global markets.
The employability sector is facing growing demands to improve outcomes, increase participant engagement, and reduce the administrative burden on staff. AI and automation are providing solutions that optimise processes, free up resources, and ultimately enhance the participant journey. Below, we outline seven key use cases showing how AI is making an impact in employability.
Engagement is crucial for job success. AI enables personalised communication at scale, combining data analytics with AI to boost participant engagement. Automated systems can adjust messaging based on real-time feedback and engagement, helping participants stay on track and increasing their chances of success.
Missed appointments and rescheduling can be a drain on both participants and staff time. AI-driven scheduling tools automate the process, ensuring participants are reminded of their commitments and offering optimised appointment times based on availability and preferences. This reduces wasted meeting slots and improves efficiency across the board.
Participants often have recurring questions about job applications, CV building, interview preparation, or local service providers. AI-powered digital assistants can handle these FAQs, providing instant answers and resources. This not only saves time for staff but also ensures participants receive timely support.
AI can track participant behaviour and predict engagement levels, flagging early signs of disengagement. By analysing data, such as communication patterns and attendance records, AI identifies at-risk participants so that coaches can intervene earlier, improving retention and outcomes.
Using machine learning, AI can assess the likelihood of a participant achieving a job outcome. By assigning a job outcome score based on various data points, AI allows staff to focus their efforts on those participants who need the most support. This insight can also help in forecasting programme success and tailoring interventions more effectively.
In employability programmes, meetings and appointments generate significant amounts of notes and paperwork. AI can automate note-taking and update records in real time. For example, an AI-driven meeting assistant can take notes and enter data directly into the employability platform, saving time and reducing the administrative load for staff.
Post-placement support is essential to ensure participants remain in work. AI-powered systems can automate regular check-ins with participants who have found employment, offering support and identifying potential issues early. These automated systems can trigger human intervention if needed, helping sustain long-term job retention and success.
These seven AI use cases highlight the transformative impact that automation and AI can have in the employability sector. By streamlining admin tasks, predicting engagement, and enhancing participant support, AI helps organisations improve outcomes while freeing up time and resources to focus on what truly matters—supporting participants on their journey to sustainable employment.
The employability sector faces growing challenges in meeting the demands of participants, providers, and funders alike. With increasing administrative burdens, tighter budgets, and the pressure to deliver better outcomes, organisations are seeking innovative solutions to optimise their processes and enhance participant engagement.
AI and automation present a powerful opportunity to transform the employability landscape. By integrating these technologies, providers can streamline administrative tasks, personalise participant support, and make data-driven decisions that improve overall outcomes.
This white paper explores how AI and automation can help employability providers reduce admin, increase participant engagement, and improve job outcomes. It outlines key use cases, including automating participant communications, predictive tools to identify high-impact factors for job success, and optimising case management. Additionally, we examine the critical success factors necessary for a smooth AI implementation and provide actionable insights for realising the full benefits of these technologies.
The Labour government has set an ambitious target of increasing the employment rate from 75% to 80%, aiming to make it the highest in the G7. With over two million people expected to join the workforce, overcoming barriers to employment—especially for those facing health-related challenges or age-related barriers—is critical. The Restart Scheme and Work and Health Programme have laid a foundation by providing support services such as mental health and job training. However, to meet these ambitious goals, employability organisations must now embrace the full potential of AI, RPA, and Generative AI to streamline and enhance the support they offer.
Imagine a participant’s journey starting with a Digital Assistant powered by AI collecting their details before their first appointment. This eliminates time-consuming form-filling and allows participants to focus on meaningful interactions right from the start. Participants receive real-time responses to their questions, creating a seamless and engaging onboarding experience.
During their first appointment, participants feel fully supported as AI tools, instantly generate tailored action plans. This enables more personalised and productive conversations, while automated communications keep participants informed and engaged throughout their journey. RPA ensures that routine tasks such as form completion and appointment scheduling are handled efficiently, allowing coaches to focus on participant needs.
AI tools, especially Machine Learning, offer predictive insights into the likelihood of participants achieving their job outcomes. This data allows staff to take proactive measures when necessary, ensuring participants stay on track. Generative AI can further assist by automating complex tasks like meeting transcriptions and document creation, reducing manual effort. The use of these technologies improves job outcomes by enabling data-driven decisions and increasing participant engagement.
The integration of Machine Learning and Generative AI into the employability process allows for more effective, data-driven interventions, significantly increasing the likelihood of participants achieving their job outcomes. Predictive insights generated by AI empower staff to act when necessary, ensuring participants have the best possible chance of success.
By automating routine administrative tasks with RPA and offering personalised, real-time support through AI-powered Digital Assistants, participants remain more engaged and motivated throughout their journey. This enhanced engagement leads to more positive outcomes for participants and a higher overall satisfaction rate.
RPA automates repetitive tasks such as scheduling, form-filling, and meeting transcription, freeing up staff time to focus on more strategic activities. This reduces the administrative burden on employment coaches, allowing them to concentrate on the needs of participants and deliver higher-value services.
The future of employability lies in the integration of AI, RPA, Machine Learning, and Generative AI, offering a pathway to more efficient, effective, and engaging services. The Restart Scheme and other employment programmes have laid a strong foundation, but to meet the ambitious goals set by the Labour government, employability organisations must now leverage these technologies. By doing so, they will not only improve job outcomes but also increase participant engagement and reduce the administrative load on staff. The future isn't just about doing more—it’s about doing better, with AI and automation at the heart of this transformation.
An employability business sought to enhance customer experience and job outcomes by freeing up staff from repetitive administrative tasks. They wanted to improve efficiency and focus on high-value activities such as participant engagement and personalised support. To achieve this, the company aimed to leverage Robotic Process Automation (RPA) and Generative AI technologies to streamline their processes.
We conducted a comprehensive four-week review of the client’s processes, technology, and operational workflow. This review included interviews, site visits, and technology assessments to identify areas for improvement. Over 20 automation and AI opportunities were identified, focusing on utilising Robotic Process Automation (RPA) and Generative AI.
We ran a series of design sprints to quickly prototype and test AI solutions, resulting in a custom-built Proof of Concept (POC). The POC introduced a Digital Assistant to automate core business functions, such as participant communications and administrative paperwork. This not only streamlined daily operations but also empowered staff to dedicate more time to strategic tasks that would improve customer experience and job outcomes.
The POC delivered several identified benefits, including:
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