8617-701 Assignment Example

Developing a high-level business case is the skill that separates senior leaders who propose change from those who actually secure approval for it. Unit 701 demands more than a good idea — it requires you to establish the strategic rationale, develop genuinely distinct options, present rigorous financial analysis, persuade stakeholders, and design a change strategy that accounts for the complexity of real organisations. This is strategic leadership in its most practical form: building the argument, marshalling the evidence, and winning the mandate to act.

This assignment example follows a chief operating officer (COO) of a 280-person UK recruitment firm through the development of a business case for implementing an AI-driven candidate matching platform — a transformational investment that challenges the firm’s established operating model while addressing an existential competitive threat.

Learning Outcome 1 — Understand the Strategic Context for Change

AC 1.1 — Establish a Business Need That Takes Full Account of the Drivers for Change and the Strategic Fit to Organisational Goals and Priorities

The firm operates across three divisions — permanent recruitment, contract/interim, and executive search — serving the technology, financial services, and healthcare sectors. Revenue in 2024-2025 was £42 million, generated by 180 consultants operating from six UK offices. The firm’s strategic plan (2024-2027) identifies three priorities: grow revenue to £55 million, improve consultant productivity from £233,000 to £280,000 revenue per consultant per annum, and achieve net promoter scores (NPS) of +50 from both clients and candidates (current: +32 client, +28 candidate).

External drivers. The recruitment industry is experiencing structural disruption. AI-powered recruitment platforms — both specialist entrants (Beamery, Eightfold AI, HireVue) and generalist technology providers (LinkedIn Recruiter’s AI features, Indeed’s Smart Sourcing) — are fundamentally changing how employers identify and assess talent. A Bullhorn (2025) industry survey found that 64% of UK recruitment firms now use some form of AI in their candidate matching processes, compared to 23% in 2022. The firm uses none. Clients are beginning to question why they should pay a 20% placement fee for a process that technology can increasingly automate. Two major clients explicitly referenced AI capability in their 2024 preferred supplier reviews — a signal that AI is transitioning from competitive advantage to competitive necessity.

Internal drivers. Consultant productivity has plateaued. The average time-to-shortlist (from receiving a vacancy to presenting a qualified shortlist) is 8.2 working days — significantly slower than the 3.5-day benchmark reported by firms using AI-assisted matching (Staffing Industry Analysts, 2024). This delay costs revenue: every additional day a vacancy remains open increases the probability that the client fills it through another channel by approximately 4%. Additionally, consultant time is disproportionately consumed by administrative sourcing activity — database searching, CV screening, and initial qualification — rather than the relationship-building and advisory work that clients value and that justifies the firm’s fee premium.

Strategic fit. The business need — implementing AI-driven candidate matching — aligns directly with all three strategic priorities. Revenue growth requires winning and retaining clients who increasingly expect technology-enabled service. Productivity improvement requires automating the sourcing and screening tasks that consume consultant time without generating proportional value. NPS improvement requires faster, more accurate shortlists that demonstrate the firm understands the client’s requirements and the candidate’s aspirations. The business case does not propose AI as a replacement for consultants but as an enabler that allows consultants to focus on the high-value advisory work that differentiates the firm from technology-only competitors.