Yemi Oluseun

‘Yemi’s starting point is that not every problem requires AI, and not every AI solution justifies the ethical risk.’

Part of the AI Leadership Index – a curated list of the most inspirational figures shaping the AI revolution.

Yemi Oluseun is Principal Consultant and Founder of The Change Hive, a boutique advisory that helps Financial Services, SaaS, and B2B service firms protect revenue and reduce churn through retention-led growth and AI-enabled Revenue Operations.

Over the past decade, she has led complex transformation programmes at organisations including JP Morgan, Barclays, UBS, Avalara, and Thredd, working at the intersection of customer experience, data, and technology. Today, she partners with private equity–backed and scaling firms to design operating models where AI, automation, and human expertise work together to drive predictable, profitable growth.

Alongside her consulting work, Yemi is an Executive MBA graduate of London Business School and holds an MSc in Computing, IT Law, and Management (Distinction) from King’s College London. She is currently pursuing a Doctorate in Business Administration at Grenoble Ecole de Management, researching organisational continuity in founder- and key-person-dependent accounting and advisory SMEs.

She is a regular contributor to Business Reporter (published in The Independent), and her firm, The Change Hive, has been recognised as a Top 100 UK SME by Elite Business.

Her mission is to help organisations move beyond AI experimentation and hype toward responsible, commercially grounded adoption that improves customer outcomes and revenue quality.

Please share a case study of how you have you innovated with AI over the last year?

In the past 12 months, Yemi supported LEAP Health in building an AI-enabled, retention-led growth engine. As their commercial mentor through the London Business School Entrepreneur Summer School, she helped the startup move from intuition to a measurable, AI-assisted operating model.

LEAP had begun as a feature-focused platform with limited customer insight and no structured retention model. Using her ‘5Rs of Retention Framework,’ Yemi introduced light-touch AI workflows to strengthen activation, engagement, and customer insight. These included AI-assisted onboarding summaries to shorten time to value, automated engagement workflows using behavioural segmentation, and AI clustering of testimonials and support messages to reveal clearer product and marketing insights. Engagement scoring was also introduced to identify upsell opportunities and detect churn risk.

Within weeks, LEAP’s first CPD webinar attracted 13 active dietitians, boosting authority and community-led retention. Conversion and engagement improved, and the team adopted KPI-driven decision-making, shifting from feature development to outcome-led growth.

The startup went on to win the Most Viable Startup award at the 2025 programme. The work was also recognised with a Commended Award at the ABM MPower Awards for excellence in commercial mentoring.

The project demonstrates how practical, responsible use of AI can help early-stage platforms build predictable and sustainable growth.

What is the biggest challenge you have faced with AI innovation and how you have overcome it?

One of the biggest challenges Yemi has faced in AI innovation is overcoming a mix of hype, fear, and weak data foundations in traditionally risk-averse organisations, particularly in financial services. Boards and investors often push for rapid AI adoption, while risk, legal, and frontline teams raise valid concerns around data quality, privacy, job impact, and unintended consequences. The result can be paralysis or fragmented pilots that never translate into real business value.

Yemi addresses this by reframing AI as revenue and retention infrastructure, rather than a “shiny object.” Her approach starts with a small number of high-impact, low-risk use cases tied directly to revenue protection, such as churn-risk visibility, onboarding automation, or improved Voice of Customer insight.

She works closely with risk, legal, data, and commercial leaders to define clear guardrails, establishing which data is in scope, where human oversight is required, and how decisions will be monitored. Just as importantly, she is candid about data readiness; in many cases the first step is improving data quality and governance so future models can be trusted.

Alongside technology implementation, Yemi places strong emphasis on change management and education, running leadership roundtables and team workshops that allow colleagues to explore AI through practical examples rather than theory.

This approach helps organisations move from scattered experimentation to responsible adoption, embedding AI into everyday Revenue Operations with clear ownership, measurable outcomes, and appropriate safeguards.

How do you balance innovation with ethical safeguards?

Yemi’s starting point is that not every problem requires AI, and not every AI solution justifies the ethical risk. She balances innovation with safeguards through a set of practical principles applied in every engagement:

Customer first: Prioritising use cases that clearly improve customer outcomes, such as faster resolutions, fairer decisions, or earlier risk detection, rather than experimentation for its own sake.

Data minimisation and governance: Using only the data genuinely required, clarifying ownership, and aligning with existing security and regulatory standards from the outset.

Human in the loop: Ensuring AI augments rather than replaces critical human judgement in areas such as pricing, credit, complaints, and complex retention decisions. Workflows are designed to keep people accountable.

Transparency and auditability: Where models influence customer treatment, there must be clear rationale, documented assumptions, and an auditable trail of decisions.

Culture and competence: Supporting leaders and teams in building AI literacy so they understand both its capabilities and limitations. Responsible innovation depends as much on culture and training as it does on technology.

How do you believe the artificial intelligence space will change over the next 12-24 months?

Over the next 12–24 months, Yemi expects AI to shift from standalone “projects” to embedded infrastructure within core business processes, particularly in Revenue Operations.

In practice, this will include AI copilots becoming standard within CRM, support, and finance platforms helping draft outreach, summarise accounts, and surface next-best actions for commercial teams. Organisations will also adopt more advanced customer health and churn prediction models, combining behavioural, financial, and sentiment data to provide earlier and richer signals to leadership.

At the same time, Yemi anticipates a stronger regulatory and governance layer, particularly across the UK and Europe, requiring organisations to demonstrate fairness, robustness, and appropriate human oversight in AI-driven decisions.

She also expects a shift in skills demand from specialist AI roles toward operators who can design and manage AI-enabled processes, including RevOps leaders, product managers, and transformation leads.

Ultimately, Yemi believes the organisations that succeed will treat AI not as a novelty but as disciplined infrastructure for running leaner, more resilient businesses, protecting revenue quality, strengthening customer trust, and freeing people to focus on higher-value work.