The keynote speech was given by Professor Michael Mainelli, a renowned economist, President of the London Chamber of Commerce and Industry (LCCI), who served as Lord Mayor of London from 2023-24 and is an international ambassador for the UK’s professional & financial services sector. He shared estimates that 25% of the world economy is based on checking to make sure people do what they say they do! He referenced roles of quality, standards and AI for better economic growth.

equitably.ai CEO, John Paul, Chartered Quality Professional and AI Lead for the CQI Digital Transformation Special Interest Group at the Chartered Quality Institute, moderated a panel as a follow on from his feature article in the Quality World Magazine, titled: Maximising AI’s Impact Using Quality Competencies on the role of professional quality competencies in quality AI with a panel of experts from investment management – Rashid Adam,  COO Findlay Park Partners LLP, legal – Simon Bollans, Partner Stephenson Harwood – and data consultancy – Richard Corderoy, CEO Oakland. Their shared view? Without rigorous quality competencies, even the most promising AI pilots struggle to deliver sustainable value.

Panel discussed how organisations invest heavily in AI, yet surveys show that only around 30% of AI initiatives scale beyond the pilot stage. This “pilot paradox” underscores a critical truth: the quality of AI pilots determines whether small‑scale AI  proof‑of‑concepts lead to a successful enterprise‑grade solution. Poorly defined, governed or inadequately tested AI projects can erode trust, inflate costs and stall digital transformation.

The panel – and at equitably.ai – discussed how every AI endeavour, from initial experiments to full production, when underpinned by the five professional quality competencies of the Chartered Quality Institute, can ensure AI pilots scale and AI programmes deliver ROI with acceptable risks:

1. AI Context: Assess regulatory requirements of the industry companies operate in, and data maturity and digital readiness before selecting use cases. This groundwork ensures the quality of AI deployments by aligning technology with real business needs.

2. AI Leadership: Secure executive sponsorship, define clear roles and champion AI adoption across the organisation. Strong leadership drives accountability for the quality of AI projects and embeds an AI‑ready culture.

3. AI Improvement: Establish measurable KPIs for efficiency, accuracy and ROI. Continuous performance tracking and iterative refinement safeguard the quality of AI deployments over time.

4. AI Assurance: Conduct repeatable tests—such as bias audits and consistency checks—to verify reliability. Robust assurance processes elevate the quality of AI pilots, building stakeholder confidence.

5. AI Governance: Implement AI policies, risk‑tolerance thresholds and guardrails to manage compliance and security. Effective governance underwrites the quality of AI projects at scale.

AI Quality Case Studies from the Panel

Agentic AI in Investment Management

Rashid Adam, COO of Findlay Park Partners, talked about using quality methods such as process mapping in investment research together with Agentic AI to transform investment decision‑making. By using AI to sift through earnings calls and broker notes, investment analysts at his firm have been freed up to focus on high‑value insights, demonstrating that pilot success hinges on a process focus, use‑case alignment and rigorous improvement discipline.

Governing AI in Legal Services

Simon Bollans, Partner at Stephenson Harwood, shared lessons from implementing AI in legal workflows. His firm’s emphasis on governance – including proof‑of‑concept policies and decisions about risk tolerance – ensured that early AI experiments maintained the quality of AI deployments, safeguarding client confidentiality and regulatory compliance.

Agentic AI for End‑to‑End Automation

Richard Corderoy, CEO of Oakland Group, argued that true competitive advantage comes from fully automated, “agentic” AI solutions. Yet he cautioned that the quality of AI projects ultimately depends on a solid Context assessment: understanding people, processes and data before designing autonomous workflows.

Best Practices for Embedding Quality in AI

The panel referenced McKinsey’s 2025 “State of AI” report, which ranks high‑impact adoption practices in order of their effect on earnings before interest and tax (EBIT). The highest‑leverage actions include:

• Establishing Dedicated Gen AI Teams: Cross‑functional groups that embed quality competencies from day one.

• Embedding AI into Core Processes: Ensuring every major function benefits from AI’s efficiency gains.

• Active C‑Suite Sponsorship: Demonstrating leadership commitment to the quality of AI deployments across the organisation.

Lower‑impact practices – such as ad‑hoc KPI tracking or standalone “change stories”or AI tool focus – often fail to drive lasting ROI. The most successful adopters weave systematic communications, role‑based training and feedback loops into their quality framework, thus guaranteeing ongoing enhancement of the quality of AI pilots and projects.

equitably.ai: Your Partner in Quality‑Driven AI

As a Chartered Quality Professional and Chartered Marketer with over 30 years’ experience across global financial services, international marketing and technology sectors, John Paul, CEO and co-founder of equitably.ai embodies the fusion of quality rigour and AI innovation. At equitably.ai, we guide clients through every phase of AI adoption:

• Context Impact Assessment: process scans, data‑maturity analyses and use‑case prioritisation to kick‑start high‑quality pilots.

• Leadership Workshops: Executive AI programme design workshops that embed accountability and ROI for quality of AI deployments.

• Improvement Cycles: practical training that uses quality-based improvement methodologies to ensure quality of AI improvement projects.

• Assurance Testing: Scenario‑based validations, bias audits and performance‑consistency checks that guarantee the quality of AI pilots before scaling.

• Governance Frameworks: Policy templates using ISO standards for AI, risk registers and “governance‑as‑a‑service” support to maintain high standards at enterprise scale.

By embedding professional quality competencies into every stage of deployment, equitably.ai transforms AI enthusiasm into measurable business impact that delivers the target ROI with acceptable risk.

Partner with equitably.ai to make quality your competitive advantage—and turn AI promise into performance.