Getting AI Quality
The Investment Association recently hosted a webinar on AI Quality in Investment Management, moderated by James King, Senior Advisor, Innovation and Operations Unit at the Investment Association and the host of the Investment Association AI Podcast.
Vincent Desmond, CEO of the Chartered Quality Institute, provided an interesting introduction, talking about the profession’s long history in applying proven professional quality competencies in the regulated sectors. John Paul Danaee, Chartered Quality Professional and CEO of equitably.ai, was the speaker, presenting the paper he wrote in Quality World on Maximising AI Impact Using Quality Competencies, together with guest speaker Dr. Nataliya Tkachenko, ESG Data & Generative AI Ethics Lead from the Chief Data & Analytics Office of Lloyds Banking Group, who provided a fascinating case study on AI quality.
Here are some of the key points covered during the session and why AI quality is a must.
AI Pilot Paradox
Artificial Intelligence (AI) is rapidly reshaping the UK investment management landscape, becoming a core pillar in financial services strategy.
The promise is immense, with Gartner forecasting that over 80% of enterprises will have integrated generative AI by 2026. Yet, many firms are facing the “AI pilot paradox.” Promising pilots that fail to scale into impactful, enterprise-wide solutions, leaving anticipated returns unrealised. Presenters provided an operating definition of AI quality (see image). They showed that in order to meet requirements, including return on AI investment – moving beyond pilots – and risks including regulation by the FCA, a more structured approach using proven quality competencies can consistently unlock AI’s potential which minimises risks.
Hidden Key: Applying Quality Management Principles to AI
What do we need to move from AI pilots to enterprise-grade solutions while meeting the FCA’s technology-agnostic approach to AI regulation based on the senior manager’s regime?
Technical and traditional compliance capabilities alone may not be enough.
The missing ingredient? Professional quality competencies.
Regulatory Imperative: Why AI Quality Can’t Be an Afterthought
Deploying AI responsibly in financial services is non-negotiable. While regulatory guidance may not always reference AI explicitly, the implications are clear. FCA rules embedded within COBS (client communications and best interest duties), SYSC (systems and controls), and PROD (product governance) all demand a level of quality that AI deployments must meet.
Is your AI project or deployment aligned with the expectations of the FCA?
Professional Advantage: Chartered Quality Institute’s Quality Competencies for Quality AI
Imagine having a strategic framework to guide your AI implementations, ensuring they are not just technically sound but also aligned, deliver real improvements, are governed, and assured. The Chartered Quality Institute’s (CQI) Professional Competencies Map offers precisely this. By adopting these core competencies, investment management firms don’t have to reinvent the wheel and can build a powerful foundation for AI success. The competencies include:
Context: Truly understanding your firm’s operational intricacies, client needs, and the specific UK regulatory demands is paramount before AI can deliver real value.
Improvement: Utilising structured quality methodologies, such as DMAIC (Define, Measure, Analyse, Improve, Control), with change management tools to navigate AI projects from concept to measurable impact, ensuring that insights translate into genuine business benefits.
Leadership: Effective leadership is the catalyst, championing a quality-first culture for AI across all levels of your organisation.
Governance: Moving beyond ad-hoc AI projects to establish clear alignment with strategic business goals, defined accountabilities (vital under SMCR), and systematic risk management.
Assurance: Building justifiable confidence in your AI systems. This involves robust validation, ongoing performance monitoring against client-centric metrics, and diligent data quality assurance.
Structured Methods, Real Impact: Tools to Drive Change
Practical tools like Voice of the Customer and Critical to Quality (CTQs) measures of performance from AI, FMEA risk analysis, and process mapping can increase the impact of AI projects and ensure they’re solving real business problems while mitigating risks. And with a disciplined methodology like DMAIC, AI initiatives can stay laser-focused on delivering value, something investment managers should understand.
But here’s the truth: even the most powerful AI solution will fail without change management to get people on board. Proven change management tools like the GE Change Acceleration Process, when applied effectively by experienced professionals, can overcome the resistance and increase the effectiveness of AI:
Effectiveness = Quality x Acceptance (E = Q × A)
Without AI literacy, change management, and cross-functional buy-in, no solution can scale successfully.
Ready to Go Beyond AI Experimentation?
To leverage AI for maximum impact in investment management is complex.
A quality-first approach offers a clear, actionable path to:
- Break free from the AI pilot trap
- Deliver compliant, high-impact solutions
- Create tangible, long-term value for your clients and firm
Unlock the Full Potential of AI in Investment Management
If you’re an investment management firm or professional looking to apply AI, it’s time for a different conversation.
Contact us today and explore how a tailored, quality-led approach can help you build smarter, safer, and more effective AI — designed for performance and built for trust.