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Louise Herring, Partner, QB UKIIO Leader, McKinsey

Louise Herring, Partner, QB UKIIO Leader, McKinsey

Describe your career to date

I lead QuantumBlack, AI by McKinsey, our AI centre of excellence, in the UK, Ireland, and Israel. I started my career in quite a different place. My undergraduate degree was in Chemistry. I loved the science and learning, but I discovered that it was best for everyone that I stay out of labs. So, I became a consultant to continue learning and joined McKinsey post-MBA (as the markets crashed each time I graduated, I was right to avoid a PhD). Scaling impact from digital and analytics has been a consistent theme in my time at McKinsey: I was elected Partner for my work supporting clients to design and run omnichannel transformations. Returning from maternity leave gave me a new ambition: I wanted to not only design transformations but to lead teams delivering the impact on the ground. This revelation coincided with our integration of QuantumBlack, and I found that I loved taking a translator role; shaping use cases and interpreting insights, and that my learning curve steepened as I began to work closely with our technical teams. I had found a new home. From my omnichannel days, I had seen that raising the bar on digital literacy mattered to scaling change, so I leaned into shaping and scaling McKinsey’s Analytics Academy, eventually leading it globally. At the end of 2022, I was privileged to be asked to take my current role as Lead of UK, Ireland, and Israel at QuantumBlack.

What challenges do you see for data in the year ahead that will have an impact on your clients and on the industry as a whole? 

The excitement we have seen around generative artificial intelligence (genAI) has elevated data conversations in the C-suite. I would characterise 2023 as a year of learning. But for most, 2024 needs to be the year of delivering. To achieve this, organisations need to:

1. Make sure that they are working on the right problems, with real impact potential and plan for adoption from day one.

2. Put data (even) higher up the agenda and ensure that the foundations are in place to enable ongoing scale.

3. Prioritise talent. Building, exciting, and retaining a large group of exceptional data professionals is hard. Supporting our community at QuantumBlack is my top priority and one I think should apply everywhere – demand is only growing

How are you developing the data literacy of a) your own organisation and b) your clients?

QuantumBlack is a centre of excellence, so we actively invest in refreshing our expertise with QB Labs, our dedicated centre of innovation in data and AI, and in learning for our teams. Literacy is built within McKinsey through sharing impact stories, training for cohorts, and on-the-job apprenticeships. It is also built by transforming ourselves. For example, we built Lilli, which allows every consultant to interrogate and synthesise insights from our full data and knowledge store. This fosters deep understanding around opportunity and mitigating risks. For our clients, it is a similar approach: sharing the art of the possible, explaining dependencies, and showing by doing – investing heavily in engagement from inception to impact.

How are you preparing your organisation and your clients for AI adoption and change management?

The principles are the same for both our organisation and our clients. Firstly, solve problems that matter. For example, knowledge is our lifeforce – it is why we started with Lilli. Secondly, start from user experience and work back. Design is incredibly important in AI adoption and change. It is no use having a technically brilliant solution, if it is not usable; Iron Man’s suit only worked because it fitted. Finally, structure learning and build for re-use to enable teams to focus on only the new news. QB Labs is our vehicle to experiment and to build reusable technical assets and products to accelerate our own and our clients’ journeys.

Louise Herring
has been included in:

100 Enablers 2024 (EMEA)

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