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Nayur Khan, Partner, Quantumblack, AI by McKinsey

Nayur Khan, Partner, Quantumblack, AI by McKinsey

Describe your career to date

As a partner within McKinsey’s London office and part of the QuantumBlack, AI by McKinsey team, I predominantly focus on helping organisations leverage data to build and scale artificial intelligence (AI), including generative AI (genAI), to improve performance. I also help companies navigate innovations, technologies, processes, and digital skills as needed. My work has spanned several sectors and industries, including energy, healthcare and life sciences, finance, and retail. I help organisations move away from pilots and experiments with AI to industrialised implementations that run reliably at scale. Before joining QuantumBlack, I helped organisations by driving strategies and implementations with data, machine learning, and AI to achieve digital change and impact. I built and led multi-disciplined engineering teams to deliver complex technology solutions and products.

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? 

Data and leveraging data efficiently and sustainably is not getting simpler. For example, there are 400+ database storage technologies available today (a conservative estimate), plus hundreds of additional technologies to manage concerns such as ETL, ELT, reverse ETL, cataloguing, streaming, querying, reporting, analytics, etc. There is a foundational challenge of simplifying the technology, its usage, and governance. We then have challenges around discovery, access, quality, privacy, compliance, and data residency – this list will continually evolve, therefore making the data world rather fluid in terms of challenges. GenAI has complicated things further with new types of storage needed and additional data governance woes.

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

1 – Learning programmes. We invest in learning programmes for our talent, available to data specialist roles (Data Engineers, Machine Learning Engineers, Data Scientists, Software Engineers, etc) or non-data specialist roles (Consultants, Team Leaders, Product Managers, etc).

2 – Apprenticeship. We invest a lot in apprenticeships, learning from others more experienced in the field. We pair up colleagues to ensure the correct apprenticeship and mentorship.

3 – For clients, we develop and roll out similar learning programmes to help them with their own data literacy and capability. For exec levels to the generalist or specialist levels.

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

AI adoption starts with learning programmes and building awareness. This is not dissimilar to data literacy programmes. Where new AI tools are being rolled out across an organisation, there are usually three steps we follow

1) Identify a set of alpha users to gather feedback.

2) Identify AI champions from within those groups.

3) Stage the rollout with a sharp focus on feedback and empowering champions to help.

We find that bringing end-users into the process helps with overall AI adoption.

Nayur Khan
has been included in:

100 Enablers 2023 (EMEA)
100 Enablers 2024 (EMEA)

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Nayur Khan, Partner, Quantumblack, AI by McKinsey

Join our membership network of over 250 global senior data and AI leaders.

The trusted global collaboration and intelligence platform for data leaders.

Join our membership network of over 250
global senior data and AI leaders.