How to Plan for Talent to Build an AI Capability Hub The 'Double Pyramids'

'People'- this term is fast becoming the standard oxymoron for any artificial intelligence initiative in an enterprise.

When it comes to AI talent hunt, at all levels, quality and relevance must always take precedence over quantity. Traditional 'bench strength' is actually a major weakness in the context of AI, as the extra workforce doesn't add any value either by way of thought leadership or in implementation. Rather, they create unnecessary clutter, make simple things look overly complex, and can potentially make every project, every sprint- a grand mess.

Given that the fundamentals of AI and ML have evolved from mathematics and statistics, good rigor in STEM is a must-have for all relevant tech talent in this space. Just knowing how to write code in Python will cut no ice in AI.

Here is a simple 'Double Pyramids' framework for AI talent planning while you set up an enterprise AI capability hub. It's generic, covers only the basic minimum, and isn't an exhaustive superset of all types of people/ talent you may need. Specifics will depend largely on 1- your scope, 2- your goals, 3- your strategies and strategic priorities.

How to Plan for Talent to Build an AI Capability Hub The 'Double Pyramids'

The pyramids, somewhat similar to Maslow's Hierarchy of Needs (in this case, the needs for talent), also reflect the demand-supply realities in the AI space. For example, for every 1 person to qualify for the L4 level, one can get about 3-5 people at L3, about 30-50 people at L2, and about 300-500 people at L1- (all ballpark estimates, not cast in stone, no formulas here, will vary by complexities in tech used and domain) in both the pyramids- be it technical skill-mix or domain-skill-mix.

So, in target resource planning, if one may start with X at L4 in both pyramids:

L3 will most likely be in range of 3X-5X,
L2 will most likely be 10X of L3, and
L1 will be about 10X of L2.

These ratios more or less reflect the current realities in the talent supply chains as well, given the over-enthusiastic coders who want to ride the AI hype thinking Python coding and AI are synonymous, and often shying away from the hard maths of data sciences that's required, to go up to L2 and so on.

Also, the other critical aspect regarding talent is to acknowledge the fact that in most use-cases and applied AI scenarios, domain expertise is more important than just generic AI/ML technical skills. This is because of the fact that empty shells of algorithms cannot deliver any value without relevant data models, knowledge graphs, good knowledge items (nodes in the graph), training data- labelled/ annotated or not, domain lexicons and ontologies. SMEs of domains are the most critical talent levers that enterprises can ill-afford to ignore.

About the Author

Tapati Bandopahyay - General Manager and Global Practice Head, Wipro Holmes AI & Automation Ecosystem

Tapati Bandopadhyay is General Manager and Global Practice Head, Wipro HOLMESTM AI & Automation Ecosystem. She drives strategy & thought leadership for AI innovations, top line impact metrics, new themes & use-cases, AI for a Cause, market making, positioning, ecosystem strategy, use of design thinking in AI solution architecture, Ethical AI design principles etc.

She is a gold medallist in engineering from Jadavpur University, a DFID scholar at the University of Strathclyde, and a PhD in AI.

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