How to be a highly effective technology leader in the AI era
By George Dragatsis (pictured), ANZ Chief Technology Officer at Hitachi Vantara
Artificial Intelligence (AI) is becoming a litmus test for technology leadership.
At a high level, an effective technology leader in the AI era is someone that is able to think and strategise years ahead.
But it’s also someone able to put in place support structures that allows a business to make measured bets on this emerging technology over time, and be prepared for any big changes or evolutions that might emerge down the line.
Importantly, effective technology leaders know they don’t have to do all this alone – they understand the value that a strategic partner can bring to the table in helping them fulfil AI’s potential as part of their current and future operations.
We’ve observed four skills being demonstrated by highly effective technology leaders in the AI era.
A good bead on the business
An effective technology leader has a very good bead on the business they’re in: understanding underlying risks and opportunities, and both current and future pressures on the IT infrastructure in place to support business needs and requirements.
A technology leader has also thought through the ‘black swan’ events that might totally upend their world, and what they should do in those scenarios. They look ahead to potential trends or disruptions that could influence their industry to determine what they can do to ensure their business outperforms that of competitors in the space.
The pandemic was an example of an event that upended how organisations used and bought technology. Artificial intelligence is shaping as the next disruptive trend to have a similar wide-ranging impact.
As all businesses will be impacted by AI at the same time, the question for technology leaders becomes how to cost-effectively support their business to embrace the technology successfully and at scale.
Making AI a growth engine you can flex
Every business wants to be able to use AI to become the best version of itself.
The best technology leaders will have a strategy and a three-to-five year roadmap to support that cause, documenting the immediate things being put in place in order to support AI use cases, and a forward plan of improvements to help the business remain AI-ready.
Flexibility is critical, not just in thinking and approach, but in enablement as well. A strategic partnership between a business and its IT infrastructure provider can help set up structures and foundations that contain costs without constraining opportunities. New infrastructure-as-a-service models for on-premises hardware are one way of creating a flexible infrastructure that can meet a business’s AI needs cost-effectively over time.
Making it about value
Shepherding AI projects through ideation and experimentation to scaled production use is a journey.
Most businesses will start small, but find costs and challenges escalate on the path to scaled production. Some will continue to invest despite the challenges, only pulling back once it becomes clear their concept is flawed and the value and ROI will never be realised.
A key skill for technology leaders is being able to figure out whether an AI use case has an achievable return on investment within an anticipated or acceptable timeframe, before too much resourcing is sunk into it.
It’s also about realism and clarity of what that ROI is. AI is typically pitched as a productivity and efficiency aid. In an era where operations are being asked to do more with the same or fewer staff, AI is often seen as a way to absorb growth without a linear rise in staff costs.
Put another way, there’s an infinite number of things a person can do, but they’re limited by time. AI bends that time constraint, and that’s its real value. With enough automation supporting them, a person can become infinitely scalable. But in order to be able to automate more and more activities that a person does, to free up their time to concentrate on just the higher-order tasks, the business’s AI program and supports need to be able to infinitely scale as well.
Balancing pragmatism and vision
AI is going to be a mixture of smaller and big bets. The exact mixture will depend partly on risk appetite – but also on whether the business has the support of its leaders, partners and IT infrastructure to really drive AI at scale.
Pragmatism is likely to win out early on. AI’s potential is enormous, but if a use case isn’t pragmatic and doesn’t generate benefits early on, it could become a money sink. On that basis, a simple use case that’s achievable within a contained period of time is more likely to be pursued over a visionary idea that transforms how the organisation works. However, it must also be said that this current wave of generative AI would not be what it is today without one or more organisations backing their big ideas. The risk is much higher on a bigger project, but so is the potential reward.
An effective approach that technology leaders can use to determine a path forward in terms of the small and big ideas they back, is to run a workshop with key stakeholders that lists out all the opportunities for use and improvement, and evaluates these use cases based on ROI and value. It may be particularly helpful to engage a strategic partner to run this process, as they can navigate internal biases and use tried and true methodologies to identify and prioritise both the pragmatic and ambitious ideas.
