Accenture’s Andrew Kelly explores the early days of his career amid Ireland’s data science landscape and how others might forge a similar career path.
Andrew Kelly, a senior manager in AI and data at Accenture, was undertaking a degree in engineering when he first started out as an intern at the organisation. He explained that it was the first time that he had real exposure to data science, through working with data to find patterns and produce insights.
He told SiliconRepublic.com, “I had always enjoyed maths and quantitative subjects, but this was different. It was hands-on, applied and had a clear commercial reason behind it. Something clicked. I went back to college, completed my master’s in engineering, but already knew where I wanted to end up.”
Soon after, Kelly re-joined Accenture as a graduate and immediately began to work once again within the realm of analytics, but also started to branch out into business intelligence reporting, data engineering, GenAI, financial services and, increasingly, banking.
Kelly said, “In recent years my focus has shifted from being a data practitioner to helping banking clients build the foundations for AI at scale – the platforms, the governance, the risk frameworks and the responsible AI processes that have to sit behind all of it – while simultaneously trying to deliver real, measurable value quickly enough to prove it works.”
Ireland’s data science ecosystem
As Kelly finds it, Ireland is “in a promising but unfinished position” within a space that has allowed for the sharp acceleration of AI adoption. According to a previous report published by Accenture, the number of Irish employees using GenAI tools daily is almost triple the figure noted just two years ago.
However, he is of the opinion that this has yet to translate into a more visible and effective organisational transformation, noting it is really only taking effect at an individual level, rather than across whole companies.
“What I see consistently in organisations is the treatment of AI as a technology initiative rather than a business reinvention. There is a strong temptation to automate isolated steps in a process, to take a multi-step workflow and automate part of it,” said Kelly.
“That is not transformation. The organisations that will pull ahead are those asking a fundamentally different question: ‘How does AI change how we work end-to-end?’ or ‘How does it change how we interact with customers?’ That requires process reinvention, not point automation.”
There is a gap shown in the research, he finds, with only around 10pc of Irish organisations having reached what Accenture would classify as ‘scaler’ status – that is, where AI is embedded in core operations. The majority are still experimenting and integrating, largely in silos.
“The data, governance and legacy infrastructure challenges are real constraints. But the deeper issue is often strategic clarity. AI investment needs to be value-led, tied to top-level business outcomes, not driven by the technology itself.
“The encouraging signal is that Ireland has the right ingredients – talent, infrastructure, regulatory framework and genuine national ambition. The question is execution at scale, and that clock is ticking.”
What does the future look like?
Though it wasn’t all that long ago, much has changed in the data science and wider STEM space since Kelly first began his career.
He explained that at the start of his journey, work was often centred on the development of predictive models in code and running data science projects that served specific parts of the business. Conversations were “largely technical” and outcomes were “meaningful but relatively contained in their organisational reach”.
“What has changed fundamentally is the scope of impact. GenAI still requires deep expertise to build and govern well, but its reach extends far beyond those who build it. It is permeating processes across entire organisations, touching roles, workflows and customer interactions that traditional machine learning never reached in the same way.”
Skill expectations have somewhat transformed too. Nowadays, workforce-wide AI readiness has emerged as a critical capability.
“AI literacy can no longer sit only with the teams building the technology. It must be distributed across the business. The thing that has not changed is the pace of change itself. It has always been fast in technology, but it is accelerating in ways that organisations must actively manage,” said Kelly.
“The technical foundation matters – understanding data engineering, machine learning, platform architecture and governance well enough to have credible conversations with both practitioners and senior clients. But in my day-to-day, the skills I lean on most are not technical ones.”
Instead, he regularly utilises a broad skillset that enables him to navigate complex environments with professionals in regulation, risk, tech, business and senior leadership, “often simultaneously and with competing priorities”.
He added, “The ability to translate between technical teams and business decision-makers, between what AI can do and what it should do, is probably the skill I use most.”
Cultivating a career
But most of all, the attribute that often stands out the most in a career setting is not a perfect track record, but a display of genuine interest.
Kelly himself came into his initial role as an intern, moving from engineering into data science, and he often wondered in the early days if his technical profile compared sufficiently with those of people who had studied computer science or statistics directly.
“That was misplaced. What I had was curiosity and a willingness to learn and, looking back, that counted for far more. The second thing, and this is something I still have to remind myself of, is to make peace with not knowing everything. In AI especially, that feeling of being slightly behind the curve is essentially universal.
“Everyone is learning with this technology. The organisations and individuals who are pulling ahead are not necessarily the ones with the most prior knowledge. They are the ones with the highest aptitude and appetite for learning continuously as things change.
“What I look for when I hire is exactly that – the ability to pick things up, adapt and grow. Skills can be acquired. That orientation, that genuine curiosity and resilience is harder to teach. If you have it, the rest tends to follow.”
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