The AI Interview: Simone Larsson, Lenovo

The well-documented rise of AI is fundamentally altering the corporate landscape, with organisations moving beyond the initial excitement of generative models toward practical, large-scale implementation.

Among those spearheading the transition is Simone Larsson, Head of Enterprise AI for the EMEA region at Lenovo, who joined the company last year. 

Lenovo is a global technology powerhouse best known for its personal computers. However, its Infrastructure Solutions Group, known as ISG, provides the critical hardware and software that powers modern businesses.

Simone leads the newly-formed EMEA AI team with a focus on sustainable innovation, helping organisations adopt AI in a way that is both practical and human-centred. The role involves shaping how the company articulates its vision for accessible and ethical technology – particularly vital as industries look to scale their digital capabilities responsibly.

“Joining Lenovo at this moment is genuinely exciting, as we work with customers to create smarter, more sustainable infrastructure and AI solutions,” Simone says. “My role is to help shape and clearly articulate what Lenovo AI stands for in this region and, most importantly, what it can do for our customers.”

A career built on technical agility

Simone’s path to the top of the AI sector was far from a linear journey. 

She began her career with a degree in Information Systems before joining Accenture as a consultant, which saw her embrace various technical challenges and a tech-agnostic approach. 

Her expertise went on to span software engineering, ERP implementation and technology service design, allowing her to understand how different systems interact within a business.

“My career evolved organically,” Simone explains. “I have gained experience in everything from software engineering and ERP implementations to big data, to product, to technology strategy, all the way to technology service design.”

After earning an MBA at Cambridge University’s Judge Business School, her focus sharpened. She began leading high-level technology innovation engagements for major corporate clients.

Simone reflects on a pivotal moment that occurred around a decade ago during a second stint at Accenture, when she led the development and implementation of a bespoke chatbot for a large customer. 

“This,” she continues, “is when I decided to specialise in artificial intelligence.”

Bridging strategy and success

In her current capacity, Simone is responsible for ensuring AI becomes a core growth priority. She works across customer success and data centre innovation to deliver meaningful results.

She also heads the AI Ecosystem Software team, ensuring that Lenovo’s products align with local needs. This involves close collaboration with the Solutions and Services Group, or SSG.

Simone says: “My ambition is to bring hands-on experience across AI architecture, software ecosystems and help turn technology into real, measurable business impact.”

As AI fuels an explosion in data across almost every industry, many organisations are struggling to deploy this data at scale or sustain it. Simone contends there exists a widening gap between the bold digital ambitions of executives and the reality of their infrastructure. 

“While AI investment has been booming in recent years,” she goes on, “the next phase IT leaders must prioritise is the ability to harness it at scale. This means selecting infrastructure that can handle growing data demands, operational models that embed AI across the business and governance frameworks that ensure responsible use. 

“This shift is redefining how enterprises think about technology, skills and long-term competitiveness.”

The importance of data sovereignty

As AI models consume more information, the question of where that data lives has become paramount. 

Simone acknowledges that conversation surrounding data sovereignty has grown louder in recent times. In the EMEA region specifically, tighter regulations and concerns over foreign legal reach are driving change, prompting organisations to localise their workloads to ensure compliance and build trust.

“Sovereignty has become shorthand for choice and control – confidence that data is handled responsibly, transparently and within local laws,” Simone says. “In an AI-driven world, trust isn’t just about ethics – it’s increasingly about geography and governance.”

In fact, research from Lenovo indicates that 88% of IT decision-makers now view data sovereignty as a primary concern, while 99% expect it to remain a critical factor over the next five years.

Redesigning the modern data centre

The rise of AI workloads is forcing a complete rethink of data centre design, with traditional centralised facilities often unable to handle the intense power requirements of modern processors.

Power density is rising rapidly, putting immense pressure on energy efficiency. At the same time, latency – the delay before a transfer of data begins – must be kept to a minimum.

“AI workloads are forcing a rethink of what ‘fit-for-purpose’ infrastructure actually means,” Simone emphasises.

“Traditional, centralised data centres are struggling to keep up. The future points to AI-ready, hybrid and distributed designs that balance sovereignty, performance and sustainability.”

Simone points out that innovations like liquid cooling, modular builds and location-aware architectures can no longer be considered as luxuries or optional extras, adding: “They are becoming essential if enterprises want to scale AI without blowing energy budgets or missing sustainability targets.”

Overcoming integration hurdles

The primary challenge when it comes to integrating AI into existing business processes is not a lack of ambition, Simone says, but a lack of readiness. 

“Many organisations want to capitalise on AI,” she explains. “However, their infrastructure, data foundations and operating models simply aren’t there yet. 

“Legacy systems weren’t built for AI-scale data volumes or power-hungry workloads and nearly half of IT leaders admit their infrastructure doesn’t support sustainability goals.”

Integrating AI into existing processes can therefore become a messy endeavour – compounded by a lack of internal skills and uncertainty regarding new government regulations.

Simone continues: “The challenge is moving from experimentation to repeatable, enterprise-wide adoption – without breaking compliance, trust or energy efficiency along the way.”

Gaining competitive advantage

Simone believes the winners in the AI race will be those who focus on deployment as opposed to “flashy models”. 

“Enterprises that win will be those that combine scalable AI platforms with sovereign, sustainable infrastructure,” she says. “Hybrid architectures, edge AI and energy-efficient technologies like warm-water cooling will unlock faster innovation without regulatory or environmental trade-offs.”

Just as important, according to Simone, will be open ecosystems and partnerships with local cloud providers, giving businesses both flexibility and control.

She concludes: “In short, competitive advantage will come from AI that is operational, rather than being stuck in the pilot phase.”