Steve Young

Last year (2023) saw artificial intelligence (AI) take centre-stage in digital transformation. Businesses not only looked to develop AI, but also considered how to better harness the technology and ensure that it is responsible at a global scale.

In particular, generative AI (GenAI) was of particular interest for enterprises, given its potential to offer more accurate data and better influence decision-making as a result.

With this in mind, we speak with Senior Vice President and Managing Director (SVP & MD) of Dell Technologies UK, Steve Young, about how the company is advancing GenAI and how businesses can best address the risks and anxieties associated with the technology.

Tell us about your career background, current role and the work you do with AI/GenAI

I was appointed SVP & MD of Dell Technologies UK in January 2023 after spending over 20 years at Dell, starting as a senior sales executive at EMC in 2001. 

My role includes being responsible for Dell's UK business, client satisfaction, and employee engagement, positioning Dell as the partner of choice for customers and partners looking to transform and revolutionise their businesses digitally. Today, that's increasingly through AI.

Before becoming General Manager for the UK, I was Global Vice President of Cloud Partnerships at Dell. I was responsible for bringing our storage, data protection software and services to the public cloud marketplace and expanding opportunities for customers and partners.

My work with AI and GenAI focuses on accelerating organisations' journey from possible to proven by leveraging innovative technologies, a comprehensive suite of professional services and an extensive network of partners. 

How is Dell Technologies advancing generative AI? 

Dell's expertise in Generative AI and underlying hardware and software runs deep. We offer full-stack solutions to support the complete lifecycle, plus services for strategy, implementation, adoption, and scaling Generative AI solutions across a customer's organisation. For those at the very start of their journey, we offer a half-day "Accelerator Workshop for Generative AI" to help customers address challenges and gaps, prioritise objectives and identify opportunities.

This year, we released Dell Generative AI Solutions for customers to set up access to large language models (LLMs) and create GenAI projects. This includes offering new hardware setups, a managed service platform and computers to run GenAI projects faster. These solutions span IT infrastructure, PCs and professional services to simplify the adoption of full-stack GenAI with LLM, meeting organisations wherever they are in their GenAI journey. We partnered with Nvidia for the infrastructure side, bringing Nvidia's Tensor Core GPU and Dell's enterprise AI software and data storage together so companies can run AI models faster.

We also offer Dell Professional Services for GenAI to help customers drive value to their business faster, leverage more powerful data insights and achieve competitive advantage. Our holistic approach begins with advisory services to assist customers with creating a GenAI strategy and roadmap. Also available are our implementation services, where we help train, validate, and support GenAI models (including cleansing, labelling, and anonymising data sets) and adoption services, where we apply the inferencing platform to a prioritised use case to demonstrate the business value. 

Lastly, we offer scaling services to address key IT skills gaps with Education Services for Generative AI, employ expert residents to drive initiatives forward and keep the Generative AI infrastructure running at its peak, or provide custom-managed services for continuous optimisation.    

Why is it important for organisations to be investing in generative AI?

The centre of the universe for 2024 will be AI. GenAI projects will shift from proof of concept to proof of productivity gains with greater adoption and scale across organisations and industries. 

According to our GenAI Pulse survey, 65% of organisations that have moved beyond pilot stages expect to see meaningful results from their GenAI initiatives in the next 12 months. Those innovators will reap the first mover's advantage, with GenAI becoming mainstream in most organisations in the coming years. With that in mind, enterprises that do not wish to fall behind the competition in priority areas such as selling, services, coding or content creation must make targeted strategic GenAI investments, picking the few GenAI projects that could prove genuinely transformational. 

With increased anxieties and risks over AI use, how can businesses best address their concerns?

Generative AI has as many advocates as sceptics. It can be challenging for businesses to determine how best to generate value from GenAI while simultaneously navigating anxieties from the board, the C-suite, or employees. Our recent GenAI Pulse Survey revealed that 75% of UK IT decision makers (ITDMs) think the impact of GenAI will be significant for their organisations, but 49% are somewhat or very hesitant to adopt GenAI. And it's understandable. GenAI doesn't have to be complex, but there is much to consider to get it right.

For starters, GenAI is not independent of other architectures. Therefore, enterprises must establish solid foundations before implementing generative AI safely and successfully. That means establishing a robust data strategy, investing in AI-ready infrastructure, and prioritising data privacy and security. 

Secondly, enterprises must have the right talent (with a mix of scientific understanding and creative skills) to understand the model complexities and data requirements. Next, businesses should adopt an iterative development approach to GenAI; models often require multiple iterations and updates to improve performance. Crucially, enterprises must also grasp the GenAI implications around regulatory compliance, content ownership, and ethics.

For businesses without the talent or bandwidth to manage all of the above, working with trusted IT partners to align resources would be the quickest way for enterprises to kick-start the process and enable the right strategic generative AI approach for their business. Engaging technology providers like Dell that offer AI expertise, hardware, and software in creating custom full-stack solutions can accelerate the deployment of generative AI in enterprises, unlocking the power of AI with support across the complete lifecycle, from strategy, implementation adoption to scaling of their strategy. 

Moving into 2024, do you have any advice for businesses on how to ensure that they are harnessing AI in a responsible way?

As enterprises race to adopt generative AI, they must also proactively mitigate its inherent risks in areas such as ethics, bias, transparency, privacy and regulatory requirements.

Achieving fairness and mitigating bias are essential aspects of responsible AI deployment. AI training data, algorithms or use cases can all unintentionally introduce bias. Picture a global retail company using generative AI to personalise customer promotional offers. The retailer must prevent biased outcomes like offering discounts to specific demographic groups only. To do that, the retailer must create diverse and representative data sets, employ advanced bias detection and mitigation techniques, and adopt inclusive design practices. The continuous monitoring and evaluation of AI systems will ensure fairness throughout their lifecycle.

Transparency and explainability in AI models are also vital for establishing trust and ensuring accountability. Consider an insurance company using generative AI to forecast claim amounts for its policyholders. When the policyholders receive the claim amounts, the insurer needs to be able to explain the reasoning behind how they were estimated, making transparency and explainability fundamental.

Due to the complex nature of AI algorithms, achieving explainability can be a challenging but essential part of ensuring responsible AI. Organisations can achieve this by investing in explainable AI techniques (e.g., data visualisation or decision tree), providing thorough documentation, and fostering open communication about the AI decision-making processes. 

Privacy is another crucial consideration for responsible AI implementation. Imagine a healthcare organisation leveraging generative AI to predict patient outcomes based on electronic health records. Protecting the privacy of individuals is a must-have, top priority. Generative AI can inadvertently reveal sensitive information or generate synthetic data resembling real individuals. 

To address privacy concerns, businesses can implement best practices like data anonymisation, encryption and privacy-preserving AI techniques, such as differential privacy. Concurrently, organisations must remain compliant with data protection regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).

Finally, the evolving regulatory landscape for AI technologies demands a robust governance framework that guides ethical and responsible AI deployment. 

Organisations can refer to resources like the European Parliament's Artificial Intelligence Act: or The Bletchley Declaration to help define AI policies and principles. Establishing cross-functional AI ethics committees and developing processes for monitoring and auditing AI systems can help organisations stay ahead of regulatory changes. By adapting to regulation changes and proactively addressing potential risks, organisations can demonstrate their commitment to responsible AI practices. 

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