AiThority Interview with Dan Diasio, Global Consulting Artificial Intelligence and Automation Leader at EY

Dan Diasio, Global Consulting Artificial Intelligence and Automation Leader at EY highlights the challenges organizations face in successfully deploying AI, talks about how EY supports clients by addressing these issues through infrastructure enhancement, talent development, and more in the following Q&A:

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What top challenges do organizations face in the successful deployment of AI, and how does EY support clients in overcoming these challenges?

As excitement around AI grows in the workforce, so do the challenges that can limit successful implementation. One big issue we’re seeing is many companies aren’t prioritizing the essential infrastructure needed to get the most out of AI. According to our latest AI Pulse Survey, just 36% of senior leaders are fully investing in data infrastructure—covering aspects like data quality, accessibility, and governance. This lack of investment means their AI systems might not have the crucial information they need to deliver accurate results. With the number of companies investing $10 million or more in AI expected to nearly double next year, it’s worrying that so many leaders aren’t focusing on their infrastructure.

Another significant challenge is the lack of prioritization of proper training and upskilling for employees- only 37% of senior leaders reported that their organization is fully training employees on AI. Since employees are key to successfully adopting AI, ensuring they are well-trained to use the technology confidently, ethically, and safely is crucial, and must be prioritized.

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Could you highlight a recent AI-powered solution developed by EY that has significantly impacted a client’s business transformation?

There are multiple different examples of where we are seeing clients gain value from AI embedded in their broader transformation initiatives. In one example, a global technology company is undergoing a major transformation in its finance function. While talent, processes and supporting systems are all part of the transformation, they recognize that utilizing AI in their future operations will be key to remaining competitive. As such, they are utilizing multiple different AI projects embedded in their processes to improve areas of forecasting, customer analysis, and margin improvement to drive increased value from the transformation.

In what ways is EY addressing the potential negative impacts of AI on the workplace and jobs, especially for the next generation?

AI is undoubtedly here to stay, but the near-constant stream of negative headlines can understandably make employees anxious about the technology. For AI to be successfully adopted in the workplace, it’s crucial for business leaders to address and reduce this anxiety. Currently, 71% of workers have expressed concerns about AI. To alleviate these worries, leaders should be transparent about how AI might alter job roles and encourage open discussions about any concerns. It’s also essential to highlight that humans remain central to the technology, ensuring it delivers accurate and unbiased results.  Additionally, it is imperative for organizations to provide opportunities for employees to be trained on AI concepts and play a role in defining how future processes will be adapted leveraging AI as an enabler and accelerator.

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How do you foresee AI evolving in the next five years, particularly in the context of business transformation and innovation?

We’re just scratching the surface of AI’s potential, and as regulations continue to evolve, we’ll likely see more leaders focusing on embedding responsible AI practices in their organizations. This includes priorities like data protection, red team testing, model drift management and aligning AI initiatives against an organization’s “AI compass.”

At the same time, many companies are shifting from using off-the-shelf generative AI solutions to developing their own custom AI capabilities. According to our AI Anxiety in Business Survey, 65% of employees say their organization is working on its own AI technology – and we’ll likely see this trend increase over the next few years. But whether a company decides to build or buy AI tech, it’s crucial to ensure that AI is at the core of all business strategies and decisions.

How does EY identify gaps in the AI ecosystem and develop solutions to address these needs effectively?

We work closely and strategically with our clients and see the challenges they are having in driving value from AI programs firsthand, enabling us to quickly help address them and take these learnings to drive proactive addressment of them with our broader client base. These can be talent gaps, technology gaps, processs gaps, data gaps or a combination of these things wrapped up in one. 

We take the issues and gaps that we see in our clients and our ability to help address them and make business moves aimed at further enhancing our capabilities and offerings for clients. An example of this is our recently announced strategic collaboration with NVIDIA. This relationship allows us to utilize NVIDIA’s industry-leading technology solutions, strengthening our ability to tackle emerging trends such as AI, 3D internet applications, and GPU computing. We are also committed to investing in the training of our professionals to ensure they are adept in these advanced technologies, thereby delivering greater value to our clients, driving cost savings, and enhancing operational efficiencies. Additionally, our recent acquisition of Nuvalence underscores our dedication to the AI ecosystem for enhanced product development. This acquisition enhances our team’s capacity to provide value-driven, AI-enabled platforms, further advancing our support for clients and addressing their evolving needs in the AI product development space.

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What are the next big milestones for EY’s AI consulting practice, and how do you plan to achieve them?

We want to be known as the number one choice for driving value from AI for our clients.  We already have dozens of companies that consider us their AI partner of choice and we want to triple that number this year.  To do so, we need to push the boundaries on where value can be driven. Our ongoing commitment to helping organizations build business strategies enabled by AI, improving efficiency across entire process areas and creating brand new revenue generating businesses built off the backs of our clients’ data assets leveraging AI will be critical to achieving increased trusted AI partner status.

 If you were to share five key pieces of advice with emerging AI leaders, what would they be?

• Focus on value: It’s crucial to have a deep understanding of the business challenges and opportunities that AI can address. With this in mind, AI initiatives should be helping solve real problems and creating tangible value and ROI for the organization. Look beyond small opportunities for incremental productivity gains and focus on applying AI to end to end processes or new revenue generating ideas that can create tangible value.
• Build a strong ethical framework: Only 32% of senior leaders say their organization is addressing bias in AI models fully and at scale, according to our AI Pulse Survey. Companies should invest in robust AI governance frameworks and strategies to address and mitigate bias, not only to help them stand out in a competitive market but to also strengthen their position against potential future regulatory challenges.
• Streamline data collaboration: A strong and well-organized data infrastructure is key to making AI technology work effectively. It ensures that data flows smoothly across different systems and provides the unified view needed for advanced AI applications. To get the most out of AI, organizations need to tackle data silos and improve how they collect data. By breaking down these silos, you create a more connected data ecosystem where information is shared across departments, leading to more accurate and efficient AI outcomes. Having modern data platforms that also center around usability for generating value is equally important.
• Prioritize internal talent development and focused external hiring: Building an internal pipeline of AI talent through upskilling programs is essential for creating a skilled, agile, and innovative workforce. By focusing on recruiting and developing AI experts, organizations can leverage AI’s full potential and better position themselves at the front of a competitive market. Balancing these two (hiring and upskilling) is key to success so that hires are aimed at gaps that cannot be filled internally, but existing resources see paths for development where their institutional knowledge can be highly beneficial.
• Prioritize human-centered design: Uncertainty around AI can make employees anxious about its future impact and rapid rollout. To ease these concerns, take a human-centric approach to the development of AI.  Transparency on what you are building, the expected benefits to be driven and an intentional integration between process experts and AI developers is key to both managing anxiety and adoption that will translate to benefits.

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