There is an AI bridge that organizations must cross to fully adopt generative AI across the enterprise. To learn about this, Digital Journal connected with Doug Gilbert, CIO and Chief Digital Officer at Sutherland Global.
As CIO and CDO, Gilbert regularly oversees product and technology development and transformation, with a proven track record of enhancing customer experiences and business through the deployment of technology.
Digital Journal: From your experience, how does a robust digital infrastructure and data strategy accelerate seamless AI integration?
Doug Gilbert: AI initiatives often struggle to scale or deliver meaningful results without a solid foundation of technology infrastructure. A strong digital core ensures that the systems can handle the high demands of AI, from data processing to seamless integration with cloud environments, allowing organizations to scale AI capabilities efficiently.
Equally important is a well-defined data strategy. Whether leveraging a hybrid, private, or multi-cloud approach, secure and accessible data is essential for building robust AI solutions. A strong data foundation not only supports AI but also ensures compliance and security across the board, which is crucial in industries where data privacy is paramount.
One compelling example is in the banking sector, where major global banks integrate AI into their operations to enhance fraud detection. By ensuring their digital infrastructure can manage vast volumes of transactional data in real time and developing a clear data governance strategy, the banks drastically reduce the time taken to fraud detection while maintaining regulatory compliance. This transformation is only possible because the digital core and data architecture are aligned to support AI systems in identifying anomalies swiftly and accurately.
The role of talent in this equation cannot be understated. Data scientists and engineers are pivotal to AI success, ensuring that data is collected, cleaned, and structured properly for AI models. Well-managed data allows AI systems to make accurate predictions, yielding more impactful business outcomes.
DJ: Why do you think some executives are struggling to identify and measure the benefits of generative AI adoption?
Gilbert: Many executives are uncertain about where Generative AI (Gen AI) can provide the most value, especially in industries where use cases are still being explored. Without a clear understanding of how it directly impacts their specific business challenges or processes, identifying benefits can be difficult.
Additionally, the relative newness of Gen AI means that many organizations lack in-house expertise to evaluate its full potential. Executives may not know how to properly measure success because their teams are still building AI skills or rely heavily on external consultants. Where I think an executive scan starts by identifying the areas of impact and continuously measuring them.
For instance, in retail, companies have used Gen AI to create personalized marketing at scale by generating individualized content, such as product recommendations and emails, tailored to customer preferences. This has resulted in increased engagement rates and, in some cases, a 20% rise in sales conversions. By continuously tracking metrics like engagement rates, purchase conversions, and customer lifetime value, executives can clearly measure the direct benefits of Gen AI in driving revenue growth and customer satisfaction.
DJ: What challenges do gaps in infrastructure and data ecosystem pose to enterprises aiming for AI readiness?
Gilbert: One often overlooked aspect of AI readiness is the significant infrastructure and data ecosystem gaps that exist in many enterprises. Building the necessary infrastructure for AI requires substantial capital and talent investment, often reaching tens of millions of dollars. Without these investments, scaling AI efforts becomes challenging.
AI’s demand for immense computing power and vast amounts of data often forces organizations toward cloud migration. However, many enterprises lack a coherent cloud strategy, which hampers their ability to handle the scalability and flexibility AI workloads require. A robust cloud infrastructure ensures that data can flow seamlessly across the organization, enabling AI models to process, analyze, and deliver insights in real time.
Additionally, gaps in the data ecosystem, such as poorly integrated data sources or lack of proper data governance, lead to inefficiencies. AI models depend on high-quality, well-structured data for training and execution. Without a solid data foundation, AI efforts are likely to result in unreliable or incomplete outcomes, undermining the potential value AI can bring to the business.
In essence, AI success is not just about the technology itself but about having the right infrastructure and data strategies in place to support it.
DJ: What key skill shortages have you identified that could derail AI-driven initiatives within businesses?
Gilbert: A 2024 survey highlights that while 81% of IT professionals believe they can utilize AI, only 12% actually possess the necessary skills to do so. Furthermore, 70% of workers will likely need to upgrade their AI skills to stay competitive in the evolving landscape.
Here are several skills gaps that I’ve observed that could potentially hinder AI initiatives within organizations:
● Data Science and ML expertise – Companies often struggle to find talent that has deep data science and ML expertise, so proactively upskilling employees in these areas is of paramount importance. Without these skills, organizations may struggle to develop and deploy effective AI technologies that generate positive business results.
● Data Engineering and Management – While having data scientists is important, perhaps more important is having employees who can effectively and efficiently manage the data companies use in their AI technologies. Without effective data management and engineering, companies can run the risk of having ineffective AI programs and models.
● Ethics + AI Governance—As AI takes on more decision-making roles, the absence of dedicated teams to oversee ethical AI usage is increasingly becoming a concern. Without them, companies could risk deploying unethical and/or biased AI models into an organization. McKinsey notes that only 18% of organizations have established AI governance boards, and many organizations are unprepared to tackle risks related to data bias, privacy, or model accuracy.
● Human in the Loop Applications of AI—While possessing technical AI skills is important for employees, it’s also essential that companies train employees to introduce the human element back into AI processes. Ensuring that AI programs work symbiotically with human expertise and decision-making is critical to ensuring companies can strategically use AI to drive forward business goals and wins.
DJ: How can companies proactively pinpoint and address AI readiness gaps to prevent project values and maximize returns?
Gilbert: To successfully bridge gaps in AI readiness, companies need a structured approach. First, systematically assessing skill levels is crucial. This can be done through a combination of objective skills assessments and self-evaluations by employees, with the two being compared to provide an accurate understanding of existing competencies and gaps. Regular benchmarking against industry standards can further help identify areas where the workforce is lagging.
Beyond skill assessments, organizations should foster a culture of continuous learning as AI and related technologies evolve rapidly. Providing ongoing learning opportunities—such as certifications, hands-on workshops, and digital courses—will ensure employees are consistently developing new skills. It’s important to recognize that addressing the AI skills gap is not a “one-and-done” process; it requires an ongoing commitment to reskilling and upskilling.
Another important aspect that companies need to address is resistance to change. This can be done by highlighting success stories of employees who have benefited from upskilling efforts. Showcasing how individuals have enhanced their careers through continuous learning and adapting to AI can inspire others to follow suit. Additionally, linking these efforts to tangible outcomes—like improved efficiency, innovation, or business success—reinforces the importance of evolving skill sets over time.
Moreover, companies can leverage AI readiness frameworks, such as those from McKinsey or Gartner, which provide strategic guidance for identifying gaps in infrastructure, data management, and AI governance. Organizations can avoid common pitfalls and successfully integrate AI into their operations by taking a comprehensive approach to skills development, data infrastructure, and cultural change.
