By james art Business units in London. Image by Tim Sandel
There is an AI bridge that organizations must cross to fully embrace generative AI in the enterprise. To find out, Digital Journal caught up 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 improving customer and business experiences through technology implementation.
Digital Journal: In your experience, how does a robust digital infrastructure and data strategy accelerate the seamless integration of AI?
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 systems can handle the high demands of AI, from data processing to seamless integration with cloud environments, enabling organizations to effectively scale AI capabilities.
Equally important is a well-defined data strategy. Whether using a hybrid, private or multi-cloud approach, secure and accessible data is essential to building robust AI solutions. A strong data foundation not only supports AI, but also ensures compliance and security across the board, which is critical in industries where data privacy is paramount.
One compelling example is in the banking sector, where major global banks are integrating AI into their operations to improve fraud detection. By ensuring their digital infrastructure can handle massive volumes of transactional data in real-time and developing a clear data management strategy, banks dramatically reduce the time it takes to detect fraud 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 quickly and accurately.
The role of talent in this equation cannot be understated. Data scientists and engineers are key to the success of AI, ensuring that data is properly collected, cleaned and structured for AI models. Well-managed data enables AI systems to make accurate predictions, leading to more impactful business results.
DJ: Why do you think some executives struggle to identify and measure the benefits of generative AI adoption?
Gilbert: Many executives are unsure 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 this directly impacts their specific business challenges or processes, identifying the benefits can be difficult.
Furthermore, the relative newness of Gen AI means that many organizations lack the in-house expertise to appreciate its full potential. Executives may not know how to properly measure success because their teams are still building AI skills or rely heavily on outside consultants. I think executive scanning starts with identifying areas of impact and measuring them continuously.
For example, in retail, companies have used Gen AI to create personalized marketing at scale by generating personalized content such as product recommendations and emails tailored to customer preferences. This resulted in increased engagement rates and in some cases up to a 20% increase in sales. By continuously tracking metrics such as engagement rates, purchase conversions and customer lifetime value, executives can clearly measure the direct benefits of Gen AI to drive revenue growth and customer satisfaction.
DJ: What challenges do infrastructure and data ecosystem gaps pose for enterprises striving for AI readinesss?
Gilbert: One often overlooked aspect of AI readiness is the significant infrastructure and data ecosystem gaps that exist in many enterprises. Building the necessary AI infrastructure requires significant investment in capital and talent, often in the tens of millions of dollars. Without these investments, scaling AI efforts becomes challenging.
AI’s demand for massive computing power and massive amounts of data often forces organizations to migrate to the cloud. However, many enterprises lack a coherent cloud strategy, hindering their ability to handle the scalability and flexibility required by AI workloads. A robust cloud infrastructure ensures that data can flow seamlessly across the organization, allowing AI models to process, analyze and provide real-time insights.
Additionally, gaps in the data ecosystem, such as poorly integrated data sources or a lack of proper data governance, lead to inefficiencies. AI models depend on high-quality, well-structured data for training and performance. Without a solid database, AI efforts are likely to produce unreliable or incomplete results, undermining the potential value AI can bring to business.
In essence, the success of AI is not only about the technology itself, but also about the right infrastructure and data strategies to support it.
DJ: What key skills deficits have you identified that could derail AI-led initiatives in business?
Gilbert: A 2024 study highlighted that while 81% of IT professionals believe they can use AI, only 12% actually have the skills to do so. Additionally, 70% of workers will likely need to upgrade their AI skills to remain competitive in the evolving landscape.
Here are a few skills gaps I’ve noticed that could potentially hold back AI initiatives in organizations:
● Data Science and Machine Learning Expertise – Companies often struggle to find talent who have deep knowledge of data science and machine learning, so proactively upskilling employees in these areas is paramount. Without these skills, organizations may struggle to develop and implement effective AI technologies that generate positive business outcomes.
● Data Engineering and Management – While having data professionals is important, it is perhaps more important to have employees who can effectively and efficiently manage the data that companies use in their AI technologies. Without effective data management and engineering, companies can risk having ineffective AI programs and models.
● AI Ethics + Governance – As AI takes on more decision-making roles, the lack of dedicated teams to oversee the ethical use of AI is increasingly becoming an issue. 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 not prepared to address the risks associated with data bias, privacy or model accuracy.
● Human in the AI application cycle – While having technical AI skills is important for employees, it is also important for companies to train employees to bring the human element back into AI processes. Ensuring that AI programs work symbiotically with human experience and decision-making is critical to ensuring that companies can strategically use AI to achieve business goals and profits.
DJ: How can companies proactively identify and address gaps in AI readiness to prevent project cost and maximize returns?
Gilbert: To successfully bridge AI readiness gaps, companies need a structured approach. First, systematic assessment of skill levels is critical. This can be done through a combination of objective skill assessments and employee self-assessments, comparing the two 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 behind.
Beyond skills assessments, organizations must 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 constantly developing new skills. It is important to recognize that overcoming the AI skills gap is not a one-time process; this requires an ongoing commitment to retraining and upskilling.
Another important aspect that companies should pay attention to is resistance to change. This can be done by highlighting success stories of employees who have benefited from upskilling efforts. Showing how people have improved their careers by continuously learning and adapting to AI can inspire others to follow suit. Furthermore, linking these efforts to tangible results—such as improved efficiency, innovation, or business success—reinforces the importance of evolving skill sets over time.
In addition, companies can use 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 holistic approach to skills development, data infrastructure and cultural change.