Tech leaders anticipate AI boosting developer productivity in their organizations over the next three years, coinciding with a 39% surge in IT requests in the past year as they deploy generative AI tools and implement large language models.
However, 62% acknowledge their organizations are not adequately prepared to integrate data systems for optimal AI utilization, hindering the transition and intensifying strain on their teams, according to a Mulesoft survey of more than 1000 IT leaders.
The report found nearly all (98%) of IT organizations face challenges in digital transformation, including concerns about data silos (80%) and interdependence among systems (72%).
Data silos continue to impede digital transformation efforts for more than eight in 10 (81%) of respondents, indicating the need for improved integration to unify all business data for effective AI deployment.
Additionally, 72% of IT leaders face challenges due to an overly interdependent infrastructure, and 75% of organizations struggle to integrate data insights into user experiences—just 26% said they believe they offer a fully connected user experience across all channels.
Willy Sennott, executive vice president of FinOps at Vega Cloud, says as enterprises are increasing not only the volume of IT related projects but also changing the type of projects, it is putting pressure on IT teams to expand and adapt internal skill sets.
“The typical areas we are seeing skill gaps in are data science and analytics, cybersecurity, AI and other emerging technologies,” he says. “There are also gaps in deeper DevOps and Agile Methodologies and for expansion of current cloud skills such as containers and microservices.”
Sennott says there is more focus on compliance in an environment where data privacy and other compliance regulations are not only increasing, but also evolving more often than in traditional IT structures.
“These skills gaps and compliance concerns, if left unchecked, will continue to slow down the successful implementation of many digital transformation initiatives,” he cautions.
Mike Rosenbloom, CEO of Lemongrass, explains in addition to mastering emerging technologies, enterprises must also maintain robust processes for technology management.
“This includes regularly updating licenses and certifications specific to the newly implemented technologies, ensuring that the team managing these tools is fully qualified and compliant with industry standards,” he says.
Parallel to this, there’s a need for dedicated personnel who can manage and upgrade current or legacy systems, ensuring seamless integration and function of both new and legacy technological frameworks.
Looking ahead, Rosenbloom says key challenges for organizations will include keeping up with rapid technological advancements, particularly in ML.
“This demands not only the adoption of new technologies but also a thorough understanding of their implications and potential applications within organizations,” he says.
From his perspective, the role of tech leadership in 2024 extends beyond specific expertise to include cultivating strategic foresight, leadership in change management, and a strong alignment with business objectives, all essential for successful digital transformation.
Jonathan Bruce, vice president of product management for Alation, says organizations need to clearly explain the benefits and processes behind using AI.
“There is a need for training programs about AI and how to use it effectively,” he says. “Rather than banning it outright, a more effective approach is to embrace AI.”
From his perspective, the key lies in engaging with generative AI, comprehending its capabilities, and learning to harness its potential in a constructive and ethical manner.
“It’s also important to develop job roles emphasizing human strengths and the collaborative nature of human-AI interactions to reassure employees about their roles,” Bruce says. “By integrating these strategies, organizations can cultivate an environment that fosters collaboration and continuous improvement.”
Adopting a people-first strategy, which involves training programs accommodating different skill levels, is vital.
“Educating employees about the data accessed by AI and its use can demystify how AI benefits them,” he says. “Additionally, an organization must be well-versed in data literacy to implement AI effectively.”
Establishing a robust data culture is the cornerstone of widespread AI adoption, ensuring employees can find, understand, trust and use data.
Bruce adds implementing a clear, achievable set of outcomes enhances an organization’s ability to embrace and fully make the most of AI for future applications.
“A data literate organization focuses on using the right data for AI purposes, drives greater value and continuously measures and tracks progress in areas most critical to the organization’s success,” he says.