digital, AI, spending, cloud cost, Navigating Budget Relations for Tech-Focused CxOs in 2023

Company leaders are striving to maintain momentum in their digital investments, with AI emerging as a key differentiator for IT decision-makers aiming to disrupt and innovate. 

According to a TEKsystems survey of 855 technology and business decision-makers, 75% of digital leaders plan to increase spending in 2025, compared to just 47% of digital laggards.  

Digital leaders are also twice as likely to allocate $10 million or more per initiative. 

However, challenges persist: Nine out of 10 organizations reported lacking the talent required for successful digital transformation projects. 

Additionally, the C-suite (42%) is twice as likely as line-level managers (19%) to expect returns on transformation initiatives in six months or less, highlighting a gap between executive expectations and operational realities. 

Meanwhile, generative AI (GenAI) has made significant strides, with one in five organizations scaling its use across multiple units or enterprise-wide, underscoring its transformative potential. 

Adoption of cloud-native platforms (72%) and IaaS (71%) also remains strong, reinforcing their role in driving innovation. 

Economic uncertainty continues to weigh on budgets, with nearly a quarter (23%) of organizations citing it as a barrier to progress. 

Ramasamy Palaniappan, CTO, TEKsystems Global Services, explained the ROI for digital transformation traditionally used to be a long-term planning exercise for organizations to truly realize value. 

“With the evolution of AI, the timeline for digital transformation has shrunk significantly,” he said. 

As a result, organizations are now focusing on incremental investments measured against incremental returns, which has proven to be highly successful. 

“While there are some exponential benefits, the fundamental principle of this approach ensures that digital transformation spending remains meaningful for organizations,” Palaniappan said.  

He said to optimize significant investments, digital leaders should start with pilot projects to prove their value before scaling up. 

They should also avoid a “Big Bang” approach, and break initiatives into incremental, continuous evolution phases. 

“Establishing a clear mechanism to connect benefits to spending is crucial, along with providing visibility and engaging end users consistently to ensure alignment and adoption,” he added.  

Lastly, considering rapidly changing technology, it is essential to design a pluggable framework—from architecture to technology evaluation—that supports ongoing adaptation and continuous evolution. 

Leslie Deutsch, TEKsystems’ vice president of people strategy, said to address the significant skills gap in digital transformation, organizations must focus on fostering a culture of continuous learning as a foundation for success. 

This involves prioritizing reskilling and upskilling initiatives and offering training programs in key areas such as AI, machine learning, and cybersecurity to ensure employees are equipped to adapt to new technologies. 

“Engaging employees at all levels is also essential,” she said. “Involving them in the transformation process through cross-functional teams, feedback loops, and open communication fosters a sense of ownership and innovation.” 

Organizations should also adopt an iterative approach to transformation, enabling continuous refinement of strategies and alignment with evolving business needs. 

Finally, implementing robust change management strategies is critical to addressing human and organizational challenges, and ensuring a smooth transition through clear communication, senior leadership engagement, and ongoing employee support. 

From Palaniappan’s perspective, the successful enterprise-wide scaling of generative AI relies on five key factors. 

“Data quality is critical, ensuring grounded and reliable responses,” he explained. “Robust security and governance controls must also be established to safeguard the deployed systems.”  

Focusing on strong, well-defined use cases is essential for driving adoption, rather than simply providing organizations with a generic toolkit. 

Building trust is paramount, which requires significant effort to eliminate hallucinations and ground models using the right datasets. 

“Lastly, fostering adoption through continuous encouragement and feedback is crucial, as generative AI adoption can either scale rapidly or fail without user engagement,” Palaniappan said.