by Adam Travaglione & Sara Ayres
Technology transformation is the top strategic priority for many U.S. business leaders, according to the C-Suite Barometer: Executive Leadership Insights in the US report from Forvis Mazars.1 And more of them are acting rather than waiting, with real financial consequences. Most U.S. leaders indicated they expected an increase in revenue this year, only slightly down from what they forecast for 2025 and 2024. Medium and large companies in the technology, media, and telecommunications (TMT) sector reported some of the highest revenue growth expectations among U.S. sectors.
The TMT sector is typically challenged to adopt the next biggest technology. Companies not trying to implement new technologies risk being left behind. This article explores how finance leaders, particularly in the TMT sector, must balance innovation, governance, workforce change, and capital discipline to succeed.
Hallmarks of an Effective Tech Transformation
A recent survey of C-suite leaders by Forvis Mazars indicated that while most have a technology transformation strategy in place, the figure declined in the past year, suggesting that companies are shifting from strategy design to execution due to the widespread adoption of artificial intelligence (AI).1
Many companies are slow to adopt new technology due to the quality of current data, disruption from system changes, the ability to integrate into existing systems, costs, and the timeline. Large system changes can take more than a year to implement, which may be expensive in both dollars and employee time. Companies must also consider the impact these transformation plans have on information technology (IT) general controls from not only internal risk management, but also a regulatory perspective as they are large enough for investors and regulatory bodies to be concerned and have reporting requirements related to them.
Businesses should take precautions so their work toward a successful transformation does not become a stalled initiative.
Successful technology transformations should include:
- Project managers to oversee affected areas; mapping goals, stakeholders, milestones, and communication protocols; and memorializing decisions and changes;
- Detailed system maps, including interfaces, both current and future state, to understand how existing risks are mitigated and where new risks could emerge;
- An assessment of the current data quality from all sources, including internal, shared, and ingested, along with an assessment of who will use it and how it will be used in its future state; and
- Current and changing security requirements.
When businesses start putting implementation road maps into action, the most difficult part is often data integration and the controls around it.
Leaders and companies looking to identify and adopt AI-based platforms need to take additional steps. AI implementations should not be done piecemeal. To integrate AI successfully, the data needs to be centralized and clean so the AI can ingest it all. In addition, companies need to make sure that their project plan sets clear goals and milestones, including identification of use cases, key data identification, the selection and design of the system, and integration. Each stage of the overall project requires close communication and monitoring from each team involved. Collaboration between a company’s finance and IT teams is more important than ever and vital for successful adoption.
Enterprise IT as the Engine of Adaptability
A defining feature of successful U.S. organizations is their ability to continuously adapt, with technology at the center. U.S. leaders are not waiting for clarity, but instead revising plans, diversifying resources, and accelerating investment to stay resilient amid volatility.1 Companies should focus on having trusted systems that can provide them with needed information. For time-sensitive decision making, business leaders can’t wait on manual work in spreadsheets that the system should be set up to do.
Data organization and usability are vital for organizations needing agility. A successful startup could grow from a few people to a few thousand, but that growth will stall if the business expands faster than its infrastructure.
With the adoption of AI, clean data is paramount. If AI is making decisions on flawed, incomplete, or inconsistent data, the output of the system will never be accurate or usable by finance leadership teams. A company’s strategy should include deciding how AI will be used and how data will be captured or tagged. Being active in planning the adoption of AI, meticulous in the approach to understand the data created and its security considerations, and mindful in the companies’ specific use case of the software is key when finance leadership partners with their IT functions to successfully adopt software across their organizations.
AI as the Stress Test of IT Foundations
AI is at the forefront of companies’ technology transformation efforts. According to the C-Suite Barometer report by Forvis Mazars, businesses are scaling AI across core functions, and most companies have already restructured teams to support it.1
While companies may see efficiencies after adopting AI-based software, the change is also exposing the weaknesses of the foundation of IT in many cases. Given the growth and sophistication of the infrastructure required for adopting AI based-software, companies should consider an unprecedented level of cyberthreats. The reality is that each day companies can receive countless attacks. It’s imperative that security is a key foundation for adopting any new and upcoming AI-based software. Overlooking security could cause significant harm to not only the companies, but also their customers, with greater impact than any one cybersecurity event.
Finance leaders are learning to ask the right questions regarding controls, auditability, and clarity. Prior to AI, they would typically ask: where is the data going, which systems are we using, and what security do we have? Now in addition to these basic principles and the necessary cybersecurity considerations, companies need to understand what new technology is doing. These new questions include: where is AI being used, what is AI actually doing, and where is it pulling the information? Then, how is our data being stored and kept secure?
IT, Data, & Financial Decision Making
U.S. executives are most confident in achieving return on investment from AI, followed by cybersecurity/risk management and data connectivity, according to the C-Suite Barometer report by Forvis Mazars.1
Companies should make sure they have systems that are collecting and using data in a way that is useful to them. However, finance teams are often still constrained by fragmented or unreliable data, despite having more robust and integrated software to help track data. A company may have automated connections between departments, but if a proper control environment is not set up, there could be a breakdown in the process that goes unrecognized.
IT investments can improve financial visibility and confidence once companies have the appropriately sized and integrated systems for their business size and security to help secure data. With more efficient software, companies can perform a more thoughtful review of their information for an improved output.
Conclusion
While U.S. finance executives, especially those in the TMT sector, are well versed in disruptive technologies, the new wave of AI-enabled technologies presents many unique challenges that this specific industry is forced to tackle quickly and accurately. As the industry continues to adapt and adjust to the current business environment, technology transformations are inevitable. By ensuring those transformations are built on a solid foundation, businesses will come out on the other side more efficient and nimble, with the help of new technology such as AI, leading to more time-sensitive and confident strategic decisions.
The information set forth in this presentation contains the analysis and conclusions of the author(s) based upon his/her/their research and analysis of industry information and legal authorities. Such analysis and conclusions should not be deemed opinions or conclusions by Forvis Mazars or the author(s) as to any individual situation as situations are fact-specific. The reader should perform their own analysis and form their own conclusions regarding any specific situation. Further, the author(s)’ conclusions may be revised without notice with or without changes in industry information and legal authorities.
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1a“C-Suite Barometer: Executive Leadership Insights in the US – Adaptability: The New Competitive Advantage for US Companies,” forvismazars.us, 2026.