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Artificial intelligence is now a part of everyday life and is making its presence felt more and more in the business world. However, akey question for finance and technology leaders looms: are companies, employees and executives really prepared for AI?
Deploying AI and machine learning applications can mean a lot of changes and the introduction of risk, and enterprises need to be ready.
“While organizations see the transformative potential of AI, executives and employees often struggle to prepare for its workplace integration,” said Rajprasath Subramanian, principal enterprise architect, business and technology innovation, at enterprise software company SAP.
“This is largely due to a lack of comprehensive understanding and training about AI capabilities, especially given the significant advancements in areas such as agentic AI and large language models,” Subramanian said.
In addition, there is a prevalent concern regarding job displacement due to AI adoption, leading to resistance or apprehension among employees. He mentioned in detail how this fear can hinder proactive engagement with AI tools and limit opportunities for upskilling.
Subramanian advised CFOs to stay aware of the rapid advancement of AI and how it often outpaces organizations' ability to provide necessary training, leading to a potential skills gap.
AI can be a disruptive technology, and that naturally presents challenges for companies. A survey of 3,450 C-suite leaders and 3,000 non-C-suite employees conducted by IT and business services firm Accenture found that many C-suite leaders and employees anticipate change will continue at a high pace in 2025 and both groups feel less prepared to respond to it than they did a year before.
More than half of C-suite leaders (57%) said they feel their company is not fully prepared. And while 2024 was the year of generative AI, Accenture said, after 12 months of rapid adoption only half of C-suite leaders say their organizations are fully prepared for technological disruption. Only 36% say they have scaled generative AI solutions.
“Most companies lack a common AI foundation, making it hard to balance the right speed with the right controls that an enterprise needs to move to scale,” said Lan Guan, chief AI officer at Accenture.
Guan mentioned how nearly one-third of the C-suite executives surveyed by his organization said limitations with data or technology infrastructure is the biggest hurdle to implementing and scaling gen AI.
“Many CIOs are still hesitant to deploy and scale new AI tools because AI costs are a moving target,” Guan said. “With breakthroughs happening every week, AI can quickly become the new source of technical debt, and the abundance of choices can be overwhelming and can paralyze decision-making.”
He added companies need to customize AI with their specialized data, “and most companies are struggling to find easy ways to do this.”
When asked about what might cause a lack of preparedness for AI and what factors contribute to an organizations’ ability to get the most value out of AI, Guan said it comes down to the investment strategy and the implementation process.
“Generative AI is projected to improve productivity by more than 20 percent over the next three years, so a lack of preparedness for gen AI means lost productivity and failure to achieve meaningful ROI on the investments companies are directing to gen AI,” Guan said. “Companies lagging in AI adoption and proficiency may find it challenging to compete with industry peers who have effectively harnessed AI for innovation and decision-making.”
In addition, a workforce unprepared for AI might struggle to adapt to new workflows, resulting in disruptions and decreased productivity during the transition period. “Without proper understanding and training, employees may not fully leverage AI tools, leading to suboptimal performance and missed opportunities for efficiency gains,” Subramanian said.
Another potential setback is the impact on employee morale and retention. “Fear and uncertainty about AI's impact on job roles can lead to decreased employee morale, engagement, and increased turnover rates,” said Subramanian.
Devopling an AI strategy
CFOs can take steps to help prepare their organizations for the broader use of AI.
One is to develop a clear AI strategy. “CFOs should collaborate with other C-suite executives to establish a comprehensive AI strategy that aligns with the organization's overall vision and mission,” Subramanian said. This involves identifying areas where AI can add value, setting realistic adoption timelines and allocating necessary resources.
Another step is to invest in employee training and upskilling. “To effectively bridge the skills gap, CFOs must champion initiatives focused on developing employees' AI literacy and competencies,” Subramanian said. “Pharmaceutical companies like Johnson & Johnson have implemented mandatory generative AI training for over 56,000 employees, ensuring their workforce is equipped to integrate AI into various business processes.”
CFOs also need to ensure their organizations have robust data governance and infrastructure. “Effective AI implementation relies on high-quality data and the infrastructure to process it,” said Subramanian. CFOs should work closely with chief data officers and CIOs to establish strong data governance practices, ensuring data accuracy, security, and compliance with regulations.”
Subramanian stressed CFOs should invest in scalable IT infrastructure to support workloads. “For instance, companies are advised to have a robust IT infrastructure in place to process large volumes of data, as larger datasets require greater computing power,” he said. “By proactively addressing these areas, CFOs can facilitate a smoother transition into AI adoption, positioning their organizations to fully capitalize on the benefits of AI technologies.”
To access the value of AI, companies should rethink work processes Guan advised. He added that CFOs should build a strong digital core that is dynamic rather than static, and to collaborate in building a talent pipeline and development system that relishes opportunities to explore with generative AI.
To support this, Guan said CFOs must take a multifaceted approach. “CFOs must encourage their organizations to build a strong technology foundation, enforcing data governance and operationalizing AI to drive scalable, high-impact outcomes,” he said.
