Artificial Intelligence is stepping into business sectors. Is your company geared up for this shift?
AI and Business: Are Companies Ready for the AI Revolution?
Artificial Intelligence (AI) is becoming more prevalent in everyday life, including the business world. However, a significant question remains unanswered: are organizations, employees, and executives truly prepared for AI?
Implementing AI and machine learning applications can bring about numerous changes and introduce risks. Companies need to be prepared for the shift.
"Organizations see the transformative potential of AI, but executives and employees struggle to prepare for its integration into the workplace," said Rajprasath Subramanian, principal enterprise architect for business and technology innovation at SAP.
This struggle is primarily due to a lack of comprehensive understanding and training about AI capabilities, particularly in emerging areas like agentic AI and large language models, as Subramanian points out.
Furthermore, there's widespread concern about job displacement due to AI adoption, leading to resistance or apprehension among employees. Subramanian discusses how this fear can hinder proactive engagement with AI tools and limit opportunities for upskilling.
Subramanian advises staying aware of the rapid advancement of AI and the potential skills gap it may create, as organizations' ability to provide necessary training often lags behind the technology's progression.
AI can be a disruptive technology, and that creates challenges for companies. According to a survey by IT and business services firm Accenture, many C-suite leaders and employees expect change to continue at a rapid pace in 2025, and both groups feel less prepared to respond to it compared to a year ago.
More than half of C-suite leaders (57%) acknowledged their company is not fully prepared, and after a year of rapid AI adoption, only half of C-suite leaders claimed their organizations are fully prepared for technological disruption. Only 36% said they have scaled generative AI solutions.
"Most companies lack a common AI foundation, making it difficult 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.
Key concerns for organisations include limitations with data or technology infrastructure being the biggest hurdle to implementing and scaling generative AI, according to nearly one-third of C-suite executives surveyed by Accenture.
Other challenges include AI costs being a moving target, creating a sense of technical debt, and the abundance of choices leading to indecision. Companies must customize AI with their specialized data, and many struggle to find easy ways to do this.
When asked about factors contributing to a lack of preparedness for AI and an organization's ability to get the most value out of AI, Guan said it comes down to investment strategy and implementation process.
"Generative AI is projected to improve productivity by more than 20 percent over the next three years, but a lack of preparedness for generative AI means lost productivity and failure to achieve meaningful ROI on investments companies are directing towards generative AI," Guan noted. "Laggard companies in AI adoption and proficiency may struggle to compete with industry peers who have effectively harnessed AI for innovation and decision-making."
In addition, an underprepared workforce might struggle to adapt to new workflows, leading to 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.
Preparing for AI Adoption
Companies can take steps to prepare their organizations for broader AI use.
- Develop a clear AI strategy.
- Collaborate with other C-suite executives to establish an AI strategy that aligns with the organization's overall vision and mission.
- Invest in employee training and upskilling.
- Champion initiatives that develop employees' AI literacy and competencies.
- Ensure robust data governance and infrastructure.
- Work closely with data officers and CIOs to establish strong data governance practices.
- Invest in scalable IT infrastructure.
- Establish a robust IT infrastructure to support workloads, especially when dealing with larger datasets.
By effectively addressing these areas, companies can facilitate a smoother transition into AI adoption, positioning their organizations to fully capitalize on the benefits of AI technologies.
To get the most value from AI, companies should rethink work processes and adopt a dynamic, digital core, encourages Guan. This strategy involves enforcing data governance, operationalizing AI, building a talent pipeline, and an ongoing, multifaceted approach.
References
- McKinsey & Company: "Unlocking AI potentional to drive growth and innovation."
- World Economic Forum: "Artificial intelligence is accelerating job displacement, here's how to Mitigate its Impact."
- Forbes: "How AI will redefine your future workforce."
- TechTarget: "What is data governance?"
- Deloitte: "Artificial intelligence: What it means for organizations and the workplace."
- Companies must recognize the transformative potential of AI and invest in comprehensive understanding and training for employees, particularly in emerging areas such as agentic AI and large language models, to prepare for its integration into the business.
- Organizations can face challenges in adopting AI due to the abundance of choices leading to indecision, AI costs being a moving target, and the limitations with data or technology infrastructure being the biggest hurdle to implementing and scaling generative AI.
- To capitalize on the benefits of AI technologies, companies should develop a clear strategy, collaborate with other C-suite executives, invest in employee training, ensure robust data governance, and establish a scalable IT infrastructure.
- Companies needing to respond to the rapid AI transformation in 2025 may struggle due to less preparation compared to a year ago, leading to potential losses in productivity and failing to achieve meaningful ROI on investments directed towards generative AI.
- In the era of AI and finance, organizations should leverage AI tools to improve productivity by more than 20 percent over the next three years, but this requires an agile mindset, investment in employee upskilling, and a dynamic, digital core that enforces data governance, operationalizes AI, builds a talent pipeline, and embraces an ongoing, multifaceted approach.
