In many life situations, if there is a high risk, the tendency is to slow down. It’s common to take your time, think through things and move cautiously until you’re ready or have nearly perfect knowledge. A familiar example, among many exceptions, is learning how to ride a bicycle.
Intuitively, as a beginner, going faster on a bicycle seems riskier. But it also becomes clear once you start that going slower makes it harder to maintain balance. The best option is to pick up speed; balancing becomes easier, requires less effort and results in a smoother ride.
So how does this relate to AI or technology? This analogy has relevance when considering Protiviti’s 2025 Top Risks Survey results. Many firms need to shift gears in their artificial intelligence (AI) and automation efforts to mitigate risks related to talent and the competitive environment. Going slow in the AI game means increased risk.
In the survey, three of the top five risks identified by chief information officers and chief technology officers pertain to labor and talent. Whether it’s the rising costs of talent, its limited availability or lack of requisite skills, information technology executives are concerned about these various risks that show job demand continuing to outstrip supply.
Somewhat surprisingly, there is a noticeable year-over-year decrease in the perceived risk of AI and other emerging technologies as disruptors. While it’s crucial not to overlook the talent side of risk, companies should view these results as a wake-up call to accelerate their AI or automation efforts. They can offset some of these risks by doing the following:
- Rethink business processes: How can AI and automation drive efficiencies in key business areas?
- Redefine job roles: How can AI and automation simplify tasks and roles?
- Consolidate expertise: How can we ensure that resources are focused on high-priority AI and automation opportunities to build momentum?
Balance prototype development with big-picture thinking
For many of our clients, a solid first step in their AI journey is developing a strategy for how AI can add value to their business. This often involves developing a focused set of use cases prioritized by business value, scoped tightly enough for quick prototyping. Mastering this process in the early stages of AI adoption is essential, according to Protiviti’s AI Pulse Survey, not just to move past the experimentation stage and realize AI’s full value, but also to reduce challenges that might delay or prevent progress.
This approach allows organizations to evaluate an idea’s value or its technical feasibility quickly. Almost every business process can be improved by these technologies, reducing decision-making time, eliminating hours of work and sometimes reducing headcount needs.
Organizations should continually balance this approach with thinking big — rethinking broader business processes by linking multiple AI use cases within a function (e.g., finance) or starting with strategic exercises to answer questions like “How could we apply emerging technologies to halve our labor costs?” or “If we redesigned this business process tomorrow, what would it look like?”
The goal isn’t to arrive at a specific solution but to get the organization thinking about leveraging AI to improve its business and expose it to how new entrants or current competitors view technology’s power. If labor-related risks are your primary threat, you need to rethink your business processes by automating repetitive tasks and equipping existing staff with AI-driven insights to unleash their potential.
Reshaping jobs to incorporate AI, automation and robotics effectively is increasingly becoming the new normal. This process is iterative rather than a one-off effort, encompassing the entire cycle of work and organizational design, skills analysis, assessment and talent acquisition. Currently, the pace at which many are doing this today is simply too slow.
Don’t underestimate your people
Most organizations lag in rethinking job roles, partly because of fear of alienating their workforce. However, the latest advancements in generative AI suggest that this fear underestimates employees’ capacity to appreciate its benefits. Tools like ChatGPT have revolutionized AI’s accessibility, making its potential transparent and easy to interact with.
A significant portion of the workforce is already leveraging AI through tools like Microsoft Copilot. Employees are aware of AI’s potential and are eager to use tools that simplify their tasks, enhance creativity and improve efficiency. Companies should be transparent about their objectives in this area and the potential impact on the employee population, as reducing effort appeals to employees and organizations alike. Often, the capacity freed up can allow a greater focus on innovation or community contributions.
Many organizations have yet to harness the full power of these tools to boost individual productivity. It might be surprising how many organizations lack plans to ensure that employees understand AI tools’ capabilities, or to discover why adoption rates vary. This responsibility often falls on HR departments, which are frequently stretched thin. Change of this magnitude should be led, with HR support, by business-process owners who can consider the bigger picture and the intersection of redesigned processes and job roles.
Create an AI Center of Excellence
Many organizations, as highlighted in the Top Risks Survey, lack the in-house talent to advance AI initiatives at the desired pace. Talent is often spread thin trying to assist multiple business areas simultaneously. This decentralized approach, although well-intentioned, slows progress and hinders prioritization and results.
To accelerate progress, creating an AI Center of Excellence (COE) is becoming a popular strategy. The goal is to focus limited AI resources on the most significant opportunities for business performance and competitiveness. COEs, with specific target operating models, streamline idea intake, prioritization and rapid prototyping to production. These models facilitate asset harvesting, enabling organizations to grow smarter with each rollout and provide governance around processes, risks and communication.
Moving forward: How to accelerate
Based on our experience, the following strategies can expedite business transformation to address talent and competitiveness risks:
- Develop a clear AI strategy: Clearly communicate the highest-priority use cases and the processes offering the most transformation potential. This will help to ensure that everyone understands the opportunities AI provides.
- Connect strategy to execution: Many companies develop great use cases but then stall. Once high-impact use cases are identified, move quickly. An AI COE with a target operating model can aid this process. For context, we often spend a few days with clients scoping top use cases, then proceed to coding and development in a week.
- Think holistically but act swiftly: The pandemic showed how rapid digital transformation can occur when survival is at stake. The pace of AI, automation and low/no-code innovation is outpacing businesses’ ability to adapt. This gap presents challenges and opportunities for those who act aggressively. Ten years ago, when I entered the AI space, our guiding mantra was “Think big, start small, move fast,” a principle that still holds true.
- AI literacy program: Organizations should develop an AI literacy program to equip individuals with the understanding and skills to engage with AI technologies effectively. The focus should be on defining and assessing critical skills, creating an upskilling roadmap and building career pathways to ensure continuous employee engagement.
Now is the time to reassess how AI can transform business processes, key job roles and the overall operating model to accelerate idea-to-production timelines. This initiative requires discipline, excellent communication, and courage to face the inevitable evolution of business processes and the workforce due to these new technologies. The speed of this transformation can either be a competitive advantage or can increase risks across the board.
