The role of the chief information officer (CIO) has undergone radical changes with continuous upgrading of IT-driven business processes. As a CIO, I faced rapid changes that drove artificial intelligence (AI) from a fad to a predictable certainty. No longer are leaders asking if it is a good idea. Today, C-suite leaders ask what, when, and how AI will be deployed across businesses.
However, CIOs face obstacles during and after the execution of AI projects. Overcoming these challenges is essential for companies to maintain their focus on organizational objectives rather than creating stress among employees and simply adopting AI for its own sake. I’m excited to share insights on how implementing AI strategies at the enterprise level can turn employee overload and implementation challenges into opportunities.
Are AI improvements increasing workloads?
Like any IT project, AI endeavors that flow smoothly with defined objectives are more likely to deliver tangible business outcomes. One critical factor in a successful AI implementation is the presence of clear, value-driven use cases. Unfortunately, many CIOs do not anticipate the issues that occur during the aftermath of the AI project.
While defining the business outcomes of an AI project, unanticipated challenges arise that affect the success of businesses. As we navigate these challenges, we recognize that AI can sometimes create more work than it saves. The list includes legal compliance issues, output inaccuracies known as hallucinations, or an actual increase in workload.
For instance, while AI can help employees craft emails faster, it may lead them to respond to ten times as many emails, resulting in more work for their colleagues who must read and respond to those messages.
Here are other examples of AI creating more work than it saves:
- Increased report generation: AI can enable employees to produce reports more quickly, often leading to an increased number of overwhelming reports that others need to review. Instead of focusing on high-impact insights, teams may be sifting through excessive documentation.
- Content creation overload: Due to the ease of content creation with AI tools, employees might generate multiple documents on less relevant topics. This flood of low-quality content can clutter knowledge bases and make it difficult for others to find valuable information.
- Administrative task inflation: While AI can automate routine tasks, employees often time spent on other low value tasks may grow. For example, they may spend more time managing increased volumes of administrative tasks instead of engaging in higher-value activities.
- Attention fragmentation: AI tools that allow employees to juggle multiple projects simultaneously can lead to attention fragmentation. Employees may attempt to manage too many tasks simultaneously, resulting in burnout and decreased decision-making quality.
- Overwhelming from too many tools: The rapid introduction of various AI tools can create confusion and inefficiencies, as employees need help keeping up with and learning new systems while maintaining productivity in their existing workflows.
- Quality control issues: The ease of generating content can decrease quality control, requiring additional time for editing and revisions. For instance, creative teams might spend more time refining AI-generated drafts than they would have spent creating original content from scratch.
Charting a path to post AI deployment success
To overcome these challenges, we have adopted a strategic approach focused on fundamental principles:
- Focus on high-value use cases: Shift from implementing “AI for the sake of AI” to targeting specific business problems where AI creates tangible value. Never forgetting to how operations will be affected after an implementation. This focus drives stakeholder buy-in and demonstrates clear ROI.
- Prioritize data infrastructure investment: Emphasize data quality, accessibility, and governance as essential pillars of our AI strategy.
- Bridge the process gap: We take an inclusive approach to developing departmental understanding of processes to predict possible overload after an AI solution is deployed.
- Adopt a phased approach: Starting with smaller projects allows us to achieve quick wins and build confidence among employees and momentum for more significant initiatives.
- Prioritize explainable AI: Building trust requires focusing on systems that clarify their decision-making processes, which is especially critical in regulated industries.
- Collaborate Across Departments: Partnerships between IT and business units following a sound DevOps process will ensure our solutions address real needs.
- Continuous improvement and adaptation: We establish a continuous improvement process for ongoing evaluation and adaptation of our deployments and strategies to acknowledge the rapidly evolving nature of technology.
The future of enterprise AI deployment success
We are looking at a future where enterprises will adopt AI APIs or deploy AI-enabled applications. Tremendous shifts lie ahead as far as enterprise operations are concerned. Amongst all the bright innovations, get excited about the promise of AI.
Even while it carries its share of frustration and obstacles, such as fragmented attention and an uptick in administrative tasks, we are still optimistic about the probable impact of AI on operational efficiencies. The concept of AI is no longer being sold to CIOs. Today, C-level executives consider AI a priority for their organizations. Further, leaders have committed funding to the cause. That’s a future for AI that all of us can be pretty excited about and look forward to seeing materialize.
However, every organization must bear in mind the limitations of AI models as they exist today. Specifically, do desk workers completely trust the output?
This is a resounding no. Oversight is necessary for buy-in with frontline workers. As CIOs, we stand at the forefront of such transformations. We’re responsible for sifting through technological possibilities and translating them into tangible business strategies while helping our organizations navigate the complexities and frustrations around adopting these innovations.
Most importantly, we must sharply focus our strategy and process for continuous improvement and adaptation in exploring the capability and roadblocks of AI.
There is tremendous promise to increase productivity and drive innovation in many industries and this requires thoughtful consideration of how to implement it. These strategies must take into account the potential future of employee overload and anticipate business workflows. Balancing these factors is essential as we navigate this transformative landscape.
As a CIO, one must boldly work across departments to create process efficiencies, increase business value, and predict roadblocks that hinder improvement of employee workload.
