In October, Gartner reported that 74% of CEOs believe AI will transform their industry in 2024. Yet, 70-80% of AI projects still fail. But that’s hardly because of technology. Slack’s recent Fall 2024 Workforce Index sheds more light on a growing disconnect between executives and employees. It’s especially one data point that I can’t stop thinking about. (More on that in a second.)
In conversations with CIOs and VPs of IT, I hear that their companies already use AI—often developed and implemented by their IT teams. These are real business scenarios in which AI adds value or helps the business team do a task they were unable to do before, from supply chain optimization to product descriptions and document processing. But it’s not without struggles. Organizational dynamics, politics, and resistance to change come up more often as adoption barriers. So, how does all that fit together?
A Common Perception: Using AI Makes You Seem “Lazy”
According to Slack’s Fall 2024 Workforce Index report, 48% of respondents feel that admitting they use AI at work will make them seem “lazy.” They will also likely end up with just more work due to their newly unlocked efficiency.
Nearly half (48%) of all desk workers would be uncomfortable admitting to their manager that they used AI for common workplace tasks. The top reasons for workers’ discomfort are 1) feeling like using AI is cheating 2) fear of being seen as less competent and 3) fear of being seen as lazy.
But here’s the thing: it says more about the state of corporate culture than about AI technology if the most innovative employees—who are evidently creating new efficiencies with AI(!)—are afraid to share that they know how to use AI (and do it). By the way, they still use AI but they just won’t tell you.
A common perception of employees about admitting to using AI at work
Too often, corporate cultures and reward systems are based on output, not efficiency. Creating value for the organization and its customers is equated with the actual effort we put in (ourselves). In the daily grind to keep the wheels moving, we lack the time and the permission to increase efficiency, and we struggle to follow the mantra: “Work smarter, not harder.”
With company leaders pouring money into Generative AI tools to increase efficiency, seeing adoption efforts stall and falter because of a lack of trust is mind-blowing. Adopting AI well is not a “technology problem” but a deeply human one. Your team members are concerned about how you, as the leader, perceive them and their performance. That’s why you need to normalize it. Technology providers are typically strong on the first aspect, and it’s on your own teams to figure out how to navigate the transition.
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AI Adoption At Scale: The Simplicity of Scale, Cost, and Time
Automating business processes has largely been focused on point solutions and isolated improvements—from automating a few steps to matching, classifying, and predicting information to generating, summarizing, and translating it. But it has not been possible (yet) to drive more automation with greater autonomy.
The current scope of what AI agents are used for spans individual agents and virtual teams of agents. Soon, the next phase of inter-departmental agents will emerge before inter-company agents prepare sales proposals and quotes, negotiate terms, and dynamically optimize for available supplies between companies. As both the technology and early adopters mature in their capabilities, the adoption of AI (agents) will be driven by economic aspects.
Evolution of AI agents
Based on the iron triangle of project management:
Scope: If AI agents can perform the same task as human workers with similar risks, organizations will use agents.
Cost: If AI agents can perform the same task at a fraction of the cost and with similar risks, organizations will use agents.
Time: If AI agents can perform the same tasks in a fraction of the time and with similar risks, organizations will use agents.
If agents can further improve one of these variables while keeping the other two at least stable, organizations will use agents. Why? Because for-profit organizations are shareholder-driven, it makes economic sense.
However, leaders are not well-prepared yet to manage this transition. After all, it threatens the status quo. More change leads to more disruption. But this disruption will come one way or another—driven by internal or external forces of change!
Navigating the “Un-comfort” Zone: Encouraging Your Team’s AI Use
Leaders face lower budgets and fewer resources as the pressure on organizations to optimize operations and establish new business models increases. However, in this paradox, they need to encourage their teams to use Generative AI and create new efficiencies.
Leaders need to reward team members for experimenting and sharing their results and approaches so others can follow along. Using AI needs to become the “new normal.” Employees need to know that they can and should use AI (in compliance with company policies). But it won’t happen overnight, and it won’t happen without the leader’s active encouragement and sincere interest.
Here’s what you can do today if you’re a leader:
Find out if your company has a Generative AI use policy (e.g. which tools are permitted, and what data can/cannot be entered)
Get hands-on with Generative AI tools yourself (you need to learn about the capabilities and limitations first-hand)
Encourage your team to use Generative AI within the policy guardrails (depending on the size of your company, this might be as simple as: “Don’t enter any confidential or personal data. Check the output’s accuracy before sending it off.”)
Create space for team members to share their experience with the tools, how they use them, and how they help them (for example, team members could use the time saved to prepare a share & learn session for their peers)
Accept that your team members will likely be more skilled at using AI tools than you are (this creates a learning opportunity; you can also share your learnings!)
Need help with where to start your journey? Get in touch to explore the best path forward.
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