It’s well documented that AI yields individual productivity gains.
However, as more organizations invest in AI, it’s becoming increasingly apparent that AI initiatives need a clear strategic direction to achieve long-term success.
The initial excitement of AI’s technological potential is fading as leaders consider how to scale its impact to create measurable returns.
This article will discuss the importance of activating AI at the team level and will introduce a practical, research-backed framework for successfully scaling AI across the organization.
The Importance of Team-Level AI Adoption
Recent research from McKinsey and MIT reveals that approximately 80-95% of organizations that invest in AI see no measurable enterprise-level ROI.
While there are many nuanced reasons for this figure, HBR suggests that unstructured, unmeasured individual AI use is likely a key factor.
Research: For many organizations, AI is in the “Trough of Disillusionment” stage of the Gartner Hype Cycle. This designates the phase beyond the initial excitement in which experiments aren’t delivering as expected or hoped. To move to the next stages (The “Slope of Enlightenment” and the “Plateau of Productivity”), organizations must figure out how to align AI with their organization’s specific needs and scale it across teams.
6 Reasons Teams Struggle with AI Adoption
Team-level AI adoption is notoriously difficult.
Here are six common barriers that might sound familiar to leaders hoping to accelerate transformation:
- Limited trust in AI: Concern that AI will replace jobs, hallucinations, and frustration with low-quality “workslop” can hinder openness to AI.
- Unclear value: A Gallup poll revealed that only 16% of employees who use AI strongly agree that the tools their organization provides are useful, with the most common challenge to adoption being “unclear use case or value proposition.”
- Solutions don’t stick: Experimentation without focus or connection to business outcomes creates very few scalable opportunities.
- Cross-functional challenges: Siloed AI use creates “disconnected efficiency gains” but doesn’t solve coordination problems or move the needle on business outcomes.
- Lack of clear guidelines: A lack of structure or clear guidelines surrounding AI use can cause doubt or discomfort, according to Gallup.
- Manager attitudes: Some leaders may feel that using AI is essentially “cheating” because work should “feel busy.” This can discourage teams from experimenting with AI.
According to UC Professor and author of “The Digital Mindset,” Paul Leonardi, another significant barrier to AI adoption is “AI overload.” Tools and trainings aren’t going to make a meaningful impact if they’re not targeted at real business problems.
“The focus should be about ‘how do we improve our work and improve our workflows?’” says Leonardi in the recent Teamraderie Leadership Lab webinar, AI at Work: Transforming How Teams Work. “AI is just a tool to help us do that.”
Accelerating Team-Level AI Adoption: 4 Steps
Individuals use AI for useful purposes like generating content, summarizing documents, and automating tasks.
The next step is to accelerate workflow redesign with AI so that the system—not just people—speeds up.
Below are four important steps to take to accomplish this.
1. Experiment at the Team Level
The first step to enabling scalable success with AI is activating it at the team level.
Research: A recent HBR article suggests that AI adoption can’t be perceived as a “single decision,” but rather a collection of systemic organizational experiments. The authors make the case that integrating experimentation into the organization’s culture and then scaling what works is how successful companies will make AI useful, rather than just impressive.
This necessitates turning siloed individual experiments into structured team experiments.
It’s important to note that giving employees access to AI tools isn’t enough. According to Leonardi, the question teams should ask is “Where are opportunities with the capabilities of our tools to do things we haven’t done before or to do old things in new ways?”
Here are five tips for getting teams started with AI:
- Provide structure: According to Gallup, around 70% of organizations don’t have either formal or informal guidelines for using AI. This is an important first step to enabling transformation.
- Create a shared mindset around AI: In an HBR article, Leonardi explains that AI transformation demands a “digital mindset” where team members think creatively and strategically about new opportunities and innovative approaches to existing work.
- Start small and scale: Research from MIT shows that companies that see the most value from AI start with “small t transformations” to build the foundation for larger-scale transformations.
- Bring cross-functional teams into the conversation: Successful AI use requires collaboration between HR and Technology. According to Leonardi, “AI doesn’t like silos… It wants to reach out and pull together things that look similar, no matter where they live in the organization.”
- Empower frontline innovation: Top-down initiatives are more likely to frustrate teams than prompt innovation. According to Leonardi, “The real innovation comes from the people who are on the front lines struggling with how to get their work done and envisioning, ‘okay, I bet I could do this better if I had this capability.’”
In short, it’s important to start by inviting team members to the discussion and equipping them to begin reimagining workflows.
AI transformation won’t come from giving teams access to an abundance of tools and hoping that something happens, but by a strategic activation of team-level experimentation.
2. Prioritize Team Use-Cases
Once you’ve begun experimenting at the team level, the next step is to establish a process for identifying and scaling successful use cases.
This requires structured thinking about where AI capabilities intersect with business needs.
- Break workflows into tasks: Identify which tasks in a workflow need to be manual, which can be augmented with AI, and which can be automated with AI. This gives you a clearer picture of how AI can help, rather than jumping to automation.
- Measure and iterate: Define target KPIs up front and measure experiments against those KPIs. It’s important to track workflow performance, not just time savings and productivity.
- Start with low-hanging fruit: Don’t try to drive company-wide transformation immediately; instead, begin with routine, repeated workflows where there’s a clear opportunity for automation and build upon those successes.
- Leverage employee expertise: A MIT study found that the employees who are most effective at determining best use cases for AI are those with the most experience, since they’re able to draw a better comparison between past results and AI-driven improvements.
As your team experiences small victories, build on the foundation of those wins to create confidence and momentum.
3. Make Learning a Process
Team learning is key to AI transformation.
Research on team learning shows that the most effective learning is dynamic and iterative, defined by the following factors:
- Ongoing: Team learning is a continuous process involving multiple iterative learning sessions, rather than a one-time event.
- More than information-sharing: It requires a shared mindset, diverse viewpoints, and the ability to adjust strategies based on learnings.
Read more: What Is Team Learning & Why Is It Important?
Scalable AI-driven workflow improvement requires experimentation, review, and iteration. This learning process should cycle until measurable workflow improvements are realized.
4. Create Team-Level Accountability
Accountability often has negative connotations in the workplace, but when framed as ownership rather than punishment, it’s an important lever of success.
Read more: How To Elevate Accountability in the Workplace: 6 Tips
Team members need to support each other and hold one another accountable.
A common approach is appointing “AI Champions” or “AI Ambassadors” to help reduce friction to taking action.
“The idea [behind AI champions] is we want well-placed people who understand the technology,” says Leonardi. “Those people help to inspire others to think bigger about what they could be doing in this big era of transformation.”
It’s also important to create an accountability framework for measuring team progress.
Below is an actionable framework to help you lead AI transformation at your organization.
Measuring Team-Level AI Adoption: A Practical Framework
Before you begin scaling AI across your team or organization, you need a clear understanding of where you currently stand.
Teamraderie’s AI index is a new, research-backed diagnostic tool to measure where your team stands on AI adoption and identify next steps.
- Activation measures team usage, shared direction, use case clarity, experimentation quality, and learning cadence.
- Institutionalization measures the ability to make AI into durable, metric-tracked process changes to workflows across the organization.
The most successful teams using AI move right and up along the curve.
If you want to measure where your team currently stands, take 2 minutes to complete our AI assessment. This quick questionnaire will help you determine your team’s current AI proficiency and provide actionable next steps to take your team to the next level.
