Enterprise AI ROI: From Initial Testing to Proven Results through Human and AI Collaboration

Why AI ROI Still Feels Uncertain

AI is all around us, yet the returns often sound more like promises than tangible results on the balance sheet. Most organizations have moved beyond the experimental phase; the current challenge is proving value at scale and encouraging people to actually use the tools. Technology is rarely the barrier. The greater challenge is human: shallow adoption, scattered use cases, and resistance from teams who do not see how AI benefits their work.

The Real Issue with “Enterprise AI”

Purchasing tools is not the same as transforming the way work is done. Success hinges on scaled adoption with clear accountability and evident benefits. This requires shifting the conversation from what the tool accomplishes to what the results are—a Human + AI model that enhances people's capabilities while keeping them in control. Asana’s product direction supports this: “AI teammates” are designed to collaborate with teams, operate under enterprise controls, and hold humans accountable for outcomes.

A Practical Strategy to Drive ROI and Adoption

1) Measure impact with a clear framework.
You need more than just anecdotes. Asana’s 4-step ROI framework focuses on both business and human outcomes—time saved and throughput, yes, but also adoption, satisfaction, and collaboration impact—backed by real-world examples you can replicate. Use it to establish a baseline, conduct a pilot, and compare before/after outcomes with credible metrics.

2) Enable teams with manageable, practical AI.
Tools should integrate into daily work, not operate as side projects. Asana AI Teammates work within your work operating system—creating content, synthesizing research, flagging risks, and executing rule-based tasks—while adhering to enterprise-grade permissions and human checkpoints. This isn’t autonomy for its own sake; it’s automation you can manage.

There’s increasing evidence of significant gains when workflows are constructed this way. Clear Channel Outdoor reported a ~60% reduction in manual intake work—about 15 hours saved per creative request during an AI Studio pilot, and their CMO Dan Levi summarized the moment: “If there are tools that allow us to work smarter, faster, and more effectively, it would be unwise not to use them.”

3) Lead the change like a program, not a side project.
Approach AI like any transformation: visible sponsorship, clear governance, incentives that reward adoption, and structured learning. Recent leadership talks—from Betterment and ThredUp alongside Asana—highlight practical strategies: track adoption, scale the successful use cases, and build Human + AI teams without chaos. In parallel, Asana’s own leaders share how they encourage adoption culturally (e.g., making AI impact part of performance discussions).

What Successful Enterprise AI Looks Like

When you apply a measurable framework, embed AI where work happens, and lead change deliberately, three outcomes follow.

You gain clarity. You can identify where AI is effective—and where it’s stalled—using defensible metrics your board will respect. (The Asana framework aids you in making that case.)

You accelerate without losing control. Teams automate repetitive tasks—brief enrichment, status summarization, risk flagging—while humans remain accountable. This is the core promise of AI Teammates: speed with oversight.

You build organizational trust. Resistance decreases when people see time returned to meaningful work. Clear Channel’s pilot result—~15 hours saved per request—is the kind of proof that changes minds and budgets.

FAQs

How do we measure AI ROI beyond “time saved”?
Combine hard metrics (cycle time, throughput, error rates) with human metrics (adoption rate, satisfaction, collaboration quality). Asana’s 4-step ROI framework provides a simple, repeatable method to do this, with examples you can adapt.

Will agentic AI operate without oversight?
It shouldn’t. AI Teammates are designed for human-in-the-loop control, inherit enterprise permissions, and display their work through checkpoints—enabling teams to trust what’s happening and intervene when necessary.

Is there credible proof of real time savings?
Yes. In a publicly shared pilot, Clear Channel Outdoor reduced manual intake work by about 60% (~15 hours per request) using Asana AI Studio. Your results may vary, which is why we recommend a short, structured pilot first.

How do leading marketers approach adoption?
Recent panels with Betterment and ThredUp emphasize tracking adoption, scaling proven use cases, and managing Human + AI teams. Asana leaders also share internal tactics for sustaining adoption.