Enterprise AI ROI: From Pilots to Proof with Human + AI Collaboration

Why AI ROI still feels slippery

AI is everywhere, yet the return often reads like a promise rather than a balance-sheet result. Most organisations have moved beyond dabbling; the new problem is proving value at scale and getting people to actually use the tools. Technology is rarely the blocker. The harder part is human: shallow adoption, scattered use cases, and resistance from teams who can’t see how AI helps their work.

The real problem with “Enterprise AI”

Buying tools isn’t the same as changing how work gets done. Success depends on scaled adoption with clear accountability and visible benefits. That means reframing the conversation from what the tool does to what the work achieves—a Human + AI model that augments people and keeps them in control. Asana’s product direction leans this way: “AI teammates” are designed to collaborate with teams, operate under enterprise controls, and keep humans accountable for outcomes.

A practical strategy to drive ROI and adoption

1) Measure impact with a clear framework.
You need more than 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—supported by real-world examples you can replicate. Use it to baseline, run a pilot, and compare before/after with credible metrics.

2) Enable teams with controllable, practical AI.
Tools should fit into daily work, not sit off to the side. Asana AI Teammates work inside your operating system for work—creating content, synthesising research, flagging risks, and running rule-based tasks—while following enterprise-grade permissions and human checkpoints. This isn’t autonomy for its own sake; it’s automation you can govern.

There’s growing evidence of material gains when workflows are built 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 summed up the moment: “If there are tools that allow us to do our work smarter, faster, more effectively, it would be crazy not to use them.”

3) Lead the change like a programme, not a side project.
Treat 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 playbooks: track adoption, scale the use cases that work, and build human + AI teams without chaos. In parallel, Asana’s own leaders share how they nudge adoption culturally (e.g., making AI impact part of performance conversations).

What successful Enterprise AI looks like

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

You gain clarity. You can point to where AI is working—and where it’s stalled—with defensible metrics your board will respect. (The Asana framework helps you make that case.)

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

You build organisational trust. Resistance softens when people see time returned to meaningful work. Clear Channel’s pilot result—~15 hours saved per request—is the sort 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 offers a simple, repeatable way to do this with examples you can adapt.

Will agentic AI run without oversight?
It shouldn’t. AI Teammates are designed for human-in-the-loop control, inherit enterprise permissions, and show their work via checkpoints—so teams can trust what’s happening and intervene when needed.

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

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