Observed Signal · Sep 9, 2026 · Research Report · Source: https://martechseries.com/feed/ · Impact: 2/5 · Sentiment: Positive
Only one-third of enterprises put AI agents to work
A new report from Tempo Software reveals that while 91% of enterprises are piloting or using AI in project delivery, only 33% delegate actual work to AI agents. Organizations with agents in production report faster delivery times, with 71% of projects finishing within six months versus 55% for those without. The report highlights governance challenges, as 39% of leaders cannot distinguish AI-produced work from human work, and 42% cannot tie AI spend to ROI. Tempo introduced Workforce Intelligence to address attribution. The findings underscore the need for Intelligent Portfolio Orchestration to manage a blended human-AI workforce.
Report provides insights into AI agent adoption and productivity, relevant to AI-powered marketing and project management.
Track Real-Time AI Agents Signals & Market Shifts
Polaris7 autonomous intelligence agents track regulatory filings, primary sources, executive changes, and deal flow 24/7. Create your free Explorer workspace to monitor these entities.
Key Takeaways & Evidence Grounding
- 91% of enterprises are piloting or using AI in project delivery.
- Only 33% delegate actual work to AI agents.
- 71% of projects finish within six months at organizations with production agents, vs 55% without.
- 42% of leaders cannot tie AI spend to ROI.
- Tempo Software launched Workforce Intelligence to provide AI work attribution.
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Agentic AI: 80% of Companies Still Testing AI Agents
A new Lünendonk study reveals that autonomous AI agents, while strategically important to many companies, are still mostly in pilot or exploration phases. Only 19% of surveyed companies use AI agents in specific tasks, and just 1% have achieved end-to-end integration across core processes. The study, titled 'Agentic AI: From Copilot to Autopilot', highlights a significant gap between expectation and implementation, with 96% expecting efficiency gains and 90% anticipating increased speed and flexibility. Scaling from pilot to production remains difficult due to technical shortcomings, inadequate data integration, security concerns, and a lack of skilled personnel. Companies are planning heavy investments in data quality, orchestration platforms, and IT infrastructure over the next two years, as well as in digital enablement and AI compliance. Most expect AI agents to supplement existing applications rather than replace them entirely.
Talkdesk report: AI in CX outpaces orchestration
Talkdesk published “The State of Agentic Automation in CX,” reporting that AI deployment across customer journeys is nearly universal but organizational orchestration is lagging. The survey of 252 mid-market and enterprise CX, IT, operations, and AI leaders (fielded April 2026 by NewtonX) found 98% of organizations have deployed AI, yet only 15% combine agentic AI with cross-departmental orchestration and 85% lack the orchestration to connect AI agents, human teams, data, and workflows. The report highlights hidden costs from fragmented AI (unfinished workflows, increased human handling) and shows that organizations with higher CXA maturity achieve materially better outcomes. Talkdesk and cited analysts argue that multi-agent orchestration and operating models for a hybrid human-AI workforce are the critical capabilities to translate AI activity into measurable business impact.
Enterprise AI Agents Still Very Early
The author attended meetings in Chicago with ~50 enterprise CIOs, CTOs and AI heads and found that widespread, scaled deployment of agentic AI inside regulated, legacy-heavy enterprises is still nascent. Few organizations reported agents in production; common barriers include security, unclear governance, legacy system modernization, and difficulty measuring ROI. Cost management (token spend) is emerging as a top pain point—cited by Uber's internal token-budget issues—and firms expect model routing (frontier models for high-value work; cheaper models for other tasks) and stronger context layers (ServiceNow/Atlassian/Claude examples) to be critical. The piece argues the biggest commercial opportunity is tooling and services that map and redesign workflows, provide enterprise context/ownership, enforce governance, and control costs as agents move toward production.
Track Real-Time Market Signals & Shifts
Set up custom watchlists to receive automated, evidence-grounded executive digests whenever material signals or shifts occur across your tracked landscape.