Harness Launches Two New Products to Give Enterprise Engineering Teams Full Visibility into ROI of AI Spend

Harness, a leader in the AI Software Delivery Platform space, has announced the launch of two new products designed to provide engineering organizations with real-time visibility into AI-related expenditures. As enterprises navigate the complexities of widespread AI adoption, the tools—AI DLC Insights and Cloud & AI Cost Management—aim to bridge the gap between financial investment and measurable business outcomes.

The introduction of these products comes at a time when the pressure to demonstrate the return on investment (ROI) for artificial intelligence is intensifying. According to the company’s 2026 State of Engineering Excellence report, 94% of engineering leaders feel that their current measurement frameworks lack the critical metrics necessary to evaluate the success of their AI initiatives.

Connecting Developer Activity to ROI

AI DLC Insights addresses the challenge of tracking developer productivity in an environment where AI assistance is becoming standard. By utilizing an on-machine agent, the tool monitors token usage across various coding tools—such as Claude Code, Cursor, GitHub Copilot, and Windsurf—and maps this spend directly to specific pull requests, tickets, and deployments.

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This granular approach allows organizations to identify inefficiencies, such as bloated prompts or abandoned code, while also tracking how AI-assisted work impacts DORA metrics and overall cycle times. The product is intended to help teams distinguish between productive AI usage and wasted resources.

Did You Know? Worldwide AI software spending is projected to reach $2.59 trillion in 2026, representing a 47% increase over the previous year.

Managing Infrastructure Costs

Beyond the development phase, the Cloud & AI Cost Management tool focuses on the unit economics of AI infrastructure. As automated workflows and customer-facing AI agents trigger increased inference demands, organizations often struggle to look beyond top-level invoice line items.

This new solution connects directly to AI providers, including AWS Bedrock, GCP Vertex AI, OpenAI, and Anthropic. It allows for spend attribution at the agent, session, or workflow level, enabling teams to set governance controls and receive alerts regarding anomalous spending spikes.

Expert Insight: Samantha Carter notes that as enterprises move past the initial phase of AI experimentation, the focus must shift toward financial accountability. The ability to correlate infrastructure inference costs with specific business workflows represents a significant evolution in how engineering teams manage their operational budgets and prove the long-term value of their AI investments.

Looking Ahead

As these tools enter their beta phase, the industry may see a shift in how engineering departments justify their AI budgets. If these solutions successfully standardize the measurement of AI ROI, organizations could move toward more data-driven procurement strategies. A possible next step for the industry is the adoption of universal benchmarks that allow teams to compare their AI efficiency against broader organizational or industry-wide standards.

Frequently Asked Questions

What is the primary function of AI DLC Insights?
It provides visibility into developer AI token spend by mapping it to specific software delivery outcomes, such as shipped code, pull requests, and deployment metrics.

Which AI providers does the Cloud & AI Cost Management tool support?
The tool supports a range of providers and managed services, including OpenAI, Anthropic, AWS Bedrock, and GCP Vertex AI.

Are these products currently available to the public?
Yes, both AI DLC Insights and Cloud & AI Cost Management are currently available in beta within the Harness platform.

How does your organization currently track the impact of its AI investments on overall productivity?