AI Productivity Measurement Platforms 2026 Buyer’s Guide

Here’s a typical problem with AI productivity measurement platform selection. Most enterprises start with a small shortlist of potential platforms that engineering is familiar with and don’t think about what metrics actually need measured. Often overlooked are developer metrics like PR cycle time and DORA benchmarks. Eight months later, finance is asking where the AI budget went. Legal and support have quietly rolled out their own platforms using Copilot. Even worse, not all of the metrics needed appear in the dashboard the company just paid for.

The fix isn’t a better vendor list. It’s setting the criteria first, then scoring vendors against the set. This guide walks through the eight criteria an enterprise-buying committee should consider in selecting its AI productivity measuring platform. Covered platforms include Larridin, DX (GetDX), Jellyfish, Faros AI, LinearB, Swarmia and Worklytics.

Here’s the field at a glance before the detailed breakdown:

Engineering-only or cross-functional is the top criterion

Will the platform assess engineering alone, or will it need to work cross functionally, measuring all company employees who use AI? That’s the top criterion for platform assessment.

DX, Jellyfish, Faros AI, LinearB and Swarmia all have engineering-intelligence roots. Their measurements, integrations and organizational hierarchies are built around engineering terms like git activity, pull requests, sprints and delivery pipelines. Jellyfish’s FTE-allocation model and Faros AI’s DORA reporting metrics are important for measuring engineering’s output. Yet these five platforms aren’t designed to measure a marketing team’s performance drafting campaign copy, or to support a team’s AI ticket triage resolution time, or a legal team using AI for contract review. That’s an obvious drawback if you want to know your AI’s impact on your organization overall.

Two platforms designed with a wider scope are Worklytics and Larridin. Worklytics benchmarks AI adoption across teams and roles, not just engineering. Like Worklytics, Larridin also built cross functional AI usage and tracking software, and took it one step further by capturing AI spend and outcome metrics. Called Scout, Larridin’s platform captures AI and agent usage across whatever tools employees actually use, engineering included.

Our guide to AI monitoring for engineering leaders covers this topic in more depth.

How AI usage and adoption are measured

AI adoption tracking tool accuracy depends on where the data comes from and how it's measured. Some data is self-reported, such as a survey asking employees whether they used an AI tool that week, and some data is instrument driven, such as a system that tracks actual tool activity without asking anyone to report it.

Self-reported data is inexpensive, but consistently overstates actual usage. Engineering-based platforms monitor version control systems like GitHub and deployment pipelines (CI/CD) to determine AI usage, but overlook chat sessions, browser-based AI tools and IDE assistants that never reach version control.

Larridin’s AI productivity measurement platform, called Scout, takes AI productivity measurement one step further as it includes real usage and connector data. In non-engineering departments, it observes cross-platform AI usage directly, without reading the underlying code or document content. That matters both for accuracy and for privacy reasons, which are outlined in a later section. Larridin’s customers include Klaviyo, SurveyMonkey, EcoVadis and Gainsight.

AI productivity measurement platform G2 reviews are generally positive with respect to the convenience of a single data source which is better than “stitched-together spreadsheets.” Reviewers’ common complaint is the lack of transparency in the methodology. Too often a composite number is provided with no information on how the figure was calculated.

To facilitate better understanding each platform’s methodology, here are a few questions to ask.

• Can you share the raw usage events behind any summary metric, on request?
• Is the scoring formula documented anywhere a customer can read it?
• Does the platform read message or document content, or only usage metadata?
• How is a user’s activity attributed when they use more than one AI tool?
• What happens to the number when an integration goes down for a day?
• Who internally can see an individual’s usage data? Who sees only aggregated team data?

Whether spend and ROI connect to real business outcomes

The big question that’s often still unanswered by these productivity platforms is the one of utmost importance to finance stakeholders: ROI. Most platforms include limited or no AI ROI measurement tools that correlate between spend and output.

For example, DX correlates AI adoption with delivery metrics inside engineering. That's telling data, but only as it relates to code output. Jellyfish rolls AI usage into its FTE-allocation categories. That’s useful for showing where engineering time goes, but it’s limited to select engineering levels. LinearB and Swarmia both added AI adoption and cost add-ons at the team and developer level, a meaningful step toward spend visibility. But these numbers still stop at engineering and don't roll up into a company-wide ROI figure. Worklytics benchmarks adoption against peer data, which is a novel measurement for HR functions, but as of this writing, Worklytics doesn’t connect that adoption data to token spend or measured business outcomes. Faros AI’s DORA-focused metrics don’t extend into spend or ROI territory either.

At Larridin, we built Scout to address this exact information gap: Utilization times Proficiency times Value. Utilization is who’s actually using the AI tools the company pays for, versus who merely has a license. Proficiency is how well the tools are built into real workflows. Value is the impact. Our Spend & Token Intelligence and Work Intelligence products connect those three numbers to actual token cost and business results. That view spans departments, not just engineering. A finance leader building the spend case can see the fuller argument in our notes on AI monitoring and ROI measurement for CFOs. It walks through the math line by line.

Security and privacy posture of usage capture

Measuring AI productivity company wide may require different tools than an engineering- intelligence platform. AI productivity platforms can capture two types of data. Usage metadata tracks which tool is used, when and how often, and content, which covers what was actually typed, generated or committed. Legal and HR departments may have different views on how to monitor content, if at all.

Larridin’s Scout is built specifically to avoid reading code or document content. It still captures who used which AI tool, how often and in what context. But the design choice allows the platform to span engineering, legal, support and marketing without each department renegotiating what data access means. We’ve reviewed compliance in more detail for security teams, including how SOC 2, HIPAA and GDPR considerations apply to an AI usage-capture layer. Read more on enterprise-ready AI measurement and compliance and in our guide for CISOs evaluating AI monitoring.

Integration surface and toolchain fit

An engineering-metrics platform needs a narrower, deeper integration surface to cover source control, CI/CD, project management and maybe a chat tool for notifications. DX, Jellyfish, Faros AI, LinearB and Swarmia have all built mature connectors for the following:

• GitHub and GitLab
• Bitbucket
• Jira and Azure Boards
• CI/CD pipelines
• Slack or Microsoft Teams for notifications
• Single sign-on for identity and access control

Company-wide measurement needs an even wider scope. AI tools worth tracking are widespread, commonly found in the browser, the IDE, the CRM and at the support desk. They might also include half a dozen SaaS apps that aren’t even involved with software delivery. That’s the tradeoff: broader reach generally means shallower per-tool depth than a platform that’s spent years myopically focused on one department. Scout is different. It’s designed to measure AI usage wherever employees actually work, not only where code gets committed. Our developer productivity page provides more details.

Deployment complexity and time to value

How long it takes for a platform to get up and running is another data point to consider in AI productivity measurement platform selection. Engineering platforms with narrow, well-trodden integrations, particularly Swarmia and LinearB, can connect a single team’s GitHub and Jira in days. Platforms doing deeper historical analyses, like DORA benchmarking or FTE-style allocation modeling, typically need weeks of backfill and configuration before they’re ready to produce numbers trustworthy enough to show a VP.

For a company-wide AI usage platform, allow a few weeks for successful deployment to ensure time for both IT and security reviews prior to rollout. Small-team engineering rollouts through Swarmia’s free tier or LinearB’s self-serve plans remain the fastest path when the focus is engineering. Our developer productivity overview provides more detail.

Pricing model transparency

LinearB and Swarmia are the only two platforms to publish pricing. LinearB lists $29 per user per month for its Essentials tier at 50-plus developers. Enterprise, at 100-plus developers, runs $59 per user per month. Both include an AI adoption and cost add-on. Swarmia is free for under nine developers and $45 per developer per month for its Standard plan. Its AI adoption and cost module adds $5 per developer per month. DX, Jellyfish, Faros AI are also engineering-centric platforms, but they do not publish pricing.

For Larridin, the rationale for not publishing pricing is simple. Company-wide coverage across engineering, sales, support, legal and marketing doesn’t translate onto a flat per-seat number the way a single-department (engineering) tool does. Information about the enterprise size and scope plus desired AI ROI metrics is needed.

Support for multi-department rollout

An engineering-intelligence platform is built around the department’s organizational structure and output. Extending that singular platform’s scope to include marketing, legal and other departments is counterintuitive. Departmental goals and benchmarks differ and a multi-department rollout is more complicated.

Larridin’s facility with multi-department AI assessment is proven by its diverse customer base which includes marketing-led organizations like Klaviyo and SurveyMonkey, a customer-experience platform in Gainsight and a sustainability-ratings company in EcoVadis. Our guide for CHROs rolling out AI monitoring and our guide for CIOs managing a multi-department AI program both describe the multi-department rollout in greater detail.

A final note. Worklytics also offers multi-department assessment and is worth considering if the buying committee is HR-led and the goal is adoption benchmarking specifically, rather than spend and outcome tracking. Read more at our Worklytics alternatives page.

Which criteria should decide your shortlist

If the AI usage you’re worried about lives entirely inside the IDE and the pull request, an engineering-first platform is the right platform for you. DX, Jellyfish, Faros AI, LinearB and Swarmia each do that job. But the moment finance, legal, support, sales or marketing start running their own AI tools, you’ll need better tools to measure ROI. Our CFO-focused ROI guide can help.

See how Larridin measures AI usage, spend, and value across your whole company.