ROI for Implementing AI / Automation Tools

Increase Operational KPIs at a Series B Company

(Go here if you just want to play with the ROI simulator / calculator)

Executive Summary

• Opportunity: Operational efficiency translates directly into accelerated growth, higher profitability, and sustainable scalability.

• Benefit: Measurement-driven operational improvements consistently yield 15-30% efficiency gains within six months (McKinsey, 2024).

• ROI Framework: Identifies key operational domains, measures specific KPIs, and translates them clearly into financial outcomes with a probabilistic NPV model.

• Action: Immediate baseline data collection recommended for benchmarking and quick demonstration of ROI.

Outline

• Introduction

• Nota Bene

• Automation is Easy

• Immediate ROI

  • Time Savings

  • Product Delivery

  • Quality Impact

  • Employee Retention

• Long Term ROI

  • Valuation

  • Decision Making

  • Employee Satisfaction

• ROI Summary

• NPV and Payback Period

• Four KPI Areas

  • Productivity

  • Quality

  • Team Alignment

  • Administrative Efficiency

• VSM for Selecting Metrics

• Next Steps and Timeline

Introduction

Every tech startup has two engines: the one building the product, and the one running the business. While focus is often given to optimizing the building, the business engine is often run on much guesswork and intuition. This silent drag of operational friction doesn't just erode margins; it dictates a growth ceiling.

This whitepaper outlines a clear and practical ROI framework designed to measure the impact of operational improvements across a business in order to identify improvements that most directly translate into faster growth, higher margins, and greater agility—crucial factors for scaling successfully.

Now is an ideal moment to adopt this rigorous measurement approach. AI-powered measurement tools enable gathering insights about operational performance that were previously impossible to capture at scale. This creates a novel opportunity to optimize with the same precision applied to a technology stack.

Operational inefficiencies tolerable at small scale will later compound into significant costs, given the exponential growth of overhead time cost in the number of employees. Inefficient processes can silently erode margins (up to 20% in some studies), slow time-to-market, and inhibit growth capacity.

The opportunity is equally compelling. Companies that implement measurement-driven operational improvements often see sustained efficiency gains within six months, gains that may represent the difference between hitting or missing critical milestones. The framework is designed to identify both immediate 'quick win' opportunities and to build a foundation for long-term, sustainable efficiency.

This framework provides a systematic approach to identifying, measuring, and capturing such gains. It outlines four key operational areas – productivity, quality, team alignment, administrative efficiency – where targeted improvements deliver the highest ROI, supported by specific metrics that translate directly into financial impact. This approach works regardless of the solution, whether through process redesign, new tools, or AI-powered automation.

The purpose of this approach is to bring a scientific perspective to understanding the real value of operational improvements. While the organization may be full of intelligent people with useful insights, greater rigor is necessary to identify which efforts may be most valuable and by how much. Intuition alone is insufficient for this. An implementation of this framework will predictively model the value of investment in any particular effort, as well as feedback loops to quantify the actual delivered value of that investment

One way or another, the processes in place today will not support a team twice the current size. Evolution is inevitable – measured and directed growth minimizes the rate of overhead growth, while unintentional evolution deadlocks an organization. A systematic approach maximizes the marginal value of additional hires instead of monotonically decreasing that value.

It is strongly recommended to begin baseline measurement across all four areas immediately. Early data collection is critical for establishing benchmarks and demonstrating improvement impact to stakeholders—including investors who increasingly value operational excellence as a key growth driver.

Finally, this paper concludes with a proposed timeline to concretize the recommendations into an actionable roadmap with clear, specific ownership. This framework and roadmap minimize upfront investment and focus on delivering actionable results in less than eight weeks.

Nota Bene

To ensure clarity and actionable focus, this whitepaper specifically emphasizes operational processes over purely technical ones. Rigorous frameworks such as DORA, SPACE, and DX Core 4 already comprehensively address technical software development metrics. Therefore, to avoid redundancy and maintain strategic clarity, those technical metrics are excluded in their specifics while still providing inspiration through their themes and insights.

Also excluded are any customer-facing AI products or features. A compelling AI integration or “AI embedded” narrative may positively impact valuations, as seen in recent fundraising data, especially for growth-stage B2B startups with proprietary data assets. However this whitepaper deliberately maintains its focus on internal operational effectiveness and measurable return on investment.

Automation is Easy (Transformation is Hard)

Successful operational improvement requires a dual focus: implementing the right tools and driving the behavioral changes necessary to maximize their impact. While AI-powered measurement and automation tools create unprecedented opportunities for efficiency gains, technology deployment without corresponding process and cultural adaptation typically yields minimal ROI, if any. It is critical to recognize that automation, tooling, and AI deliver their greatest value when supported by intentional cultural and behavioral shifts across the organization.

A core principle of this framework is that automation and AI are not “magic wands” – they are amplifiers. The critical path towards meaningful improvements is dependent on behavioral changes by the people and teams using the tools. The ROI of any new tool is unlocked not by its implementation, but by the organization's willingness to evolve its processes and behaviors to maximally leverage it.

There is no change without change.

History is filled with examples of powerful technology failing to deliver value because it was layered onto broken workflows. An AI tool used to streamline a process that is fundamentally flawed is the modern equivalent of printing an email and dictating a reply for a secretary to type. The net result is complexity and negative ROI.

The question is not “what AI can do for us?” but rather “what are we going to do differently with AI tools?”

Maximizing ROI from operational improvements depends on three critical factors: leadership, process redesign, and adoption measurement. Leaders must visibly use new tools and processes, demonstrating their value through their own behavior. They must also clearly communicate expectations, incentivize and reward new behaviors, and actively support teams during the transition. Process redesign should eliminate old workflows entirely rather than layering new tools on top of existing inefficiencies. Finally, tracking adoption metrics—not just output metrics—provides early indicators of implementation success.

Immediate ROI

Time Savings

= Hours saved per cycle × No. employees × Fully-loaded cost per employee × Leakage % In addition to technical acceleration (e.g. code generation), this includes time saved from improvements like shorter meetings, faster information retrieval, and automated reporting. There is a final term for productivity leakage under the assumption that not all time saved by a given employee will be spent on work output. This could be further refined by considering time-value savings of individual roles, varying the fully-loaded cost by the role. This is the most culture sensitive of the ROIs, as it fundamentally assumes that time savings imply accelerated business outcomes. There is a ready pitfall to succumb to Parkinson’s Law here.

Product Delivery

= Increased feature delivery rate × (Customer acquisition + Customer retention) This captures the value in customer acquisition and retention based on a greater velocity of feature delivery. Even if product features do not directly correlate with customer acquisition (i.e. in a white glove business with an extremely small customer pool), accelerating delivery may build future customer confidence in a difficult market. Rapid delivery serves to directionally signal internal capability and is often considered a strong signal for investor valuation. These assumptions should be substantiated or invalidated by internal data.

Quality Improvement

= Reduction in bug fixes × (Internal cost per fix + external cost per fix) The necessity of high quality for success is seen in existing operations: developers spend time testing, dedicated staff automate QA, monitoring tools for ongoing insights, and support staff for customer issues. This captures the value in increased bug prevention, earlier detection, and quicker remediation. Boehm's Curve suggests a 30-100x cost for post-release issues vs a baseline of addressing during specification. Internal costs capture not only technical remediation but also internal issue management, process disruption, staff burnout, and customer support. (Internal pre-release process improvement is already included in time savings.) External cost captures the value of customer retention and NPS (revenue protection) and also the risk-adjusted cost of a catastrophic failure e.g. mass data leakage.

Employee Retention

= Retention improvement rate × turnover rate × No. employees × Replacement Cost Improving employee retention directly boosts organizational ROI due to the significant financial impact of turnover costs. This captures the value saved by factoring in recruitment expenses, onboarding, lost productivity, and knowledge gaps.Even modest improvements in retention yield substantial financial returns because reduced turnover not only mitigates direct replacement costs but also preserves institutional knowledge, enhances productivity, and fosters greater employee engagement and morale. Consequently, initiatives focused on enhancing employee retention are not merely beneficial from a cultural or operational standpoint; they constitute a strategic financial investment with a quantifiable and compelling return.

Long Term ROI

Valuation

As noted in the introduction, recent fundraising suggests that compelling AI enablement in B2B products typically results in a meaningful valuation increase. The "growth at any cost" investor mentality has been supplanted by disciplined scaling expectations, where operational excellence directly impacts valuation multiples. Inefficient operations may reduce valuation significantly compared to mature comparables. For validation, consider discussions with previous investors and with comparable peers.

Decision Making

A decrease in time spent on administrative tasks is expected to create more literal time and figurative head space for decision makers. Furthermore an increase in the signal-to-noise ratio of the information leaders must continuously digest should lead to better informed decisions. While it is unclear that there is a well researched quantification method for the value of improved decision making quality and cadence. For validation, consider a metric around decision confidence and clarity at the time vs retracted decisions.

Employee Satisfaction

It is a truism that top performers want to work with other top performers using cutting edge tools. Implementation of better tooling ought to improve acquisition and retention of top talent. Minimization of toil and burdensome work reduces burnout while creating more time and mental space for both individual and team innovation. Enabling better problem solving and creativity will be a long term advantage. For validation, consider employee surveys and weight the results by per-individual desired retention.

ROI Summary

Operational efficiency improvements deliver ROI across three dimensions:

• Cost Reduction: Directly lowers operational expenses.

• Operational Improvement: Increases throughput, speed, or quality of key processes.

• Strategic Advantage: Enhances agility, flexibility, and responsiveness to market conditions.

All six of the above measures can be mapped into this three dimensional space, as can the more detailed metrics below, ensuring a holistic view of the framework’s value.

While financial models are essential, they may underestimate the non-linear value of new technologies. Historically, when manufacturing plants transitioned from steam power to electricity, initial efforts merely replaced old equipment with electric motors, yielding modest improvements. Over time, however, workflows were entirely redesigned around electrical power, resulting in dramatic productivity gains. Similarly, operational improvements today initially focus narrowly on integrating new tools, but substantial value emerges as processes evolve to fully leverage these new capabilities.

If tooling is an appropriate solution to addressing organizational bottlenecks, there should be careful attention paid to the expected payback period of such tools. Investments in operational automation or AI tools must explicitly consider the opportunity cost – comparing e.g. the value of increasing headcount vs the compounding value (and comparatively low cost) of tooling. Executives might clearly define thresholds (e.g., increased productivity / revenue per employee) to bound whether incremental dollars would be better spent on tooling or hiring an additional staff member.

See the end of this document for specific implementation timeline suggestions, resource allocation proposal, and framework success analysis.

NPV and Payback Period

A three year NPV model for calculating net benefits (ROI) given estimated costs and the benefits formulae above is available to inspect and modify at this link. (Alternative link for cloud issues.)

The results are presented based on taking random draws from the input parameters (PERT distributions) and running the model 100,000 times (Monte Carlo simulation).

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Four KPI Areas (and Twenty Two KPIs)

Please note, there are tools and methods readily available to evaluate each of the metrics below. Specifics and examples have been excluded in an attempt at brevity.

Productivity Metrics

Work unit variance / consistency of delivery

This measures the steadiness of a team’s output across units of work. Typically this is calculated by comparison of actual feature delivery time vs estimated time, although less desirably it can be calculated as per-sprint story point gap. A low variance / high consistency result often indicates high quality planning and processes as well as good communication and cross-team collaboration. Conversely, a high variance indicates planning or communication issues e.g. unanticipated blockers or excessively rapid focus shifts. Ultra high consistency may also be problematic, indicating a lack of ambition or sandbagging estimates. (AI tooling may help with predictive estimates e.g. based on the files or services anticipated to change.)

Reply turnaround time / flow efficiency

Short reply times are indicative of highly collaborative teamwork, fast decision making, and minimal context switching. Short reply times enable individuals to stay in flow, teams to deliver more quickly, and prevent a buildup of information overload. This can be automatically measured across email, Slack, and MRs. Slow reply times indicate bottlenecks, lack of prioritization clarity, and difficulty finding relevant information. Industry benchmarks suggest MR reviews be completed within one business day and reviews taking longer than two business days may be considered a red flag.

Work breakdown cycle times

At the highest level, this measures the time for each stage of feature conceptualization, decision to commit, planning, first commit, and final release. Each of the stages could be further decomposed into sub-stages as appropriate for their unique processes. Shorter times indicate efficient decision making, prioritization, and clarity of communication. Long cycle times introduce lack of context across discovery and decisioning, over-full backlogs, and high friction between decision makers and implementers.

Number of context switches per day

Switching work streams between relatively unrelated contexts is a cognitive burden and potentially costs real time to re-focus. This is particularly true of individual contributors who have expectations than executives. This could be automatically measured by e.g. tracking window switching or activity across internal tools but may be better suited to surveys. Note that this may stand in tension to the reply time metric and the two should be considered in conjunction.

Release frequency

This may apply to individual deployments or to entire features. Frequent releases indicate a high level of automation, increase customer feedback gathering, and lower per-release risk by amortizing changes. It is a higher level metric in that it focuses directly on value delivery. To that end, it is ideal to use impact / size of a release as an adjustment factor. Increased release cadence without sacrificing quality suggests tooling or process improvements that most directly impact the end user.

Quality Metrics

Bug work per cycle + bugs in backlog

The above metric measures any unplanned changes or re-work, this metric simply calculates time spent on bugs whether planned or unplanned plus the accumulation of bugs over time. While this is seemingly simple, it will likely prompt further metrics around e.g. ongoing work quality, prioritization of bugfixes vs feature work, and code complexity.

Automated test coverage vs bugs discovered in production

Automated test coverage is the percentage of code and infrastructure covered by various types of automated testing and QA, i.e. unit tests, integration tests, etc. Bugs in production are all post-release issues presumably not caught by automated testing. Many metrics frameworks only consider coverage percentage alone; this is a mistake since it neglects the near-certainty of unanticipated behavior in complex systems as well as the cost of maintaining high coverage. This metric enables an understanding of the quality / sufficiency of the automated tests in terms of their impact on the end user’s experience. Notably, a very high pre-production bug discovery rate may indicate problematic upstream quality processes which require further interrogation.

CI pipeline failure rate (given sufficient quality tooling)

This measures how frequently a set of code changes does not pass initial automated quality inspection when submitted for team review. High failure rate indicates that there is likely an issue with a team’s expectations and tooling (or, less likely, that the automated CI process is not reliable). This is widely considered to drastically slow delivery and increase tool costs. A very high pass rate may also be problematic and indicate a lack of automated checks or widely available quality tooling.

Production issue rate

The number of defects discovered after a release. This is not only outright bugs, but also freezes, crashes, excessive slowness, and other errors. These defects are summed over a given time period to calculate the issue rate. Ideally issues are given a severity (user impact) multiplying factor to generate a semantically meaningful score.

Mean Time to Resolution (MTTR)

The average time to resolve a production issue or bug once it’s detected. This captures how quickly the team reacts and fixes problems – a key aspect of operational quality and customer service. A shorter MTTR means service is restored or errors corrected quickly, minimizing negative impact. Industry benchmarks for MTTR show elite orgs recovering in <1 hour, the lowest performers take a week or more.

Customer satisfaction

For B2B customers, this is likely an NPS or other survey to gauge quality as seen through the user’s perspective. For customer facing apps, this measure relies primarily on app store ratings augmented by analytics and possible other short surveys. Despite the brevity of this paragraph, this metric is arguably the most important (if vague) quality measure given the direct customer focus.

Team Alignment Metrics

Rework rate (tickets reopened)

The frequency at which a ticket once considered complete later requires additional work. This metric does not stem from bugs alone but from other sources such as downstream conflicts, lack of specification clarity, or some other subsequent rejection. This may be difficult to measure since new tickets are frequently opened in some of these cases. If it becomes apparent that new tickets are frequently opened, it will be necessary to automate detection of “virtually reopened” tickets.

Cross-team technical understanding and coding

Coordination around systems that affect multiple teams, planning for serial work across code bases, and waiting on resolution of blockers are significant drains on productivity. These problems tend to greatly exacerbate delays, create misunderstanding, and engender conflict over priorities. This metric is calculated by counting the rate of code changes by engineers who do not belong to the team that “owns” the code in question. This could be thought of as a measure of how enabled teams are to “play in each other’s sandboxes” or conversely how siloed they are. Many blockers involve relatively trivial changes and much time cost is in the administrative ceremonies of coordinating the work. Compounding this, if the work is non-trivial and can only be performed by one team, an understanding of the nature of the non-trivialities by collaborating teams will greatly enhance communication speed.

Awareness and documentation rates of decisions

This captures the rate at which decisions (whether team minutiae or executive direction) are captured in written form within 6 hours (or some other period) and communicated to the relevant audience. The aim is to capture strategy or other tribal knowledge into a concrete artifact that inhibits misalignment. Prompt knowledge sharing is a leading indicator of alignment that suggests there will be minimal need to rediscussion and rehashing in subsequent conversations. The capturing is somewhat easy to measure while the communication is less so. This is because “communication” is defined in this context as “awareness of the nature of the decision (at least at a high level) and ready access to the relevant document(s).” Given this definition, it should be clear that “communication” is emphatically not “document was posted in a channel somewhere.”

Work changes mid-cycle

Mid-cycle work changes can have many sources: high change failure rates, lack of clarity before starting work, priority changes from leadership, etc. It is likely that each of those (and other) sources will require their own tracking metric. For example, it may be useful to understand how many clarifying questions are asked across Slack, email, and Jira about work that has already been taken up. But to gather an initial baseline, measure unexpected shifts in individual or team focus past the stage where those shifts are considered appropriate.

Participation rates in planning

Measures the distribution of speaking time among the participants in team meetings or other small meetings. Broad participation (high rate) indicates the whole team has bought into plans and understands them, which leads to better alignment day-to-day. If only 1-2 people dominate, others might not fully understand priorities (risking misalignment).

Rate of missed dependencies + unexpected blockers

Counts instances where a cross-team dependency was discovered late and led to a schedule slip or blocker. Each such “miss” indicates a planning/communication gap. This is an indicator of both the quality of planning and the dependency of team delivery on that planning. This formulation is slightly unusual in that most frameworks present such a metric as one that is “ideally zero” without acknowledging the reality or working in complex systems. By contrast, including planning dependency here suggests that while “misses” or “surprises” should be minimized, a failure to reach zero is not necessarily a meaningful failure and might instead indicate that the planning time should itself be capped to a reasonable maximum.

Administrative Efficiency Metrics

Meeting productivity vs costs

This is a composite measure, adjusted for a given meeting type / purpose. AI tools enable novel insights into meeting practices and outputs at scale but AI will not generally address identified issues. At a high level this metric captures the value generated by a meeting vs its intended outcome. This score is computed by a compound metric of factors such as: rate of revisited/repeated discussions, rate of meetings with clear agenda, rate of agenda completion, attendee participation, and ratio of decisions made to meeting time. A brief thumbs up/down survey would give qualitative insights and suggest further details to capture. Finally, the score is used to map out the distribution of the productivity of the meeting vs the cost (based on attendees). (This metric ties directly to decision communication and breakdown cycle times above).

Status update automation percentage

The portion of project/status reporting that is automated (via dashboards, Slack bots, etc) vs done manually (via written reports, speaking in meetings, etc.). For instance, if currently managers spend many hours compiling weekly status reports, implementing automated dashboards can increase this percentage. This is a leading metric because the more routine reporting is automated the more capacity is freed up (which will show in time savings, a lagging result).

Information retrieval time (esp. for critical project info)

Measure how quickly can team members find the information they need e.g. requirements, designs, API specs, etc.? This is a proxy for how well knowledge management and communication practices are working – a shorter time means information is organized and accessible, whereas long search times mean confusion and misalignment.

Tool disintegration score

Count of how many separate tools or manual handoffs a process involves. If a large number of systems or people must be involved to complete a discrete administrative task (e.g. by copy-pasting data between them) that indicates high disintegration (low integration). Reducing that count (through integrations or unified tools) would be a proactive efficiency measure, saving time and reducing mistakes.

Administrative time

Tracks per-week time spent on tasks that individuals identify as work not core to their path to providing customer value. This metric directly converts to capacity gain (time that could be redirected to productive work) as well as team morale. This could be measured via survey or with automated opt-in tracking.

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Value Stream Mapping for Selecting Metrics

Not all metrics carry equal value – the list above is a menu not a mandate. Metrics gathering should take a phased approach; in the first phase identify a subset of no more than five metrics to immediately measure and baseline. Attempting to track too many from the start creates analysis paralysis and dilutes focus from high-impact improvements. Taking a targeted approach will accelerate initial baselining and sets the stage for rapid, phased improvements. This initial phase of both selecting and implementing metrics should be timeboxed to prevent overruns or lack of accountability.

To prioritize these initial metrics effectively, start with a Value Stream Mapping (VSM) approach. VSM is a technique to visually document every step in the process of delivering a product from concept to customer. Creating a diagram of materials and information flow through each stage of development empowers an organization to align on, understand deeply, and redesign processes. The goal is to gain immediate clarity on where improvement efforts will have maximum impact. Metrics selection can then directly target those bottlenecks and processes most responsive to improvement.

Visualizing a shared understanding of processes will enable identification steps that are inefficient in driving bottom-line customer value. Metrics choice can then be targeted to the mapped out processes that are most likely to be bottlenecks or alternatively most amenable to improvement. Since VSM precisely illustrates the concept-to-customer flow, it provides an honest, shared understanding of how work is done and where inefficiencies occur. This in turn directly informs which metrics will offer the highest impact when optimized.

The initial five metrics should be selected based on well defined criteria, such as:

• Direct Financial Impact: Metrics should explicitly target bottlenecked processes or areas with high potential for improvement along the critical path to value delivery

• Measurement Feasibility: Phase 1 metrics that should be quickly measurable, ideally through existing data or retroactively derivable information, in order to accelerate baselining.

• Benchmarking Potential: Emphasize metrics aligned with industry standards to facilitate external comparison and validation.

• Actionability: Chosen metrics must directly inform decisions around automation and tooling, allowing swift and measurable improvements.

Implementing a lightweight, flexible change process is essential; initial metrics should be viewed as provisional. Metrics that fail to generate meaningful, actionable insights should be set aside with alacrity. The goal is to drive actual improvement – the alternative is to succumb to Goodhart’s Law. To that end, focus on rapid iteration toward the metrics that matter most.

Additionally, establish clear success criteria for the metrics-gathering initiative itself, distinct from the metrics tracked. These might include:

• Frequency of Actionable Insights: How often metrics lead directly to operational decisions.

• Ease of Interpretation: Metrics should be clearly understandable to stakeholders.

• Measurable Decision Impact: Metrics should demonstrably clarify or simplify key decisions.

Briefly (expanded on in the timeline below): 1. Map the process with VSM, 2. Agree on starting metrics and their evaluation criteria, 3. continuously execute and re-review.

Next Steps and Timeline

This whitepaper has presented a framework for transforming operational efficiency into a durable, compounding strategic advantage. It introduced an ROI-driven framework designed to accelerate growth, enhance operational margins, and strengthen delivery agility. It emphasized selecting specific metrics and leveraging cultural and process transformation to rapidly unlock measurable improvements.

The AI principal (AIP) will take charge of gathering feedback, implementing plans, and producing a clear path forward in close partnership with the senior team (ST).

Regular status checkpoints will identify and address implementation risks, including potential delays in tool adoption, resistance to cultural change, or resource constraints. Consistent, transparent communication will accompany each phase, clearly setting expectations, celebrating early wins, and reinforcing executive commitment.

To translate this strategy into results, proposed below is an 8-week implementation program led by the AIP. The goal of this first phase is to establish baselines, secure quick wins, and build momentum for continuous improvement.

Success by Week 8 is clearly defined as:

• All selected KPIs fully implemented and baseline data cohesively and clearly visualized.

• Initial process improvements identified, prioritized, and prepared for implementation.

• Clear executive alignment around metrics and an agreed path forward.

Timeline:

• Week 1

  • AIP conducts short interviews with individual contributors and their managers to identify frustrations and perceived bottlenecks. (Pain Points Survey)

  • AIP begins VSM diagram and coordinates following week review with ST.

  • ST requires each engineering team to appoint one or more AI champions.

• Week 2

  • BoW: ST + AIP reviews VSM to identify missed processes and align on pain points.

  • AIP continues interviews.

  • AIP meets with AI champions to discuss Responsibilities and Incentives.

  • EoW: ST decides on AIP’s recommendation for <5 metrics and measurement processes.

• Week 3

  • BoW: AIP + execs review NPV model and agree on how metrics will feed into the model.

  • EoW: AIP + execs review proposed measurement tools and agree on a budget.

• Week 4

  • Measurement initiative is formally announced, clear expectations for participation are communicated to the participants.

  • Launch a ‘Process Bounty’ initiative: publicly recognize and reward employees who suggest actionable, high-impact process improvements targeting identified bottlenecks.

  • AIP + CTO + CFO begin agreements with metrics tooling vendors as necessary.

• Week 5

  • AIP leads the beginning of implementation of measurement processes and tooling

  • EoW: at least one metric is “live” and visible (e.g. a dashboard) to stakeholders

  • Begin implementing the most straightforward process bounty ideas

• Week 6

  • AIP continues implementation, with goal of two additional live metrics by EoW

  • AIP collects team feedback on tool rollout and process bounty followup

  • AIP begins selecting promising improvement projects from the roadmap / wishlist

• Week 7

  • AIP finalizes implementation of all metrics by EoW

  • AIP integrates metrics / VSM into a cohesive “at a glance” tool for stakeholders

• Week 8

  • ST + AIP comprehensive metrics, status, and feedback review

  • ST + AIP select initial improvement projects from AIP’s recommendations.