Executive TL;DR (30 seconds)

CFOs, CPOs, CTOs, and Heads of Ops need a quick way to determine if an AI pilot is delivering real value within a single quarter. The 90-Day AI Pilot Scorecard is a one-page, finance-grade report that tracks a pilot’s key KPI delta, translates it into dollar impact, and tallies total costs. It comes with a structured cadence of weekly, monthly, and quarterly checkpoints to decide whether to prove or pause the initiative. Senior executives own the final go/no-go decision, ensuring alignment with business goals from day one. In short, this scorecard and cadence aim to eliminate guesswork and ensure every pilot either earns its keep or gets shut down quickly.

ROI = (Net Benefit ÷ Total Cost) 
Net Benefit = $$ impact − current run-rate 
Total Cost = build + run + overhead + contingency

Formula: ROI measures pilot return on investment. Net Benefit is the financial gain (e.g. revenue uptick or cost savings) minus the pilot’s ongoing run-rate cost. Total Cost includes one-time build expenses, cumulative run costs (e.g. API calls, infrastructure), plus overhead or contingency allocations.

Why Pilots Fail (and How This Scorecard Fixes It)

Many AI pilots fizzle out because they never bridge the gap between promising metrics and tangible business value. A common culprit is treating the pilot as a tech demo rather than a workflow transformation. In fact, a recent McKinsey global survey found that redesigning workflows to integrate AI has the single biggest impact on capturing EBIT value from generative AI – yet only ~21% of organizations have fundamentally changed any workflows for AI[1]. Another key driver of success is executive oversight: pilots with active C-suite governance (e.g. CEO involvement) correlate with significantly higher bottom-line impact[2]. Unfortunately, many pilots are delegated to mid-level teams without senior sponsorship, and thus lack the clout to drive organization-wide change.

The Scorecard Solution: The 90-day scorecard addresses these failure points by baking in workflow alignment, executive ownership, and disciplined measurement. It forces teams to define a “hero” KPI (the metric that most reflects business value) and measure it against a clear baseline. No vanity metrics or vague “engagement” stats – just the number that matters (e.g. conversion rate, average handle time, etc.). The scorecard’s operating cadence then ensures the pilot isn’t running in a silo. Weekly operations meetings drive any workflow tweaks needed on the ground. Monthly finance reviews translate KPI movement into dollars, so finance leaders validate the impact. And a quarterly executive checkpoint puts the CEO/CFO or relevant exec in the driver’s seat for the scale/stop decision. This direct oversight is exactly what McKinsey identified as a top factor for AI success[3][4]. Moreover, organizations that rigorously track well-defined KPIs for AI see the greatest bottom-line benefits – but fewer than 1 in 5 companies do so today[5]. The scorecard fixes that by making KPI tracking non-negotiable. In essence, it rewires the pilot into the business, ensuring any lack of adoption, value, or ROI becomes apparent (and addressed) long before resources are wasted.

The 90-Day Cadence (Who Meets, What Decisions, What Artifacts)

Illustration: 90-day operating cadence.  Weekly Ops meetings (blue ticks each week) focus on technical and user metrics; Monthly Finance check-ins (green markers at Month 1 and 2) translate KPI changes into dollar terms and track cost vs. budget; the Quarterly Executive review (red marker at Month 3) evaluates board-level ROI and risk to decide whether to scale or stop.

A strict cadence forces cross-functional accountability. Here’s how the 90 days break down:

  • Weekly Ops (Product/Ops/Eng Teams): Every week, the product manager and operational team meet to review tactical indicators and guardrails. They look at things like latency (p95 response time), accuracy or error rates, any AI guardrail triggers/overrides, and user adoption stats. For example, if it’s a support chatbot pilot, the ops team checks how often agents had to take over (override rate) or if response times stayed under the 2s target 95% of the time. They log issues and quick wins in an “ops log” – an artifact that feeds into the monthly review. The decision at Weekly Ops is: do we need to adjust anything right now? This could mean tweaking a prompt, rolling out a hot-fix, updating instructions to staff, etc. The weekly cycle embodies the “Measure and Manage” functions of the NIST AI Risk Management Framework[6] by continually measuring performance and managing issues in real-time.
  • Monthly Finance (Finance Lead + Product Owner): At Day 30 and Day 60, a finance-focused review ensures the pilot’s results are translated into business terms. The team takes the KPI delta observed (e.g. +2 percentage points in conversion rate, or -20% in handling time) and computes the $$ impact. They’ll use unit economics – e.g. 2% higher conversion on 5,000 leads = 100 extra sales; at $500 profit each = $50,000/month added revenue. They’ll also tally the run-rate cost so far: how much have we spent on model API calls, cloud infra, etc., versus the budgeted plan? Any variance is analyzed (e.g. is cost per API call higher than assumed? Is usage volume above plan?). A simple sensitivity analysis might be included to project what full-scale costs or savings look like. The output is a one-page financial update (part of the scorecard) that the CFO or FP&A partner can audit. This monthly cadence enforces financial discipline (“Measure” in NIST terms, linking KPI to impact) and early cost control, rather than finding out after 6 months that the pilot is burning cash. If the numbers aren’t penciling out by month 2, the finance lead will flag it.
  • Quarterly Exec (C-suite Sponsor & Board-level Update): Around Day 90, it’s decision time. The product leader and finance lead take the updated scorecard to the executive sponsor (e.g. the CFO or COO, and possibly the CEO/Board in a quarterly business review). In this executive meeting, the question is simple: Go or No-Go? The scorecard now shows the ROI (or lack thereof) in a board-ready format. Alongside ROI, the team presents a risk register – any significant risks or incidents encountered (e.g. a compliance flag, an outage, a user backlash) and how they were managed. This is where the “Govern” function of NIST AI RMF comes in[7]: senior leadership evaluates if the pilot adhered to governance policies and if risks are tolerable for scale-up. They also ensure proper documentation exists (fulfilling NIST’s call for accountability and transparency). The outcome is a decision: either scale the solution (with necessary funding and perhaps integration into core operations), extend/iterate the pilot (if results are promising but need more proof or tweaks), or shut it down (if ROI or risks fall short). By having this clear quarterly checkpoint, the company avoids the limbo of endless “science projects.” As one McKinsey insight put it, organizations are “rewiring” with bold leadership moves – aligning AI projects to business value and oversight to truly capture value[3].

Throughout this cadence, notice that each step produces an artifact: weekly ops logs, monthly KPI-to-$ reports, and the 90-day scorecard for the execs. These map neatly to NIST’s core functions: Map the context and objectives, Measure the results, Manage the risks and performance, and Govern at the leadership level[8]. Nothing is left unmanaged for long.

The Scorecard (Fields & Formulas) — Download Included

Example layout of a one-page Pilot Scorecard. It includes a header with pilot metadata, an Impact section calculating KPI changes and $$ benefit, a Cost section tallying all expenses (with savings from batch or caching optimizations), and a Risk/Quality section tracking latency, failure rates, and compliance checks. (Download the editable sheet for this scorecard template.)

The 90-Day Pilot Scorecard is a single-slide or single-sheet report that anyone from an engineer up to the CEO can quickly grasp. It captures four main areas:

  • Header: This top banner lists the basics – Use Case name, the Hero KPI being moved (and the target set for it), the Owner (who is responsible for the pilot’s outcome), and the Phase (e.g. Proof-of-Value, Pilot, or Scale). For example: “Use Case: Customer Support AI Assistant; KPI: Avg Handle Time (target −20%); Owner: Jane Doe, Head of CX; Phase: Pilot (Day 30).” This context makes it clear what we’re trying to achieve and who’s driving it.
  • Impact Section: Here we quantify the KPI delta vs. baseline and convert it into a business impact ($$). It typically includes the baseline value (e.g. AHT was 10 minutes), the current pilot value (e.g. now 8 minutes), and the percentage improvement (-20%). We then scale that by the scope or exposure: e.g. “Pilot covers 15% of support tickets” or “applied to 2 out of 5 sales regions”. This tells us how much of the business is seeing that improvement. Using those, we calculate Net Benefit – for instance, 2 minutes saved per ticket * 10,000 tickets/month * $0.50 labor cost per minute = $10,000 saved per month. Or in a sales use case, +2% conversion * 5,000 leads * $500 profit per conversion = $50,000 added revenue. The scorecard might show a simple formula or a line item: “Monthly impact: +$50K” and then extrapolate “Year-1 impact (at full scale): ~$600K”. This section essentially answers: if the KPI change holds, how does it hit the P&L? By making the math explicit, it gives finance the ability to audit assumptions and ensures everyone talks in the language of dollars, not just model accuracy or clicks.
  • Cost Section: All costs – build and run – are tallied here, adjusted for any provider discounts or optimizations. We break costs into categories: LLM/API costs, Infrastructure/Platform, and People/Overhead. For LLM/API, we incorporate cost-control levers from day one. For example, if using OpenAI, the scorecard assumes use of the Batch API for any asynchronous processing, which offers 50% cost savings on token fees[9]. We explicitly note that in the unit cost (e.g. “GPT-4 via Batch @ $0.03/1k tokens instead of $0.06”). If using Anthropic Claude, we plan for prompt caching – paying a 25% premium once to cache a large prompt, then only 10% of normal price on subsequent uses[10]. These adjustments ensure the run-rate cost isn’t overestimated (and also signal to finance that we’re leveraging available discounts). Infrastructure might include cloud compute or vector database costs if applicable. People cost could be part-time analysts or an annotator verifying AI outputs, etc., as well as an overhead/contingency percentage (e.g. 10-15% buffer for unexpected costs or compliance work). The scorecard shows Total Cost to date and compares it to plan. It also projects out the 90-day burn and the Annualized Run-Rate if scaled. This transparency prevents unpleasant surprises – if the model is calling more tokens than expected, you’ll see the cost overrun by the first monthly review, not after a year. Ultimately, we compute ROI = Net Benefit ÷ Total Cost, using the net benefit from Impact and the cost here. A healthy pilot might show an ROI >1 (meaning benefits exceed costs) even at pilot scale, or at least a credible path to >1 at scale-up.
  • Risk/Quality Section: Finally, the scorecard reports key risk and quality metrics to ensure that, in the rush for ROI, we didn’t compromise on governance or user experience. Here we include technical quality metrics like latency (p95) – was the response time consistently low for users? – and failure/override rates – e.g. what percentage of AI outputs had to be discarded or intervened by humans due to errors or policy violations. We also list safety/compliance checks: for instance, “No PII leaks detected; HR approved for bias; passed XYZ compliance checklist.” This is essentially a mini risk register focusing on the pilot. If any incidents occurred (model produced inappropriate content, or a privacy issue), it’s noted along with mitigation. We map these to the NIST AI RMF categories where relevant – e.g. if the pilot involves decisions about people, we might note fairness checks (bias testing) as part of “Map” and “Measure” functions[7]. By having this section, the scorecard reassures executives that the pilot isn’t a black box running wild – it’s under control, with proper guardrails and documentation. It answers the question: are we operating this AI responsibly and within risk tolerance? This is crucial given emerging regulations and internal AI governance policies.

All together, these fields provide a holistic view: what we did, what changed, what it’s worth, what it cost, and whether it was done safely. High Peak Software offers a downloadable 90-Day Pilot Scorecard (Excel/Google Sheet) pre-filled with these sections and formulas – you can plug in your own metrics and use it immediately to track your pilot. (See the end of this article for the download link.) By standardizing the format, your organization can review any AI pilot with the same lens, making it easier to compare and decide where to double-down.

Measurement 101: Baseline, Counterfactual, Attribution

Accurately measuring the KPI impact of an AI pilot is half the battle. Without proper rigor, you risk declaring victory (or failure) based on noise. Here’s how to nail the measurement:

Establish a Solid Baseline (4–8 weeks): Before flipping the AI on, observe the status quo. For 1–2 cycles (typically 1–2 months) prior to pilot launch, record the baseline performance of your hero KPI and related metrics. For instance, if our goal is to cut average handle time (AHT) in support, gather the last 8 weeks of AHT data, perhaps segmented by team or channel. This baseline gives you a yardstick and helps account for seasonality or trends. It’s important to lock down any baseline window and note if any anomalies happened (e.g. a holiday rush). The baseline should ideally be “frozen” – meaning no major changes in process or tools during that period – so you have a clean before/after comparison.

Pick a Counterfactual Method: Counterfactual analysis means figuring out what would have happened without the AI, so you isolate the AI’s true effect. The gold standard is an A/B test (randomized controlled trial). For example, route half of incoming chats to the AI-assisted workflow and half to the old workflow, then compare outcomes. This controls for external factors and is statistically robust[11]. If A/B isn’t feasible (sometimes operationally or ethically you can’t randomly split), consider a phased rollout: e.g. first month pilot on Region A, second month extend to Region B while observing Region A vs B. This mimics an A/B over time (though watch out for time-based differences). Another approach is Difference-in-Differences: have a comparable control group (maybe another business unit not using the AI) and measure the differential change over time between pilot vs control. Lastly, an Interrupted Time Series can be used if you have to do a universal rollout – you look for a “break” in the metric trend coinciding with the pilot start, adjusting for prior trends. Each method has pros/cons: A/B is most direct but can require extra effort and careful randomization; phased rollouts and diff-in-diff require a good control group; time series needs enough data points and assumes no other major disruptions occurred.

Control Confounders: No matter the method, be vigilant about other changes that could confound results. For example, if Marketing ran a big promotion during your pilot, sales conversions might spike due to that, not the AI assistant. Or if you did a hiring freeze, support response times might increase unrelated to the AI. Ideally, institute a change freeze for the pilot’s domain – pause other major initiatives in that area for 90 days. If that’s not possible, at least document concurrent events and adjust your analysis (finance can help normalize metrics or exclude certain weeks if a known external factor hit). It’s also wise to monitor secondary metrics: e.g. for a support pilot focusing on handle time, also watch customer satisfaction scores – if AHT improved at the cost of worse CSAT, that’s important to know (and fix).

Attribution vs. Correlation: When presenting results, be clear about attribution. If you did an A/B test, you can attribute differences to the AI with high confidence (given randomization). With observational methods, you might say “KPI X improved 15% during the pilot, while control group saw 5%, so we attribute ~10% uplift to the AI.” Be cautious about over-claiming – use statistical significance where possible. The scorecard can include a note like “Attribution method: A/B test, 95% confidence” or “Results directional due to observational data”. This level of candor actually boosts credibility with skeptical CFOs.

In summary, design the pilot like an experiment. Define your baseline, choose a control or comparison approach, and lock down the environment to isolate the AI’s impact. This rigor will make your KPI delta credible and board-ready.

Turn KPI Delta into Dollars (Worked Examples)

Let’s illustrate how a change in KPI becomes a financial ROI using two example pilots:

1. Support Copilot (Customer Service AI): Suppose the AI assists support agents, and the target KPI is Average Handle Time (AHT). Baseline AHT was 10 minutes per ticket. After deploying the AI suggestions, AHT in the pilot group is 8 minutes – a 2-minute reduction, or 20% faster. The support team handles 10,000 tickets per month. That’s a total of 20,000 minutes saved monthly. Now, convert time to money: assume fully-loaded cost per support agent is about $30/hour, roughly $0.50 per minute. Saving 20k minutes saves $10k in support labor cost per month (20,000 * $0.50). Annually, that’s $120,000. Meanwhile, the pilot costs include the AI tool subscription and integration, say $15k per month (LLM API + infrastructure + some one-time setup amortized). Over 3 months, pilot cost ~$45k. Net benefit in the 3-month pilot window: ~$30k (savings minus cost). But Year-1 ROI is even more telling: if scaled to all tickets, you’d save $120k/year vs. an annual run-rate cost of perhaps $60k – that’s a 2x ROI (200% return) in the first year, and likely higher in subsequent years (once one-time costs are sunk, the ongoing ROI improves). Payback period is quick: you recoup the investment in ~6 months of full deployment.

KPI-to-$$ conversion for a Support Copilot example. Reducing AHT from 10 to 8 minutes (–20%) across 10k tickets/month saves ~20k minutes. At $0.50/min labor cost, that’s ~$10k saved per month, or $120k/year. With pilot costs (~$60k for 3 months), the Year-1 ROI would be ~2x and payback ~6 months.

Finance can follow these calculations in the scorecard’s Impact and Cost sections. They might even stress test assumptions: “What if volume is only 8k tickets/month, or if we need to hire staff to manage the AI?” That’s why clarity in the spreadsheet matters. The example above is deliberately straightforward and conservative (not accounting for potential quality improvements or customer satisfaction gains, which could be additional upside).

2. Sales Assist (AI for Sales Conversion): Now consider a pilot where an AI tool suggests next-best actions to sales reps, aiming to increase lead conversion. Baseline conversion rate (leads to deals) is 10%. In the pilot group using the AI, conversion is 12%. That 2 percentage-point lift is a 20% relative increase in conversion (from 10 to 12%). If the team handles 5,000 leads per month, baseline would yield 500 deals; pilot yields 600 deals – 100 extra deals per month thanks to the AI. If each deal on average gives $1,000 in revenue and, say, $500 in gross profit (after costs of goods/service), that’s $50,000 additional profit per month. Over a year, if sustained, that’s ~$600,000 in extra profit. Meanwhile, the cost of the AI tool might be, for example, $10k/month subscription + $5k one-time training = $125k/year. Even if we factor in some sales commissions on the extra deals, the ROI is clearly positive – roughly 4x return on profit ($600k gain on $125k cost). However, we must check for cannibalization: Are these 100 extra deals truly incremental, or did the AI just help close deals sooner that might have closed later anyway? To assess that, the sales VP might look at pipeline metrics or compare regions with vs. without AI. Let’s assume they determine 80 of the 100 are truly net-new (20 were likely to close eventually). Even at 80 incremental deals/month, that’s $40k/month profit increase, still ~$480k/year – very strong. The pilot scorecard would document this analysis, including a note like “Adjusted for potential cannibalization (-20% factor)”. The payback on this pilot might be just 2-3 months of full deployment.

In both cases, we translate the KPI movement into the language of Finance: revenue, costs, and ROI. The scorecard also provides a space to note qualitative factors or footnotes on the calc (e.g. “assuming constant ticket volume”, “assuming avg deal size \$1k at 50% margin”). This helps pre-empt questions. The goal is that by the quarterly exec review, the CFO can say, “I see how this AI impacted our dollars. The support pilot saves $X per year if rolled out, and the sales pilot could drive $Y in new revenue. Given the costs and risks, I’m convinced / not convinced.” That is board-ready ROI.

Lastly, Finance may compute payback period (how long to recover the investment) and potential NPV if it’s a significant investment. For a small 90-day pilot, payback is often not the focus (since pilots are relatively cheap), but for scaling, it is. In our support example, a 6-month payback is excellent (anything under 12-18 months for tech investments is typically favorable). These worked examples can be used as templates in the downloadable scorecard – you can plug in your own baseline, improvement, and unit economics to get instant ROI calculations.

Cost Controls that Don’t Hurt

One reason AI pilots can stumble is run-rate shock – the solution works, but it’s too expensive to run at scale (e.g. “each answer costs $0.05 and we have millions of queries!”). To combat this, savvy teams employ cost control levers from the start, so the pilot remains cost-effective without degrading user experience. Here are key levers:

  • OpenAI Batch API (50% Discount): If your pilot uses OpenAI models (GPT-4, etc.) for tasks that don’t need instant responses (think batch processing, data analysis, nightly report generation), use the Batch endpoint. OpenAI offers ~50% cost savings on inputs and outputs when you run requests asynchronously over a 24-hour window[9]. For example, instead of paying $0.03 per 1K tokens, you’d pay $0.015 per 1K. The trade-off is latency: batch jobs might return in minutes or hours instead of seconds. For any non-user-facing inference, this is a huge cost win. In the pilot plan, identify which calls can be batched (perhaps nightly summary emails, periodic re-training, etc.). By design, the scorecard’s Cost section uses Batch-discounted prices for those portions, demonstrating proactive cost efficiency. This lever can often cut total pilot API costs nearly in half without impacting the end-user experience at all (since users aren’t waiting on those jobs). It’s essentially taking advantage of volume pricing in exchange for patience on certain tasks.
  • Anthropic Claude Prompt Caching: For pilots using Anthropic’s Claude models (either directly or via providers like AWS Bedrock or GCP Vertex), prompt caching can drastically reduce token costs for repeated context. The idea is simple: if you have a large block of prompt (e.g. a knowledge base, a large set of instructions/examples) that you need to send with every request, you can cache it once. The initial cache write costs +25% of the normal input rate (a one-time premium), but thereafter, reading that cached prompt costs only 10% of the normal input token price[10] – a 90% discount on those tokens. This is game-changing for long prompts: e.g. instead of paying for 10,000 tokens every time, you pay 12,500 tokens once, then 1,000 tokens each subsequent call. In practice, this can reduce both cost and latency (since the model doesn’t need to process the full context every time). Use prompt caching for static or slowly-changing context: for instance, the support copilot might cache the entire help center manual upfront, or a sales AI might cache product info and examples. The user’s query and recent conversation is still sent normally. Our scorecard would list something like “Prompt caching enabled: 90% token cost reduction on ~X tokens per call.” The result is a smoother UX (faster responses) and a lower bill. Importantly, prompt caching requires some effort (you have to implement the caching calls and handle cache IDs), so plan it in the pilot if you expect heavy prompt reuse.
  • Model Right-Sizing and Tiering: Not every request needs the most powerful (and expensive) model. A cost-savvy pilot might use a two-tier model approach: e.g. use a cheaper model for simple tasks and only call the top-tier model for complex cases or final validation. For example, an AI agent could attempt an answer with a $0.002/1K token model (like a distilled model) and only if confidence is low or answer not found, escalate to GPT-4 at $0.03/1K. This kind of tiering can save a lot. Another angle is context window management – don’t send unnecessarily long histories or data if not needed, as token costs scale with prompt length. If you can summarize or truncate context with minimal performance hit, do it. These strategies require careful design to avoid hurting quality, so A/B test them within the pilot if possible (e.g. measure user satisfaction when using the cheaper model vs expensive model on some traffic).
  • Adaptive Throttling of High-Cost Features: If your pilot has optional features that are very costly (e.g. an AI vision module that analyzes images), consider throttling their frequency or making them opt-in for users. For instance, if 5% of users use a feature that costs 5x more per request, you might limit it to power-users or specific times. The key is you’re controlling cost exposure while monitoring if it impacts usage or outcomes. If it doesn’t hurt UX much, you found a cost lever; if it does, you know that feature drives value and can justify the spend or find another optimization.

The beauty of these cost controls is that they are mostly invisible to the end-user. Done right, a user might simply notice the AI is fast and available – not realizing you queued some tasks to a batch or cached half the prompt. By incorporating these levers from day one (and showing them on the scorecard), you also signal to leadership that scaling this pilot won’t break the bank. It moves the conversation from “Can we afford to scale this AI?” to “We’ve engineered it to scale efficiently – here’s how.”

One real-world example: a fintech company piloting an AI document analyzer initially saw high costs using GPT-4 synchronously for every doc. By switching to Batch processing overnight, they saved ~55% on API charges with no user impact (users submitted docs by end of day, results were ready next morning). In another case, a gaming company using Claude for NPC dialogue cached the game lore context – latency dropped by 80% and costs by 90% for those prompts[12][10]. These moves can be the difference between a pilot that gets killed for cost and one that sails through CFO approval.

Governance & Compliance (Pilot-level)

Even in a 90-day sprint, governance and risk management cannot be an afterthought – especially with regulators circling and enterprise AI standards emerging. The scorecard approach builds governance into the pilot so that scaling it doesn’t trigger compliance nightmares later.

First, align with the NIST AI Risk Management Framework (AI RMF) as a guiding structure. NIST’s framework provides four functions – Govern, Map, Measure, Manage – to systematically handle AI risks[8]. For a pilot, this means:

  • Govern: Establish clear roles and accountability for the pilot’s AI system from the outset. Who is the “AI risk owner” or responsible official? Often this is the project sponsor (like the Head of Ops or CTO) in partnership with a risk/compliance officer. Set up a lightweight governance document for the pilot – e.g. an AI pilot charter that outlines objectives, acceptable use, and risk thresholds. Include an incident response plan: if the AI produces a major error or policy violation, who gets alerted and what steps are taken (pause the pilot? notify legal? etc.). Governance also means ensuring ethical use – check the pilot against company AI ethics guidelines or relevant laws. During the executive quarterly review, one agenda item is to review this pilot risk register and confirm all governance requirements were met. This satisfies the “tone at the top” element that regulators and NIST emphasize (leadership overseeing AI use)[2].
  • Map: In NIST terms, mapping involves understanding the context, scope, and stakeholders of the AI system[7]. For the pilot, do a brief risk mapping exercise at the start. Identify potential risks: e.g. does the AI make customer-facing decisions? Could it impact fairness or privacy? Map where data comes from and where outputs go. For example, if piloting an HR resume screening AI, map out that it affects hiring decisions (high risk for bias) and thus you might label it a high-risk use case requiring extra checks. If it’s a low-risk internal tool, mapping confirms that too. Also map what regulations or policies apply – e.g. GDPR if EU personal data is involved, or sector-specific rules. This mapping informs what you need to measure.
  • Measure: Define what risk metrics or audits you will perform throughout the pilot[6]. This ties into the Risk/Quality section of the scorecard. For instance, you may measure false positive/negative rates, bias (e.g. outcomes by demographic if applicable), robustness (did the model fail when inputs were weird?), and so on. If the NIST framework or your compliance team has specific checklists, incorporate those. Many pilots use a pre-launch checklist (did we do a privacy impact assessment? did legal approve the data usage? is our model card/documentation ready?) and an ongoing checklist (monitor for drift, security tests, etc.). The NIST AI RMF playbook provides suggestions for such actions[13][14]. For example, NIST recommends documenting training data sources and known limitations as part of measurement – a pilot should at least have a one-page model fact sheet. Measure also means tracking incidents: keep a simple log of any complaints or exceptions (e.g. “On Sep 10, AI suggested an off-policy action, caught by human QA”).
  • Manage: This is about actually responding and mitigating risks as they arise[6]. In a pilot, “manage” could involve setting guardrails (like content filters on an AI chatbot), enforcing a human-in-the-loop for certain decisions, or rate-limiting the system if it starts to behave oddly. Since pilots are contained, management is often easier (you can shut it off quickly if something goes wrong). But demonstrate that you have those controls. For instance, include in your risk section: “Manage function: Enabled real-time monitoring, manual override available 24/7 by on-call engineer, rollback plan in place.” If the AI is providing customer outputs, perhaps have an easy way for customers or staff to report issues. Manage also involves improvements: using the weekly ops meetings to patch any process gaps (if, say, support agents found the AI sometimes gives outdated info, you manage that by updating the knowledge base or model).

Now, let’s talk compliance – especially the looming EU AI Act which is set to start biting even during pilot phases. As of this writing, the EU AI Act has been passed and has staggered effective dates. Notably, August 2, 2025 is a key milestone: certain obligations for General Purpose AI (GPAI) models and other governance provisions will apply from that date[15]. This means if your pilot (or eventual product) relies on a GPAI like GPT-4, there will be requirements on the provider (and potentially you as deployer) around transparency, documentation, and risk management. For example, providers may need to supply documentation on training data, and users might need to disclose AI-generated content in some cases. While the heavy burden falls on AI model providers, organizations running AI systems in the EU will need to ensure compliance (especially by full rollout in 2026 when the Act fully applies).

What to do in a pilot? Proactively incorporate compliance checks. If operating in Europe or with EU customer data, treat the pilot as if the AI Act already applies. Keep records of what the AI is doing, ensure you can explain the model’s decisions at a high level (transparency), and include an “AI disclaimer” to users if appropriate. For instance, if it’s customer-facing, consider a note “This response was assisted by AI” (some jurisdictions might require that). Also, engage your legal or compliance team early – a 30-min review with them during pilot design can surface any red flags (e.g. if the pilot uses biometric data or something classified as high-risk under the Act, you’ll need strict controls). By designing the pilot in alignment with upcoming rules, you avoid rework later. And executives will appreciate that you’re not just chasing ROI blindly – you’re building something that can actually be deployed legally and ethically.

A quick governance win: maintain a brief Pilot Documentation Pack – maybe just a folder with the pilot proposal, the scorecard updates, the risk register, and any compliance approvals. That way, if auditors or the AI governance committee ask “what happened in that pilot?”, you have an evidence trail. It also makes it easier to onboard the production team if it scales – they inherit all the context.

To summarize, governance from day one is like insurance. It might add a small overhead (a few extra meetings, some documentation), but it prevents costly disasters or delays. It instills confidence at the board level that scaling this AI won’t result in headline risks. As Gartner and others often note, trust in AI is as important as performance. With NIST’s framework as scaffolding and awareness of laws like the EU AI Act[15], you ensure your pilot is not only effective, but also responsible and ready for the real world.

Common Pitfalls (and How to Avoid Them)

Even with the best planning, AI pilots can go astray. Here are common pitfalls and how our 90-day scorecard approach helps avoid them:

  • No Clear Counterfactual: Teams sometimes implement an AI and see a KPI move, but can’t definitively say the AI caused it. This is the “without a control, we’re just guessing” trap. Avoidance: As discussed in Measurement 101, set up a baseline or control group. The scorecard’s KPI section should always compare against something (baseline or control). If you find yourself only showing a raw number (“conversion is 15% now”), you likely lack context. Add that A/B or pre/post comparison so you know it’s real.
  • Chasing Vanity Metrics: It’s easy to get excited about secondary metrics – “users asked 1,000 questions!” or “the AI produced 500 pages of content!” – that don’t equate to business value. These vanity metrics can make a pilot look good superficially while the core KPI languishes. Avoidance: The scorecard forces identification of a Hero KPI tied to business value (and ideally tracked to $). Everything revolves around moving that needle. If your pilot update is talking about click counts or time spent in app, ask “does that translate to our hero KPI’s improvement?” If not, refocus or pick a better KPI. The McKinsey finding that tracking well-defined KPIs leads to more impact is a reminder: fewer than 20% of companies do this[16], so be among those who do.
  • Run-Rate Shock: This pitfall occurs when a pilot looks successful, but nobody checked the scaling economics. Suddenly you realize serving each user costs $1 in API calls, and at a million users that’s unsustainable. Avoidance: Bake cost awareness into the pilot. Our Monthly Finance reviews catch any cost overrun by Day 30 or 60, not Day 300. Also, using cost controls (Batch, caching, etc.) from the start means you’re testing the model in a cost-optimized way. If you still find costs are too high, you can tweak the approach or choose a different model before scaling. No executive likes a surprise budget request that wasn’t forecast – the scorecard’s cost transparency prevents that.
  • Integration Drag: Sometimes the AI model itself is fine, but integrating it into real workflows or IT systems is a bear. The pilot might work in a sandbox but falters when tying into CRM, ERP, or live customer channels – causing delays or poor UX. Avoidance: Include integration steps in the pilot plan and timeline. For example, if the AI needs to pull data from a database, do that in the pilot even if it’s manual or partial. Weekly Ops meetings should surface integration issues early (“We’re seeing latency because we haven’t optimized the DB calls”). If integration is proving too complex, that’s a finding – you may pause the pilot until that’s resolved, rather than declare victory on a disconnected prototype. Also, consider involving an integration engineer from day one (High Peak’s AI Integration specialists often sit in pilot teams to ensure smooth tech hookup).
  • Ignoring Compliance Until Late: This is the classic “move fast and break things” risk – the pilot works great, but Legal or Compliance steps in at month 4 saying “you can’t deploy this.” Perhaps data was used without proper consent, or the AI outputs need disclaimer per EU AI Act, etc. Avoidance: Governance from day one (as detailed earlier). Run your plan by a compliance officer early, use NIST RMF to structure it, and keep a risk log. It’s much easier to address compliance in the pilot (maybe anonymize data, or disable a risky feature) than to retrofit a live system under a regulatory deadline. The scorecard’s Risk section every month ensures these considerations stay visible. No one can say “oh, we forgot about data privacy” if it’s a line item reviewed at each update.
  • Lack of Change Management (aka People Problems): An often underappreciated pitfall: the pilot delivers results, but the people who need to adopt it resist change. Perhaps support agents don’t trust the AI suggestions, or sales reps feel the AI is imposed on them. Adoption lags, and the KPI gains don’t materialize fully because of human pushback. Avoidance: Treat the pilot as a socio-technical change, not just a tech demo. This means involving end-users early (get a champion on the team), training them, and communicating the “what’s in it for me.” In our weekly ops, don’t just review numbers – gather qualitative feedback from users. For example, a support agent might say “The AI’s suggestions are good, but I have no time to read them during a call” – that’s a design issue to solve, not the agent’s fault. Solve it in the pilot (maybe change UI or process). Having a senior sponsor (like COO) also helps set the tone that this is a strategic priority, not optional. In the scorecard, you could even include an “adoption metric” (e.g. % of agents using the AI at least once per ticket) to make sure usage is on track.
  • Overlooking Downstream Effects: Pilots often focus narrowly on one metric. But improvements in one area can cause issues in another if not monitored. E.g., your AI scheduling assistant schedules 30% more meetings (great!), but now sales reps are complaining their calendars are too full and quality of meetings dropped. Or the support AI cuts handle time, but first-contact resolution drops because agents rush. Avoidance: Identify a few guardrail metrics or secondary KPIs to watch. In the scorecard, this can be a footnote in Impact or Risk (“Monitoring FCR and CSAT alongside AHT to ensure no negative impact”). If a secondary metric flags, address it in weekly ops. Sometimes you might decide a slightly smaller improvement on the main KPI is okay if it avoids a hit on another important metric.

The common theme in avoiding these pitfalls is visibility and agility. The 90-day cadence with a scorecard creates visibility: everyone sees the true status – KPI, cost, risk – so issues can’t hide. And the weekly/monthly cycle creates agility: you have built-in points to course-correct. Many traditional projects only do a post-mortem at the end (“oh, we should have done X”). In this pilot framework, you’re doing interim post-mortems constantly. That means by the time you reach day 90, you’ve already fixed the small stuff and either achieved success or realized it’s not viable – both outcomes are wins compared to a drawn-out failure.

If you’re embarking on an AI pilot and want a second pair of eyes,

We’re here to help. Book a 30-min pilot review with our experts to sanity-check your KPIs, governance plan, and cost assumptions. In a quick session, we’ll help identify any gaps and share best practices tailored to your use case (whether it’s AI product development or process integration).

Take the next step: check out our AI Integration services (we often start there to get a handle on your AI usage patterns), or explore our offerings in AI Strategy Consulting, AI Design, and AI Marketing to see how we can partner in your AI journey!