The 30-Second Executive Summary

Invest with eyes wide open. AI can deliver tangible business impact, but only when paired with workflow redesign, strong governance, and skilled talent – not by deploying algorithms in a vacuum. In fact, a McKinsey global survey finds companies are beginning to see bottom-line results by redesigning processes and putting senior leaders in charge of AI governance[1]. More than three-quarters of organizations now use AI in at least one function, yet the biggest gains come when CEOs and boards oversee AI efforts and workflows are fundamentally reworked[1][2].

Think portfolio, not one-off. Treat your AI spend as a portfolio spanning build, buy, and blend options, evaluated continually. This means initially leveraging existing AI platforms or APIs for speed, then considering custom builds for strategic differentiation. Apply risk controls from day one – adopt frameworks like NIST’s AI Risk Management Framework (AI RMF) to structure governance. The NIST AI RMF 1.0 defines four core functions for managing AI risks – Govern, Map, Measure, Manage[3] – which provide a useful blueprint to ensure your AI investment has oversight, context understanding, performance metrics, and ongoing risk mitigation. In short, balance ambition with accountability: secure quick wins with off-the-shelf AI where it makes sense, but put the guardrails and org structure in place early (e.g. an AI steering committee, risk assessment processes) to avoid costly surprises later.

Define the Business Case Before the Model

Before you even discuss models or tech stacks, pin down the business case. Identify the single highest-value use case for AI in your organization – the one thing that, if improved by AI, moves the needle most. For example, it could be reducing average handling time (AHT) in customer service by 20%, or increasing conversion rates in marketing by 15%. Define one primary KPI for this initiative and a target delta (e.g. “+15% web conversion in 90 days”). This sharp focus guards against AI projects wandering off into “science fair” territory with no clear ROI.

Demand a 90-day Proof of Value (PoV). Require a time-boxed pilot (around 3 months) that actually tests the AI in a real workflow and measures impact on your chosen KPI. Set a concrete success threshold (for instance, a 10–20% improvement in that KPI over baseline). If the PoV doesn’t move the needle, you have a pre-planned off-ramp or pivot. This approach creates urgency and accountability – it forces the team to deliver business value quickly or fail fast and cheaply.

Secure executive sponsorship and data upfront. AI initiatives cut across silos, so make sure you’ve aligned the necessary stakeholders before you fund anything. That means an executive sponsor who will champion the project and remove roadblocks, plus the owners of the data and processes you’ll need to integrate. McKinsey observes that the real value from AI comes when organizations redesign workflows and have senior leaders actively engaged in adoption[1]. So, ensure process owners and a C-level champion are part of planning the AI-driven changes – not just the data scientists or IT folks. Early executive alignment also helps set the tone from the top that AI is a strategic priority, which can drive user adoption later.

Risk, Governance, and Compliance: What You Own

Funding an AI project means you’re also funding its risk exposure. As an executive, you will own the risks (ethical, legal, operational) that come with AI – you can’t just delegate this to IT. Implement a governance framework from day one. A practical choice is the NIST AI Risk Management Framework (AI RMF), which provides an operating model to “Govern, Map, Measure, and Manage” AI risks throughout the AI lifecycle[3]. In practice, this means establishing policies and processes for AI (governance), understanding the context and scope of risks (map), setting up metrics and tests for those risks (measure), and having mechanisms to mitigate and respond to issues (manage). By adopting NIST’s framework, you signal to your teams (and regulators) that you’re building AI responsibly. For example, you would document model and data lineage, implement human oversight for high-stakes decisions, and create an incident response playbook in case the AI outputs go awry. (If an AI system ever produces a public-facing mistake or a biased outcome, you’ll be glad you had an incident response plan reviewed by Legal and PR ahead of time.)

Stay ahead of compliance curves. Map out how forthcoming regulations like the EU AI Act could impact your project’s timeline and design. The EU AI Act is slated to roll out in phases – general-purpose AI (GPAI) providers will face new obligations starting August 2, 2025[4], and additional requirements (e.g. for high-risk systems, national registries, etc.) kick in by 2026. Even if you’re not in Europe, these rules can affect any AI system deployed or used in the EU, so global companies must pay attention. Ensure someone on your team (perhaps your compliance officer or general counsel) is tasked with tracking these developments. The law is evolving: for instance, the European Commission issued draft guidelines in July 2025 to clarify how GPAI provisions will work[5]. This ongoing guidance means your compliance checklist for AI can’t be one-and-done – it needs version updates. If your use case might be deemed “high-risk” under the EU Act (e.g. AI in hiring or lending decisions), factor in the extra time and controls needed to meet those standards (like conformity assessments or transparency requirements). In regulated industries, also consider sector-specific AI guidelines (the FDA for medical AI, FTC for consumer protection, etc.). The key is no surprises: proactively identify what laws or standards your AI system will need to comply with in its intended use and plan accordingly.

Consider certifiable AI governance. For organizational readiness, you may look at emerging standards like ISO/IEC 42001:2023 – essentially an “AI management system” certification for companies. ISO 42001 provides a structured framework for implementing AI governance across an organization, similar to how ISO 9001 does for quality[6]. It’s the world’s first standard of this kind, and it emphasizes managing AI’s unique challenges (like ethics, transparency, continuous learning) in a systematic way[6]. While you don’t need this certification to start a project, the fact that it exists signals that AI governance is becoming formalized. Forward-looking executives may choose to align with ISO 42001 principles to demonstrate to clients and regulators that they manage AI responsibly (and it might save headache later if certification becomes a market expectation).

Build vs. Buy vs. Blend (and When to Switch)

A critical funding decision is which parts of the solution you build in-house, which you buy from vendors, and where you blend the two. The savvy approach is not an either/or binary, but a sequence over time. Start API-first (buy) to get something working quickly. For example, instead of training your own language model on day one, you might call OpenAI’s or Azure’s API and focus on integrating it into your product. This gets you to market faster and with lower upfront cost. But buying everything can be expensive at scale or may limit differentiation, so revisit the decision at defined checkpoints. Perhaps after the 90-day PoV or once you hit a certain user load, you evaluate if training a custom model or building an in-house data pipeline will significantly reduce long-term costs or improve performance.

Use a decision rubric to guide build-vs-buy choices. Factors should include: time-to-value, total cost of ownership over 2–3 years, regulatory/compliance requirements, data privacy needs, and portability (how hard would it be to switch out this component later?). For instance, if an external API is compliant today but might not meet upcoming EU AI Act criteria, you might lean toward building that component internally for more control. If using a cloud vendor’s AI service locks you in, consider whether portability is important for your strategy. On the flip side, if a vendor solution has security certifications and your internal team doesn’t, buying could actually reduce risk.

Importantly, be ready to mix approaches. Analysts note that companies are increasingly combining buying and building in hybrid patterns rather than sticking rigidly to one strategy. As Forrester Research puts it, instead of a binary build-vs-buy choice, there are many approaches on a spectrum – “as many as eight distinct approaches” in delivering solutions[7]. For example, you might buy an AI SaaS tool for a commodity capability (say, OCR or speech-to-text), build custom components for your secret sauce (like proprietary algorithms using your unique data), and blend by customizing an open-source model with your data (fine-tuning) in areas where off-the-shelf is close but not perfect. The funding plan should allocate resources for both paths: some budget to license or subscribe to best-in-class AI services, and some to hire or develop internal IP where competitive advantage or data sensitivity dictates. And critically, set switching milestones: e.g., “If our usage of Vendor X’s API exceeds N calls/month or cost Y, we will invest in building our own module” or “If our off-the-shelf model’s accuracy plateaus below target, we will explore custom training by Q2.”

Budget Structure Your CFO Will Accept

Break your AI project budget into clear pieces that a CFO can scrutinize. A recommended structure is: (1) One-time Build Costs, (2) Ongoing Run-Rate Costs, and (3) Contingency. Presenting it this way shows that you understand the difference between one-off investments and recurring operational costs, and that you have a buffer for uncertainties.


Example breakdown of an AI project budget into one-time build costs, annual run-rate, and a contingency reserve.

  • Build (One-time): This includes upfront engineering and data work – e.g., integrating the AI into existing systems, developing new features, setting up cloud infrastructure or a data pipeline, and any initial vendor fees or consulting. It might also cover the cost of a pilot (PoV) development. For example, you might estimate $200K for all development and integration efforts to get the AI solution up and running in a pilot environment.
  • Run-Rate (Ongoing): These are the monthly or annual costs to keep the AI service running. Typically this will include cloud compute charges, API usage fees for any third-party AI services (e.g. calls to an NLP or vision API), subscription fees for a vector database or MLOps platform, and logging/monitoring costs. For instance, if you expect heavy usage of an NLP API, you might project $10K/month in usage fees, plus $5K/month for cloud infrastructure and support – roughly $180K/year run-rate. It’s wise to show this as an annual figure and to note any assumptions (e.g., number of transactions or users) so the CFO can see what drives ongoing costs.
  • Contingency (~10–20%): Given the uncertainties in AI projects (maybe data cleaning is harder than expected, or you need to retrain a model), set aside a contingency budget. Typically 10–20% of the total project cost is reasonable. This is your safety net for unexpected needs – extra data labeling, higher cloud bills if usage spikes, or additional governance tools to meet compliance. Not only does this make you look prudent, but if not used, it comes as a pleasant “under-budget” surprise later.

Within the run-rate, highlight that you have a plan to optimize costs over time. CFOs worry about the blank-check nature of AI (“what if usage skyrockets and so do costs?”). Show levers you can pull to reduce expense if needed. For example, OpenAI’s own pricing offers a Batch API option that can cut costs by ~50% for large-scale requests[8] – essentially you pay half price if you’re willing to process jobs asynchronously (up to 24-hour delay) instead of real-time. Similarly, Anthropic (maker of Claude) provides prompt caching which lets you reuse common prompt parts at a fraction of the cost: cached inputs cost only ~10% of normal token price (with a one-time 25% overhead to store them)[9]. These tactics – batching and caching – mean you can cap or significantly reduce the per-unit cost as usage grows, by trading off immediacy or by reusing results. Include links or footnotes to the official pricing pages (e.g., OpenAI API pricing[8]) to show transparency.


Cost-saving tactics for AI APIs: using OpenAI’s Batch API for half-price processing[8], and Anthropic’s prompt caching to greatly reduce token costs on repeated content[9].

Another cost control is setting hard usage limits or alerts – e.g., configure the cloud account to alert at $X spend or throttle requests beyond a cap. The bottom line to the CFO: we have a plan to prevent runaway bills. Also, consider committing to usage with vendors in exchange for discounts (many providers will negotiate enterprise deals at lower unit costs if you commit to a certain volume). All these details reinforce that your budget is grounded in reality and has both upside and downside scenarios considered.

Data Readiness (the #1 Hidden Cost)

Data is often the make-or-break factor (and expense) in AI projects – more so than the algorithm itself. Many AI initiatives stall not because the model didn’t work, but because the data wasn’t there, wasn’t clean, or couldn’t be integrated into workflows. Executives should hunt for hidden data costs early. This means auditing what data sources you’ll need (internal and external), checking access rights (do you actually have permission to use that customer data for AI? GDPR and other privacy laws matter here), and assessing quality (how much cleaning or labeling is needed).

Be realistic about the data integration effort. If your AI solution needs to pull from three legacy systems, have you accounted for the ETL (extract/transform/load) work to unify that data? If the data is sensitive (PII, health data, etc.), include the cost of privacy safeguards or anonymization. Often, setting up a simple data lake or warehouse to consolidate data for the AI is a necessary precursor – that can take weeks of effort and budget that’s easy to underestimate.

Surveys consistently find data readiness to be a top barrier to scaling AI. For example, Boston Consulting Group reported in 2024 that 74% of companies have yet to achieve tangible value from AI at scale, and a big reason is not tech algorithms but gaps in “people and process” capabilities (which includes data governance and integration)[10][11]. In fact, BCG found about 70% of challenges in AI implementation come from organizational and data issues, versus only 10% from the AI algorithms themselves[11]. The takeaway for an executive: don’t pour millions into a fancy model before ensuring your data house is in order. If 70% of the challenge is data and process, allocate resources accordingly – possibly the first phase of your project (Phase 0) is a data readiness assessment. It’s often worth doing a quick data quality proof-of-concept: take a subset of data, run it through the pipeline, and see what breaks. This can reveal if you need a data cleaning tool, or if you have missing data that requires revisiting your use case assumptions.

Plan for minimal viable data pipelines initially. Rather than a perfect, fully automated data pipeline feeding the AI (which could take 6-12 months to build), figure out the smallest pipeline that can get you through the 90-day PoV. Maybe some steps are manual or done with smaller data extracts. The goal is to learn where the pain points are without boiling the ocean. For instance, if you’re building an AI to personalize marketing offers, perhaps start with one region’s data or one product line, and a manual weekly data dump, before connecting all systems enterprise-wide. This way, you uncover issues in a contained way and avoid sinking huge cost upfront.

Lastly, don’t neglect data lineage and governance. As part of risk management (and compliance), keep track of where the data comes from, how it’s transformed, and who is responsible for it. If your AI is making decisions that could be challenged (legally or by customers), you’ll want an audit trail showing the data pipeline. Yes, this is yet another task, but it’s far cheaper to bake it in from the start than to retrofit it under regulatory pressure later.

Architecture You Can Actually Operate

Many AI projects falter at the pilot stage because they aren’t designed for the messy reality of the company’s IT landscape. As an executive, insist on an architecture that’s practical to operate – not a fragile science project that only lives on a data scientist’s laptop or a sandbox cloud account. Two principles help here: keep integrations thin, and align with your IT ops norms (security, monitoring, etc.) from day one.

“Thin” integration patterns for legacy systems. Unless you’re in a greenfield environment, your AI will need to interface with legacy IT (CRM, ERP, databases, etc.). Avoid deeply embedding AI into legacy code in the initial phases. Instead, use facades or adapters. For example, wrap the AI model behind an API service layer – so the AI interacts with other systems through well-defined API calls or a message bus. This decouples the AI component and makes it easier to swap out or update independently. Similarly, consider using an event-driven approach: e.g., drop data that needs scoring into a message queue or Kafka topic, have the AI service consume it, and then output results back into downstream systems via another queue. This way you’re not rewriting the internals of your core systems upfront. A data lake or lakehouse can also serve as an intermediary – you export data from legacy sources to the lakehouse, run AI processing there, then feed results back. The aim is to minimize changes to mission-critical systems until the AI value is proven. Once your PoV is successful, you can gradually refactor core systems to more elegantly incorporate the AI if needed, but by then you’ll have justification to do so.

Choose where inference runs (cloud vs on-prem vs edge). This is an architectural decision with cost and compliance implications. Cloud is often the fastest way to start – you can spin up GPU instances or use a managed AI service and not worry about infrastructure. But if your data is highly sensitive or subject to residency laws, you might need on-premise deployment or a hybrid (processing data locally, but using cloud for the heavy model tasks with tokenization to protect sensitive fields). Latency is another factor: for user-facing features that need sub-second responses, an on-prem edge server or a model running directly in-app might be necessary to avoid network delays. Conversely, if you’re doing batch analytics, a 2-second vs 200ms response time is irrelevant and cloud is fine. Make an intentional decision and document it. And remember, the decision can change over time – you might start cloud-based during the PoV and early rollout (to leverage agility and scaling), but plan a migration to on-prem appliances in a year if that makes financial or regulatory sense. Budget for that potential shift in your roadmap if applicable.

Bake in security and monitoring from day one. This cannot be an afterthought. Ensure your solution integrates with your existing Identity and Access Management (IAM) systems – e.g., the AI’s API should require proper authentication and authorization. Logging is crucial: every inference or recommendation the AI makes, ideally, should be logged (including input data reference and output) for traceability. Set up basic monitoring on the AI service – uptime, latency, error rates, and drift in input data or model confidence if possible. This ties back to the NIST AI RMF’s Manage function: you need processes to prioritize and act on risks on an ongoing basis[12]. Regular monitoring and improvement are part of that[13]. For instance, you might implement an alert if the AI’s outputs start deviating (e.g., a spike in “I don’t know” responses or a drop in accuracy against a holdout set). Also include the AI system in your existing incident management process – e.g., if the AI service goes down at 2am, is your NOC (Network Operations Center) aware and able to restart it, or will it silently fail until someone notices? By treating the AI component as a first-class citizen in your IT architecture (with the same rigor around security, permissions, and reliability), you set yourself up to actually operationalize and scale it rather than getting stuck in perpetual pilot mode.

Talent & Operating Model

Who will actually execute this AI project and run it day-to-day once live? Don’t assume your existing org chart will absorb it magically. Successful AI initiatives often start with a small, cross-functional “tiger team.” You’ll need a blend of skills: a product manager or business lead who deeply understands the use case, one or more software engineers to integrate and deploy the solution, a data scientist or ML engineer to handle the modeling (if custom work is needed), plus representation from data engineering (for pipeline), and importantly security/compliance. This core team (maybe 5–7 people) should work with startup-like agility. They will also interface with existing IT and business units – so make sure they have air cover from the exec sponsor to get what they need from other departments.

Upskill and assign clear roles for governance. Beyond the project team, think about who in the org is going to own ongoing AI oversight. Many companies are now creating senior roles or councils focused on AI governance and value capture[14][2]. For example, some have appointed a Head of AI Governance or expanded the CIO’s remit to include AI oversight. McKinsey’s research correlates CEO and board-level involvement in AI with higher success rates[2] – in their latest survey, 28% of companies said their CEO oversees AI governance, and those tended to outperform[15]. The lesson: treat AI as important enough to get top-level attention. If you’re funding a major AI project, consider establishing a steering committee that meets monthly, including the exec sponsor, representatives from risk/compliance, and the project lead, to review progress and issues. This ensures that when trade-offs need to be made (e.g., launch a feature vs. mitigate a risk), the decision-makers are engaged and informed.

Build internal capability for the long term. While you might use external partners or consultants initially (especially to fill talent gaps like specialized ML skills), have a plan to transfer know-how in-house. Perhaps assign an internal lead to shadow the consultant, or require documentation as a deliverable. Additionally, invest in training programs for your staff – whether that’s sending engineers to an AI bootcamp, or training business analysts on how to use AI tools. According to industry surveys, a major barrier is the human element – lack of skills and change management[16][17]. Many executives feel the tech is moving faster than their organization’s training can keep up[16]. You can counter that by proactively upskilling your teams. Even non-technical roles will need some AI literacy (for example, a marketing manager should understand what the AI can and cannot do, to set realistic campaigns). Encourage a culture of experimentation but within guardrails – perhaps implement an internal AI sandbox where employees can play with generative AI on dummy data to get ideas, under oversight.

Finally, delineate post-launch ownership. Once the AI tool or feature is live, who “owns” it in production? It might transition from the innovation team to an operations team. Make sure that’s planned – the worst outcome is the pilot is successful but then no one is assigned to maintain it, leading to decay. Often, the best approach is to keep the core team intact through Phase 2 (limited rollout) and only hand off to a broader operational owner in Phase 3 when scaling. At that point, you might create a formal AI Product team or fold it into an existing product line team. Ensure the budget includes headcount for ongoing support (e.g., a data engineer to update the model or pipeline every quarter, etc.). AI isn’t a one-and-done deliverable; it’s more like a living system that needs care and feeding.

Milestones, Timeline, and Go/No-Go Gates

Lay out a phased timeline for the project with clear go/no-go decision points. Executives love seeing a roadmap that shows when they can expect results and when they will decide on next steps. Here’s a pragmatic 4-phase approach over roughly 6 months:

  • Phase 0 (Preparation, 2–4 weeks): Conduct a readiness and gap assessment. Before building anything, the team reviews data readiness, identifies any show-stoppers (e.g., need legal approval for data use, or must upgrade a database), and finalizes success criteria. Deliverable: a brief report on “Are we good to go?” plus a refined project plan. Gate: If critical gaps are found (no accessible data, regulatory approval missing), pause here to address them rather than charging ahead blindly.
  • Phase 1 (Pilot/PoV, 6–12 weeks): Develop the proof-of-value solution in a production-adjacent environment. “Production-adjacent” means it’s close to real deployment – perhaps in cloud environment mirroring prod, but not live to all users. The AI model is integrated with sample or shadow data flows, and a small group of users or test cases exercise it. By the end of this phase, you should have KPI measurements comparing the AI-enabled process to the baseline. Gate: Evaluate against the target KPI delta and a risk checklist. For example, “Did we achieve at least +10% improvement in efficiency? Did any high-severity risks materialize in pilot (e.g., regulatory non-compliance or severe errors)?” If the PoV fails the criteria, you decide to either pivot the approach or halt the project. If it meets or exceeds expectations, you get the green light to proceed.
  • Phase 2 (Limited Rollout, 8–16 weeks): Now deploy the AI solution to a broader audience or full production in a limited scope. “Limited scope” could be a subset of customers, a specific region, or one business unit – the idea is controlled exposure. Implement continuous monitoring here: you’re now dealing with real users/data, so put in place dashboards and alerting for the AI’s performance. Also, this is where you fine-tune based on feedback – maybe users are confused by an AI-generated recommendation, so you tweak the UI or the model. The duration can vary; some teams iterate quickly in a couple of months, others take longer to integrate feedback. Gate: End of Phase 2 is typically a major checkpoint with executives. Is the solution delivering the promised value consistently? Are operational costs in line with expectations? Are risk mitigation measures working (e.g., no major compliance issues)? If yes, you prepare to scale; if not, you might roll back to pilot mode or even shelve the project if it proves unviable at scale.
  • Phase 3 (Scale or Pivot, timeline varies): This phase moves the solution from a limited rollout to full deployment across the organization (or to additional use cases). It often involves refactoring or hardening the system now that you know it works. For instance, you might improve the data pipeline for efficiency, or retrain the model with a larger dataset for better accuracy. If the project is a success, Phase 3 is about scaling up – more users, more transactions, maybe geographic expansion, and embedding the AI into standard operating procedures. If the project under-delivered, Phase 3 could be a pivot – applying lessons learned to a new approach or a different use case. In funding terms, Phase 3 might require a fresh business case for additional investment, justified by Phase 2 results.


Illustrative timeline for an AI project in phases, with “go/no-go” gates after key milestones. Phase 0 ensures readiness, Phase 1 proves value in a pilot, Phase 2 rolls out carefully with monitoring (note the red Gate checks), and Phase 3 scales up if all looks good.

Using such phased milestones not only helps manage risk but also builds confidence with stakeholders. You as the executive sponsor can update the board, “In 3 months we’ll have a PoC in market, and by 6 months we’ll know if we’re scaling or not.” It demonstrates disciplined management. Be sure to include a rollback plan as part of your gating criteria – e.g., if at Phase 2 gate the results are poor, how will you wind down gracefully (turn off the AI feature, notify users, revert processes to old way, etc.)? It’s like an exit strategy for the project. Often just the act of planning rollback makes the team more diligent in proving value.

What Can Go Wrong (and How to Avoid It)

Even well-funded AI projects can run into trouble. Here are a few common failure modes and how to mitigate them:

  • Regulatory Surprises: Laws and regulations can change faster than your project timeline. The EU AI Act is a perfect example – you might start a project thinking it’s unregulated, and midway find that new rules (e.g., for general-purpose AI or transparency) will apply by next year. To avoid being blindsided, designate someone (or a group) to own regulatory monitoring. They should produce a brief impact assessment for your project: “If X regulation comes into force on date Y, what do we need to do to comply?” For instance, if you’re developing an AI that could be considered a HR tool, keep tabs on employment fairness laws or EEOC guidance. Maintain a document repository for compliance artifacts (data protection impact assessments, model transparency docs, etc.) – even if not strictly needed yet, doing them as you build saves pain later. Essentially, treat compliance as an ongoing workstream, not a one-time checkbox. If something like the EU AI Act imposes a new requirement (say, a registration of your AI system or an audit), you’ll have much of the info ready instead of scrambling. And don’t rely solely on vendors to handle this; if you’re using a third-party model, you still bear responsibility for its outputs under many regulations.
  • Run-Rate Shock: Sticker shock from the operational costs is a classic “failure” – the project works technically, but the usage bills are unsustainable. This often happens when AI services charge per use and usage ramps up unexpectedly. To prevent this, implement cost controls in architecture: for example, use batch processing for non-time-sensitive jobs so you pay half the price (as mentioned, OpenAI’s Batch API gives a 50% discount[8]). Implement caching of results so you don’t recompute expensive operations unnecessarily (Claude’s prompt caching can cut costs dramatically for repeated queries[9]). Also, use tiered model deployment – not every query needs the most expensive 100B-parameter model; perhaps you route simpler queries to a cheaper model and only escalate to the expensive one when needed. On the management side, set budgets with alerts: e.g., “notify if monthly AI spend exceeds $X” and have an auto-throttle. It’s much easier to have these controls in place from the start than to try to bolt them on when finance notices a budget overrun. By carefully choosing model sizes, using cost-saving features, and monitoring usage patterns, you keep run-rate within planned bounds. Always communicate to stakeholders that initial usage might be low-cost, but if the project scales company-wide, costs will scale too – hence the need for ongoing cost optimization efforts.
  • Integration Drag: This is the scenario where integrating the AI into existing systems takes far longer (and more money) than anticipated, delaying time to value. It can happen if the AI team works in a silo and then faces an “immune system” reaction from IT or if legacy tech doesn’t play nice. The mitigation is two-fold: technical and organizational. Technically, as we discussed, use loose coupling (APIs, message buses) so integration is more plug-and-play. If your core system is decades old, you might choose a simpler integration path, like writing outputs to a database that the old system reads from, rather than touching the old system’s code. Organizationally, involve the IT/DevOps teams from the get-go. If the AI needs to connect to a CRM database, have the CRM team aware and participating. Also, sequence integrations gradually – don’t try to connect to five systems at once; integrate one, see it working, then move to the next. An agile, iterative integration plan (maybe deliver one integration every two-week sprint) helps surface issues early. Additionally, keep an eye on change management: if the AI is altering a business process, the people who use that process need training and time to adapt. Sometimes integration drag is not tech at all, but user adoption – the AI was integrated technically, but employees created workarounds to avoid using it. Overcome this by involving end-users in design and by leadership reinforcing the change (“this AI tool is now how we do X process”). To sum up: integrate in bite-sized chunks, engage stakeholders early, and don’t underestimate the last mile of making the AI part of “how work gets done.”

Executive Checklist (Pre-Funding)

Before you sign that big check or officially green-light the AI project, run through this checklist to ensure all bases are covered:

  • ✔ Defined Business KPI & Owner: You have a clear metric for success (e.g., increase conversion by 15%) and an executive or manager accountable for that outcome. It’s not just an “AI for AI’s sake” project – it has a business target.
  • ✔ Governance Framework Adopted (e.g. NIST AI RMF): The team will follow a defined AI risk management process. Roles and responsibilities for AI governance are set (who oversees risk, who signs off on model ethics, etc.)[18][19]. If using NIST AI RMF, you’ve addressed all four functions (Govern, Map, Measure, Manage). There’s a RACI chart or similar showing who’s responsible for what in governing this AI.
  • ✔ EU AI Act Assessment Done: If there’s any chance the AI system or its outputs will be used in the EU (or other relevant jurisdictions), you have documented which provisions of upcoming AI regulations apply. For example, you noted if it’s a general-purpose AI (GPAI) and the obligations effective Aug 2, 2025[4], or if it’s high-risk and needs certain compliance steps. You have a timeline for compliance activities that matches the regulatory timeline.
  • ✔ Data Sources Identified & Privacy Checked: You know exactly what data is needed and from where. Access has been secured (no waiting on approvals mid-project). Privacy impact assessment (DPIA) is done if required for using personal data. If using customer data, you have customer consent or a legal basis. No “data surprises” lurking.
  • ✔ Build vs. Buy Analysis Completed: A brief of why you’re building or buying each major component exists. You have criteria set to revisit the decision. Also, you’ve defined exit strategies – e.g., if you use Vendor A now, you know how you’d switch later if needed (data portability, contract terms, etc.).
  • ✔ Budget Mapped with Cost Controls: The budget is broken into build/run/contingency and vetted by finance. You’ve explicitly planned cost control measures (using batch processing, caching, selecting cost-efficient model sizes, etc.)[8][9]. There are guardrails so you won’t have to go back for a blank check later.
  • ✔ 90-Day PoV Plan with Success Criteria: The project plan includes a detailed first 90 days to prove value, including what constitutes success or failure. You have a success gate defined at the end of the pilot and a rollback plan if things don’t work out. Monitoring is set up to track the KPI in that period.
  • ✔ Post-Pilot Decisions & Scaling Plan: You know what happens if the pilot succeeds (who gets more funding, how quickly you scale to more users, etc.) and if it fails (how you’ll stand down the effort or pivot to a new idea). Basically, you’ve thought a step beyond the pilot so the organization isn’t caught flat-footed.

If you can tick all (or most) of the above, you are far better positioned to fund and launch an AI project that delivers and doesn’t become a science experiment or a compliance headache.

Conclusion & Next Steps

AI projects can unlock substantial value, but they require a blend of strategic focus, prudent planning, and cross-functional execution. As an executive, your role is to ensure the right questions are asked upfront and that the project is structured for success – both in terms of impact and responsible management. By defining a clear use case and KPI, instituting governance (think NIST AI RMF) and compliance checks early, balancing build-vs-buy for speed and control, and keeping a tight rein on budget and data readiness, you set the stage for a win rather than a write-off.

Remember, the goal in the first 90–180 days is learning and proving value. If you achieve that, scaling can follow with confidence (and further funding). If not, early course-correction will save time and money. Use the checklist to pressure-test your plan before committing funds – it’s cheaper to adjust on paper than mid-project.

Finally, don’t go it alone. Leverage internal experts across IT, data, and risk, and consider external partners where you have gaps. Our team at High Peak has helped numerous executives navigate these early stages of AI initiatives. If you’d like to discuss how to accelerate your AI product development or need integration support, we’re here to help. Check out our offerings in AI Product Development, AI Integration, AI Design, and AI Marketing – or reach out for a scoping conversation tailored to your situation.

Disclaimer: Figures and timelines provided are directional planning estimates based on industry benchmarks and our experience. They do not constitute legal advice or guaranteed outcomes. Always tailor to your organization’s context and consult appropriate experts (legal, compliance, technical) when implementing AI solutions at scale.