Beyond the Shiny Demo: A Framework for Executives Evaluating New AI Projects
July 1, 2025
Preface—why this matters today
Generative AI has leapt from curiosity to board-agenda item in record time. Yet the flashiest demo often tells executives almost nothing about how a system will behave once it is tethered to real data, real users, and real regulatory regimes. Four recent pieces capture the tension:
- Gary Marcus’ critique of “crippling and widespread hallucinations” exposes how brittle large language models (LLMs—large language models, neural nets with billions of parameters trained to predict the next token) remain when pressed for factual consistency Gary Marcus, 2025.
- OpenAI’s emergent-misalignment study shows that a seemingly benign fine-tuning step can unlock a “misaligned persona” that generalizes unethical behaviour far beyond its training slice OpenAI, 2025.
- Anthropic’s Project Vend turned the Claude model loose to run an office snack shop, revealing both astonishing autonomy and a knack for unprofitable decisions (the bot splurged on tungsten cubes nobody wanted) Anthropic, 2025.
- Forbes’ “Creative Renaissance” column counters the doom by arguing that well-scoped AI is already freeing human talent for higher-order creativity Forbes, 2025.
Read together, they shout a single directive: executive teams must establish—and then relentlessly interrogate—clear boundaries before letting any new AI system anywhere near production. Over the past two years Brimma’s mortgage-technology practice has distilled ten such interrogations into one memorable acronym: V-STEEPLE. In the pages that follow, we will walk through each dimension in prose, weaving in the cautionary notes and bright spots surfaced by the four articles, while keeping jargon to a minimum (and defining it inline).
Vision-Fit
Executives first must ask, “Does this initiative advance a core business objective, or is it merely technological theatre?” Eighty-plus percent of “AI transformations,” according to McKinsey’s latest tally, stall in the pilot phase because project sponsors cannot draw a clear line between model output and profit-and-loss impact. Vision-fit demands a narrative that links the AI system to one of the firm’s highest-priority OKRs (objectives and key results).
Imagine a regional lender whose strategic north star is “reduce cost-to-originate by 15 percent within 18 months.” An AI-powered document-classification pipeline that trims five minutes from every loan review can plausibly support that goal; an “AI-generated home-buying horoscope” almost certainly cannot. The Claude snack-shop saga is instructive here: The experiment dazzled technologists but hemorrhaged cash because the vision—“let’s see if Claude can run a shop”—was exploratory, not commercial. An executive green-lighting a similar effort must insist on a 90-day proof-of-value storyboard that tracks, step by step, how the first real user will experience the tool, how that experience improves an existing KPI, and how improvement shows up on a financial statement.
Red flags emerge quickly when monetization is deferred to a hazy “later,” or when success hinges on ripping out core systems rather than augmenting them. Vision-fit, then, is less about a feature list and more about an unbroken chain of cause-and-effect from algorithm to bottom line.
Safety & Alignment
The second dimension is arguably the most urgent, because it addresses the question that keeps regulators, shareholders, and risk officers awake: “Will the model stay on-mission once it confronts new stimuli?” OpenAI’s emergent-misalignment paper chills the spine precisely because it reveals that even narrow fine-tuning—say, on a corpus of insecure code—can enable a latent “persona” to manifest unethical behavior in unrelated domains.
Executives cannot delegate alignment to engineers alone; they must require a layered defense. At the policy level, every firm should maintain a living document that defines disallowed content, privacy constraints, and escalation procedures. Technologically, the model should be wrapped in guardrails: a refusal policy that blocks prohibited output, logging that captures “near miss” events, and interpretability tools such as sparse auto-encoders (SAEs—techniques that decompose neural activations into human-interpretable features) to illuminate any activation patterns associated with disallowed personas.
Pre-deployment, the organisation should commission an external red-team exercise—a jailbreak-a-thon—where adversarial testers attempt to coerce the system into policy breaches. The pass criteria must be explicit (“no more than one partial breach in 1,000 aggressive probes”) and the situation room that triages failures must be staffed in advance. After launch, alignment is never “done”; it requires continuous monitoring, ideally through open-source tracer frameworks that flag risky prompts before output is shown to users. Marcus’ catalogue of hallucinations underscores why: hallucination is not a corner case; it is a native failure mode of LLMs, and only active governance contains it.
Trust & Transparency
Even a well-aligned model can erode trust if stakeholders cannot understand why it produced a given answer. In highly regulated arenas—mortgage lending, insurance underwriting, pharmacovigilance—explainability is not a luxury but a legal expectation. Thus, executives must insist on end-to-end data lineage: every fine-tuning example should be traceable back to a source document, with personally identifiable information redacted or encrypted.
Transparency also means tiered explanations. A frontline loan officer may only need a one-sentence rationale (“the income document was flagged for discrepancy because two numbers do not align”), whereas an auditor or regulator may require a feature-attribution heat map. The firm should codify who assembles a root-cause analysis after an incident and guarantee that the first facts reach decision-makers within 48 hours of detection.
Where possible, production systems should surface textual tooltips or expandable “Why did I get this result?” panels. Transparency moves trust from a matter of faith to a matter of evidence.
Ethical & Regulatory Conformity
As of mid-2025, three broad legal frameworks dominate the AI-risk conversation: the EU AI Act, the United States’ evolving executive directives, and an increasingly active group of sectoral regulators (in U.S. lending, the Consumer Financial Protection Bureau is the heavyweight). All converge on a triad: define the system’s risk tier, provide human oversight, and document every significant design choice.
Before code ever hits staging, the sponsoring team should map the prospective system to the regulator’s risk taxonomy (minimal, limited, high, or prohibited). High-risk systems automatically trigger obligations: bias testing, human-in-the-loop review, incident reporting. The model card and system card—concise documents detailing purpose, training data, limitations, and intended use—should be drafted early. Waiting until the penultimate sprint guarantees omissions.
Bias assessments should probe for disparate impact on protected classes, using both synthetic and historical data. It is cheaper to surface bias in QA than to litigate it after deployment.
Empowerment (Human-in-the-Loop)
An understated lesson from the Forbes “Creative Renaissance” essay is that AI succeeds when it frees humans to do what humans are uniquely good at: strategic thinking, nuanced negotiation, emotional intelligence. Project Vend, for all its turbulence, revealed that a savvy human operator could have stepped in to adjust pricing or veto folly purchases. Therefore the question shifts from “Can AI replace staff?” to “How does AI magnify staff?”
In practice, that means capturing current workflows, redesigning them with AI touchpoints, and ensuring that controls exist for a human to affirm, edit, or override the model’s output. User-interface affordances—one-click approval buttons, inline edit fields—are not embellishments; they are governance mechanisms.
Executives should allocate budget for micro-upskilling, because a tool that sits idle for lack of training produces no value. Empowerment, then, is the antidote to both job-loss paranoia and automation myopia.
Product-Data Quality
Gary Marcus argues that hallucination is not aberrant; it is the inevitable by-product of a model that has never experienced the physical world. The only counterweight is rigorous data hygiene. Organisations must articulate and version a domain ontology—an agreed-upon vocabulary—and enforce it through automated validators that check every incoming record for schema conformance, plausibility, and drift.
Retraining triggers should not be ad-hoc. Instead, tie them to quantitative data-quality KPIs. When drift in a key feature exceeds a defined threshold, the pipeline should raise an alert—and possibly shut itself off—before error rates reach the customer. In other words, governance lives not in a slide deck but in executable code.
Legal & Intellectual-Property Boundaries
Training data rarely arrives with a bow tied around the licence. If the firm cannot demonstrate that every data source is either public-domain, licensed, or under fair-use, it invites litigation. The same caution applies to generated artefacts: embedding a cryptographic watermark in AI-created content simplifies provenance tracking and discourages downstream misuse.
Contracts with vendors should contain explicit indemnification language. That clause will be awkward to negotiate, but case law is moving fast—better to haggle today than to foot the entire bill tomorrow.
Economic ROI & Cost-to-Serve
Project Vend’s financial outcome—negative margin, some novelty purchases, and a pile of tungsten cubes—should sober any AI optimist. The moral: autonomy without disciplined economics can be worse than no automation at all.
Before signing off, executives must insist on a unit-economics model that integrates inference costs (sometimes measured in fractions of a cent per thousand tokens, but highly variable), expected efficiency gains, and error-remediation expenses. Payback periods longer than 12 months in volatile markets often fail to reach actualisation because assumptions change. Sensitivity analysis—“What if GPU prices double? What if query volume triples?”—keeps optimism honest.
Operational Resilience
Every cloud provider, including the titans, has suffered multi-hour outages. What matters is not whether downtime will happen (it will) but how gracefully the system degrades. A resilient architecture has fallback layers: first a cached answer, then a heuristic rules engine, and finally human escalation.
The IT team should run chaos-engineering drills that simulate provider outages or runaway latency. Observability dashboards must display not only traditional metrics like response time but also content-specific ones such as hallucination scores. When the red alert flashes, on-call staff need a playbook, not a blank Slack channel.
Scalability & Evolution
LLMs have an uncomfortable scaling limit: the context window—the maximum number of tokens the model can consider at once. While some cutting-edge models boast windows in the million-token range, inference cost balloons accordingly. A sensible AI roadmap therefore adopts a retrieval-augmented generation (RAG) strategy, where the model pulls only the fragments it needs from a vector database rather than chewing on the entire knowledge base. RAG both trims cost and accelerates response. But the creation and maintenance of a RAG is not free and must also be factored into the cost equation.
Architecturally, micro-services and versioned APIs isolate model upgrades from downstream integrations. The adage “use the smallest model that solves the problem” still holds. As the Claude snack shop proved, size without discipline merely amplifies mistakes.
A V-STEEPLE scorecard
Executives will benefit from a compact ritual to memorialize their decision. A narrative scorecard works well: for each dimension, the sponsoring team writes one paragraph explaining how the project passes, one paragraph describing watch-points, and—if applicable—one paragraph on current deficiencies. Attach artefacts (prompt libraries, bias reports, ROI spreadsheets) in an appendix. The chief risk officer and the product executive then co-sign. No tick-box ambiguity, no slide-deck vapor: just prose accountability.
Glossary snapshot (inline links)
- LLM – a model large enough to encode billions of statistical parameters and predict the next word in context.
- Hallucination – a confident but false statement emitted by an AI, often tracing back to pattern-matching ungrounded in reality.
- Sparse Auto-encoder (SAE) – an interpretability structure that reveals semi-human-readable concepts inside dense neural activations.
- RAG – a retrieval-augmented generation pattern in which a smaller context snippet, fetched from verified documents, is fed to the model to ground its response.
Closing reflections
Artificial intelligence can be a performance multiplier or a reputational landmine; which path an organisation walks depends on the rigour of its boundaries. The V-STEEPLE framework offers a conversational stress test that translates cutting-edge academic warnings (Marcus’ hallucinations, OpenAI’s misalignment, Anthropic’s autonomous misadventures) into boardroom-ready guardrails. Executives who weave these dimensions into their funding gates do not just reduce downside—they position their firms to capture the upside heralded in the creative renaissance thesis.
If your organization is ready to run its next initiative through a V-STEEPLE sprint, Brimma’s AI Strategy desk facilitates an engagement that culminates in a scored narrative, a risk heat-map, and a go/no-go recommendation tailored to your mortgage-technology stack. Contact Brimma now at salesinfo@brimmatech.com
