by Dan Corcoran, Chief Technology Officer, Chief Information Security Officer, VP Sales Enablement
Artificial intelligence (AI) has made it easier than ever to create content. One prompt can generate a whole raft of executive summaries, business cases, ROI narratives, value hypotheses, industry benchmarks, and customer-facing presentations in seconds.
But speed isn’t always better. AI has also ushered in what many call “AI slop”—content that sounds authoritative but lacks accuracy, context, and meaningful analysis.
In many cases, that’s simply annoying but in business value management, it’s a serious problem. Because when sellers, consultants and executives rely on business cases to justify multimillion-dollar investments, accuracy matters and weak arguments undermine creditability. Even worse, a flawed assumption or fabricated benchmark or unsupported value claim erodes trust and can even delay (or derail) a decision.
As organizations race to embed AI into their sales and value-engineering processes, the question is no longer whether to use AI, it’s how to do so without sacrificing trust.
The Problem with AI-Generated Business Value Content
Large language models are exceptionally good at producing polished language that—on the surface—sounds convincing.
For example, ask any AI tool to generate a business case for a network modernization initiative, and it will likely produce a polished narrative explaining how the investment will improve productivity, reduce downtime, lower operating costs, and drive innovation. It may even sound like something a seasoned consultant wrote.
But where did those claims come from?
Were they grounded in customer data? Supported by industry benchmarks? Informed by real implementation experience? Or did the AI simply predict the most statistically likely response based on the information it was trained on?
More importantly, did it offer a unique point of view, or did it simply repackage information that already exists?
More often than not, the answer is the latter.
The Hidden Costs of AI Slop
AI is built to predict the next likely word, not validate the information it provides, and in business value conversations that distinction matters.
A CFO doesn’t approve $20 million investment because an AI generated a well-writtten paragraph. They approve investments because the underlying analysis is explainable, defensible, and tied to measurable business outcomes.
When polished content is mistaken for credible business value, the consequences extend far beyond a few inaccurate statements.
- Loss of executive creditability. Business value teams spend years earning the trust of executives, and that trust can disappear surprisingly quickly. One unsupported benchmark or fabricated ROI assumption can unravel months of relationship building. Instead of discussing the opportunity, buyers begin scrutinizing the numbers, the methodology, and ultimately the credibility of the team presenting them.
- Inconsistent customer experiences. AI can also create inconsistencies at scale. Without governance, different sellers can receive dramatically different answers to the same question. One AI-generated business case may emphasize productivity savings, another may emphasize revenue growth, while a third may invent benefits that were never part of the solution’s value proposition. The result is weakened messaging and confused customers.
- Slower sales cycles. Ironically, AI slop creates more work. Teams are forced to spend valuable time fact-checking AI-generated content, correcting assumptions, rebuilding analyses, and explaining discrepancies to customers. What was intended to accelerate the sales process becomes another layer of review before a business case can be trusted.
- More risk. Unsupported claims carry real consequences. They can create compliance concerns and procurement challenges. They can also invite greater executive scrutiny and expose organizations to unnecessary reputational risk. When buyers lose confidence in the numbers, deals rarely move faster—they stall while every assumption is reexamined.
Why Human Expertise Still Matters
Creating a credible business case requires understanding the customer’s environment, selecting appropriate assumptions, applying relevant industry benchmarks, validating financial calculations, and translating technical capabilities into business outcomes that executives care about.
Those aren’t tasks you accomplish by generating more content. They require domain expertise, consulting experience, financial acumen, and organizational context.
AI can accelerate parts of that process. It can’t replace it.
Mainstay’s Services as Software Approach
That reality is exactly why Mainstay developed its Services as Software model.
Rather than starting with software and asking customers to adapt their methodologies to fit the platform, Mainstay starts with the methodology itself. Years of consulting experience, proven value models, business cases, and subject matter expertise become the foundation. Only then are those methodologies transformed into scalable digital experiences.
It’s a fundamentally different way of thinking about AI.
Most AI platforms ask, “How can AI generate a business case?” Mainstay asks, “How can AI help scale a proven business value methodology?” That distinction shapes everything we build.
That’s because a business case is only as credible as the logic behind it. The Advisor platform isn’t built around AI-generated text. It’s built around validated business logic. Organizations can leverage the value models and business case methodologies they’ve spent years developing while Mainstay provides the software, governance, and scalability needed to operationalize them across the business.
Instead of inventing value narratives, AI works from proven financial calculations, established value frameworks, customer-approved methodologies, industry expertise, and real business outcomes. Its role isn’t to create value—it’s to help explain, communicate, and scale value that’s already been validated.
Explainable AI, Not AI Slop
That same philosophy guides how Mainstay is building AI into the Advisor platform.
Rather than asking AI to produce answers without context, Mainstay emphasizes transparency, source attribution, and human oversight. For example, the platform’s Researcher capability identifies required inputs, searches supporting source material, cites evidence, displays confidence levels, and allows users to review recommendations before accepting them.
Likewise, the AI Sales Assistant helps explain calculations, connect technology capabilities to business outcomes, interpret analysis results, generate executive-ready narratives, and answer follow-up questions about assumptions and risk.
In both cases, AI accelerates the work while people remain accountable for the outcome.
The Future of AI in Business Value
As AI becomes commonplace, generating content will no longer be a competitive advantage. Generating trust will.
Organizations that succeed won’t be the ones producing the most business cases. They’ll be the ones producing business cases that stand up to scrutiny because they’re built on sound methodology, reliable data, transparent assumptions, and human expertise.
Anyone can generate polished content. Far fewer organizations can produce a business case that earns the confidence of a CFO, procurement team, finance organization, and executive committee.
That’s where Mainstay’s Services as Software philosophy delivers a different outcome. By combining proven value engineering methodologies, human expertise, scalable software, and responsible AI, organizations can scale business value without sacrificing credibility.
Because in business value management, success isn’t measured by how quickly you generate an answer. It’s measured by whether people trust it.
Learn why so many leading organizations trust Mainstay. Check out Mainstay’s advisor platform or contact us.
