The State of Generative AI in the Enterprise 2025

In this episode of The Metrics Brothers, Ray Rike and Dave Kellogg break down the 2025 State of Generative AI in the Enterprise report from Menlo Ventures and explain what the data really says about where enterprise AI adoption is accelerating and where the market is consolidating.

The headline takeaway: AI software is scaling faster than any software category in history. Enterprise AI spend has exploded from roughly $1.7B in 2023 to nearly $37B in 2025, reaching scale in just three years. This revenue milestone took SaaS more than 15 years to achieve. Foundational models now represent the single largest area of spend, highlighting how infrastructure and model access remain core to enterprise AI strategies.

Ray and Dave also explore a major strategic shift inside the enterprise: buy is decisively beating build. In 2025, 76% of enterprise AI solutions are purchased rather than built internally, up sharply from 53% the year prior. Rapid model evolution, ongoing retraining costs, and model drift are making internal AI development far more expensive to maintain than many teams originally expected.

One of the most surprising findings is on go-to-market efficiency. AI software pilots convert to production at nearly twice the rate of traditional software, with roughly 47% of AI pilots reaching production versus about 25% for conventional enterprise software. This runs counter to recent narratives suggesting enterprise AI pilots are stalling and points to clearer ROI and faster time-to-value.

The episode also dives into what Menlo calls the first true “AI killer app”: AI-assisted coding. Coding tools now account for more than half of departmental AI spend, with over 50% of developers already using AI coding assistants and adoption exceeding 65% among top-quartile teams. Real-world examples show meaningful productivity gains, including double-digit increases in development velocity and significant time savings during legacy system upgrades.

Industry-wise, healthcare emerges as the largest buyer of vertical AI, representing 43% of vertical AI spend. This is notable given healthcare’s historically lower IT spend as a percentage of revenue. Much of the value is coming from administrative automation such as medical scribing, where AI directly reduces non-clinical workload and unlocks meaningful productivity gains for care providers.

Finally, Ray and Dave examine the shifting competitive landscape among foundation model providers. Anthropic has surged to roughly 40% share of enterprise AI usage, up dramatically from prior years, while OpenAI’s share has declined as Google continues to gain traction. The discussion centers on focus versus breadth and why enterprise positioning and reliability may matter more than consumer mindshare.

Key takeaways from the episode:

• AI software is the fastest-scaling software category ever
• Enterprises are rapidly moving from build to buy
• AI pilots convert to production at nearly 2x traditional software
• AI coding is emerging as the first true enterprise AI killer app
• Anthropic’s enterprise focus is translating into meaningful market share gains

If you care about how AI adoption actually translates into spend, productivity, and competitive advantage inside large organizations, this episode is a must-listen.

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The claim that “95% of AI projects fail” has become one of the most repeated talking points in enterprise AI. But where did it come from, and does it actually hold up?

In this episode, Dave "CAC" Kellogg and Ray "Growth" Rike take a detailed, data-driven look at the MIT NANDA report, titled The GenAI Divide: State of AI in Business 2025. They break down how the "95% fail rate" statistic went viral, why it stuck, and why the underlying evidence does not support such a sweeping conclusion.

What Ray and Dave cover:

• Why the NANDA report is often mistaken for a peer-reviewed academic study when it is not
• How ambiguous definitions of “failure” turn partial adoption into sensational headlines
• Data inconsistencies and methodological gaps that undermine the 95% claim
• The difference between failed AI initiatives and early-stage pilots or experiments
• Why measuring AI success by the percent of projects is misleading compared to the business value created
• The rise of Shadow AI and employee-driven adoption, and why that may be a feature, not a flaw
• How the report’s conclusions conveniently align with the authors’ proposed NANDA architecture
• The real issues enterprises face with AI: workflow integration, governance, and change management

The episode also discusses why personal productivity gains still matter to the P&L, even if they do not appear as a clear line item, and why fear-driven AI narratives can do real damage within organizations.

Key takeaway:

The NANDA report raises some legitimate concerns about scaling AI from pilot to production, but the infamous “95% of AI projects fail” claim does not survive close inspection. Leaders should read the report skeptically and push back when flawed statistics begin to drive decisions and strategy.

Recommended for:

CFOs, operators, AI leaders, and anyone tired of scary AI statistics that fall apart under scrutiny.

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Brand vs Demand: Why B2B Marketing Is Stuck in a Measurement Trap

In this episode of The Metrics Brothers, Dave "CAC" Kellogg and Ray "Growth" Rike tackle one of the most persistent and controversial questions in B2B marketing: Brand vs. Demand.

The discussion is grounded in new data from the 2026 B2B Brand vs Demand Benchmark Report. While most marketing teams say they believe brand and demand are complementary, the numbers tell a more complicated story.

Today’s reality?

Marketing budgets are still heavily skewed toward short-term demand generation, with roughly 70% of spend allocated to demand and only ~25% to brand. Yet when asked how they want to invest, marketing leaders overwhelmingly say they’d prefer a much more balanced future, closer to 50% demand and 40% brand.

So why the disconnect?

Ray and Dave dig into the root cause: measurement.

Demand generation is tied to metrics CFOs understand like pipeline dollars, opportunities, and ARR. Brand, on the other hand, is still largely measured using proxy metrics like website traffic and awareness, leaving many executives unable to confidently link brand investments to revenue outcomes. Only 28% of companies say they can directly tie brand activity to pipeline, and when budgets are cut, brand is sacrificed five times more often than demand.

The episode also explores:

• Why performance marketing struggles are pushing CMOs back toward brand
• The growing inefficiency of demand spend aimed at “future buyers”
• How much of the “demand” budget is effectively unmeasured brand spend
• The dangerous gap between belief in brand and proof of impact
• Why AEO, AI search, and LLM visibility will make brand ROI even harder and more urgent to measure

Ray and Dave don’t just highlight the findings, they discuss the reality of Chief Marketing Officers making the Brand vs Demand budget allocation trade-offs.

One key takeaway? Until brand investments can be credibly connected to pipeline efficiency, win rates, and ARR, it will remain more a faith-based investment instead of a financial one the CFOs understand.

If you’re a CMO trying to defend brand spend, or a CFO trying to understand where marketing dollars truly drive growth, this episode is required listening.

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