Glenn Gow, The Scaling Executive Coach and host of The Scaling Executive Podcast, spoke with Ted Krantz — CEO of Interos AI and former CEO of Data.ai — about the inflection point that separates SaaS companies that stall from those that scale. Krantz’s answer is unambiguous: CEOs who chase product-market fit confirmation through sales volume will overspend and underperform. CEOs who confirm it through product usage data — daily active use, ERP integration depth, and user breadth inside the enterprise — will know when to step on the gas and when to hold.
Krantz is the CEO of Interos AI, an AI-powered supply chain risk intelligence platform covering 250 million companies and 11 billion business relationships. He served six years as CEO of Data.ai and spent nearly nine years there as president before that. His operating background includes senior revenue leadership at SAP, C3 AI, and PeopleSoft.
This episode is for CEOs who have achieved initial product-market fit and are deciding whether to scale sales investment now or hold until the product earns it.
Key Takeaways
- CEOs who mistake partial product-market fit for full product-market fit will overfund sales before the product is sticky enough to retain what sales wins — confirmed product-market fit requires evidence of daily active use, not just contract volume.
- Daily active use is the clearest indicator that a SaaS product has crossed from nice-to-have to need-to-have; monthly or weekly active use signals a product still at risk of churn.
- ERP integration depth — how tightly a product connects to systems like SAP, ServiceNow, or PeopleSoft — determines long-term retention in enterprise B2B because those platforms carry inherently low churn.
- CEOs who move from a functional background (sales, revenue) to the full CEO seat must practice being last to speak while ramping across all other functions — speaking first locks the room into the leader’s initial frame.
- AI deployment across BDR and engineering functions specifically offers the highest operational leverage for scaling SaaS companies right now, because both are volume-intensive functions where quality output at scale creates durable competitive advantage.
How CEOs Diagnose Real Product-Market Fit vs. Partial Product-Market Fit
The most expensive mistake a scaling SaaS CEO makes is treating partial product-market fit as complete product-market fit. Krantz frames it directly: “I do think it’s often overlooked that you have partial product market fit and you assume that you have full product market fit. That’s a very different dynamic.”
The diagnostic is not NPS alone, though Krantz tracks that too. The primary signal is how the product is consumed. Krantz uses a three-tier usage framework to assess where any SaaS product sits on the stickiness curve:
| Usage Frequency | Signal | Stickiness Level |
| Monthly active use | Customer uses product occasionally; likely a nice-to-have | Low — high churn risk |
| Weekly active use | Customer has a repeating workflow built around product | Medium — retention possible with strong CS |
| Daily active use | Product is embedded in operational routine; approaching mission-critical | High — approaching need-to-have |
Krantz describes the diagnostic this way: “That nice to have versus need to have chasm is always something that is pretty interesting. So the use cases and the first thing I look at in a more modern realm of SaaS technology, now the adaptation of AI, is I look for the use cases in terms of how active our customers in the product. Is it a monthly active use case? Is it a weekly active use case? Is it a daily active use case? If it’s the latter, the more you’re getting closer to DAO in terms of the usability of the product, the stickier it’s going to be.”
Daily active use is the floor for confident scale investment, not the bonus outcome.
The second diagnostic is ERP integration depth. Krantz argues that enterprise SaaS retention in B2B is heavily determined by how embedded a product becomes inside the customer’s existing technology stack: “The ERP systems right where I came from, the PeopleSofts, the SAPs, even Bill McDermott, now at ServiceNow. These are very low churn rate investments that have end-to-end workflow functionality. So the more that you are integrated in rather than a separate tool, you’re also going to get more stickiness from that perspective as well.”
A SaaS product that lives in its own window is fragile. A SaaS product that lives inside a customer’s existing workflow platform is embedded.
The third diagnostic is user breadth: how many different roles inside an enterprise are actively using the product. Broad adoption across a customer organization creates switching costs that a single-function tool never achieves.
CEOs who have all three signals — daily use, ERP integration, and multi-user adoption inside the enterprise — have confirmed product-market fit and have earned the right to fund sales aggressively. Those who have one or two signals are still in the product phase.
How CEOs Scale Themselves When Moving from a Functional Role to the Full Seat
Krantz spent years leading revenue functions at SAP, C3 AI, and PeopleSoft before stepping into CEO roles at Data.ai and then Interos AI. The behavioral shift required at the CEO level was not strategic — it was communicative.
The hardest habit to unlearn from a revenue background is the instinct to own the narrative in any room. In a sales or CRO role, that instinct is an asset. In the CEO seat, it is a tax on every other leader in the organization. Krantz attributes the shift to what Daniel Kahneman calls “thinking slow” — in Kahneman’s Thinking, Fast and Slow — deliberately decelerating judgment while gaining depth across unfamiliar functions: “That thinking slow element and adaptation to these other new cross-functional areas where you have to get a lot deeper is probably the biggest shift.”
The practical discipline Krantz applied: be last to speak in any cross-functional discussion while ramping into a new domain. Glenn Gow’s framing of this is precise — speaking first as CEO locks the room into your frame before anyone has offered a different one. Krantz identifies this discipline as the direct behavioral consequence of moving from a revenue function, where commanding the narrative is how deals close, to the CEO seat, where the same behavior closes off input the organization hasn’t offered yet. Speaking last means the CEO synthesizes what the room has produced rather than overriding it.
The second relearning for revenue-background CEOs is how to build a value proposition without brand leverage. At SAP, Krantz experienced the power of an enterprise logo that opens CFO conversations without cold effort: “You have the SAP logo. Wow, what a massive advantage and access for CFOs and an assumptive position of command and mastery of the ERP landscape. That’s what you have coming in to the discussion.” Moving from that environment to earlier-stage companies required a full reset. Without the SAP logo, Krantz describes the same CFO conversation as requiring evidence, outside-in analysis, and what he calls a “challenger disposition” — studying the customer’s annual reports, understanding their gaps, and arriving with a recommendation they did not already have. CEOs who carry revenue-function habits from large-logo companies into scaling-stage companies will continue to sell on brand and relationship — an approach that fails when the logo does not open the door.
The third shift is product depth. Krantz is explicit: “You simply cannot be a generalist at this level.” Krantz credits Tom Siebel at C3 AI with demonstrating what happens when revenue leaders get close to the product: they become harder to compete with, because they connect product decisions to revenue outcomes without requiring a handoff across functions. Krantz applied that model — building product fluency at Data.ai before moving to Interos AI — rather than delegating product ownership and remaining at the revenue-leader’s distance he had operated from in prior roles.
How CEOs Should Deploy AI Across Operations Right Now
Krantz runs AI deployments across every function at Interos AI — including corporate development, market research, competitive intelligence, recruiting, engineering, and BDR. His view is that agentic AI is applicable to virtually every domain, and he is not waiting for consistency across tools before deploying: “I’ve got it all over the place. We’re using some Microsoft tools. We’re using Anthropic, right? I might be using Grok on the competitive intelligence. They all have their strengths and weaknesses for different use cases.”
This is a deliberate portfolio approach. Krantz does not bet on a single model. He matches the tool to the task and treats the landscape as a continuing experiment, noting that OpenAI, Google, Grok, and Anthropic are all competing actively and none has locked up dominance. His rule: “There’s not going to be 20 winners in this” — so CEOs must pay attention to which platforms are building sustainable capability, not just which model won last quarter’s benchmark.
Two functions get his most aggressive AI investment right now.
The first is BDR and outbound sales development. The old model — high headcount, cold calling, unsolicited proposals — does not match how enterprise buyers engage with SaaS vendors today. Krantz’s view is that the BDR function “is ripe for the taking” through AI automation, and that product virality is replacing cold outreach as the primary top-of-funnel mechanism. CEOs who still fund large BDR headcount for a product that does not yet have viral pull are paying for a mechanism that is losing effectiveness.
The second is engineering. Krantz sees production-quality code generation as the highest-leverage AI application for scaling SaaS companies: “I think whoever masters and figures out how to get that to production quote code and high quality fastest will be an ultimate winner because it’s such a massive scale advantage.” Engineering throughput is the constraint for most scaling SaaS companies. AI that meaningfully compresses the time from spec to production-quality code compounds faster than almost any other operational investment.
| Function | AI Application | Why It Scales |
| BDR / Sales Development | AI-driven outreach, sequence personalization, prospecting | Replaces volume headcount with targeted, consistent outreach at lower cost |
| Engineering | Code generation to production quality | Compresses development cycles; compounds as model quality improves |
| Competitive Intelligence | LLM-based research synthesis (Grok, Anthropic, others) | Keeps CEO-level analysis current without analyst headcount |
| Corporate Development | Market research, diligence preparation | Surfaces insights from public data faster than traditional research methods |
The Scaling CEO’s Toolkit: Named Principles from Ted Krantz at Interos AI
| Principle | What it means in practice | Named evidence from this interview |
| Confirm stickiness before funding sales | Verify daily active use, ERP integration, and multi-user adoption before increasing sales headcount | At both Data.ai and Interos AI, Krantz held the investment mix between product, G&A, and sales and marketing until usage signals confirmed retention — preventing the spend pattern where sales wins contracts the product cannot keep |
| Be last to speak while ramping cross-functionally | In domains outside your functional background, gather before you guide — speaking first as CEO eliminates input the room hasn’t offered yet | Krantz identifies this as the specific behavioral correction he applied when moving from revenue leadership at SAP, C3 AI, and PeopleSoft to the full CEO seat — where speaking first had been how deals closed, and the same instinct became a tax on every other leader |
| Earn the right without the logo | At scaling companies without enterprise brand leverage, the value proposition must be built from outside-in customer intelligence, not relationship and brand assumption | Krantz describes the SAP logo as delivering “assumptive command and mastery” in CFO conversations; moving to C3 AI and later Data.ai removed that access entirely, requiring him to rebuild the value proposition from challenger analysis of the customer’s own annual reports |
| Build a portfolio of AI tools, not a single-model bet | Match LLM to task; treat the competitive landscape as continuously unstable and test accordingly | Krantz runs Anthropic, Microsoft, and Grok simultaneously at Interos AI for different operational functions, explicitly rejecting single-model commitment while the platform competition remains unresolved |
| Prioritize BDR and engineering for maximum AI leverage | These two functions carry the highest throughput potential from AI because they are volume-intensive and quality-constrained | Krantz calls engineering the ultimate winner category — fastest to production-quality code wins the scale race — while flagging the BDR function as “ripe for the taking” through AI automation as enterprise buyer behavior shifts away from cold outreach |
Quotes from This Episode
- “I do think it’s often overlooked that you have partial product market fit and you assume that you have full product market fit. That’s a very different dynamic.” — Ted Krantz, CEO, Interos AI
- “The ERP systems right where I came from, the PeopleSofts, the SAPs, even Bill McDermott, now at ServiceNow. These are very low churn rate investments that have end-to-end workflow functionality.” — Ted Krantz, CEO, Interos AI
- “You simply cannot be a generalist at this level.” — Ted Krantz, CEO, Interos AI
- “I think whoever masters and figures out how to get that to production quote code and high quality fastest will be an ultimate winner because it’s such a massive scale advantage.” — Ted Krantz, CEO, Interos AI
- “There’s not going to be 20 winners in this.” — Ted Krantz, CEO, Interos AI
Frequently Asked Questions
How does a CEO know when they have real product-market fit in a SaaS company?
Real product-market fit in SaaS requires three concurrent signals, not one: daily active use (not monthly or weekly), deep integration with existing ERP or workflow platforms the customer already depends on, and broad user adoption across multiple roles inside the enterprise. Krantz, CEO of Interos AI, argues that CEOs who act on one signal — such as strong initial contract volume — while missing the others are operating on partial product-market fit, which will not hold under aggressive sales investment. The confirmation test is whether the product is embedded in customers’ daily operational routines or sits as a tool they visit occasionally.
When should a scaling SaaS CEO invest heavily in sales headcount?
A scaling SaaS CEO should fund sales headcount aggressively only after confirming that the product has achieved daily active use, is integrated into customers’ existing systems, and is being used across multiple buyer roles inside enterprise accounts. Before those signals are confirmed, sales investment wins contracts the product cannot retain. Krantz’s framework, applied at both Data.ai and Interos AI, is to get the investment mix between product, G&A, and sales and marketing right first — optimizing unit economics before stepping on the go-to-market accelerator.
How should a CEO without a product background develop enough product fluency to lead a scaling SaaS company?
CEOs from revenue or sales backgrounds must prioritize product fluency as a non-negotiable leadership requirement at the scaling stage, not a nice-to-have. Krantz, who built his career in enterprise sales at SAP, C3 AI, and PeopleSoft before becoming CEO at Data.ai and Interos AI, identifies getting close to the product as the thing he learned from Tom Siebel at C3 that most revenue-background leaders skip. The practical approach is to be last to speak in cross-functional product discussions while actively ramping understanding — not delegating product decisions but building enough context to connect them to revenue outcomes directly.
CEOs Work with Glenn Gow to Scale Their Companies and Careers
Glenn Gow is The Scaling Executive Coach — he coaches ambitious executives into the CEO seat and CEOs into successful exits. With 25 years as a CEO and 5 years in venture capital, Glenn helps leaders scale their companies by scaling themselves first. If this conversation was useful, you can apply for executive coaching with Glenn Gow or apply to be a guest on The Scaling Executive Podcast.
