Run the Numbers — “How a CFO Budgets for Forward Deployed Engineers”

Host: CJ Gustafson
Guest: Varsha Uday Ubanu (SVP of Finance, Invisible Technologies)
Date: February 19, 2026

Episode Overview

This episode demystifies the financial and operational playbook behind deploying forward deployed engineers (FDEs) in innovative AI and data labeling companies serving enterprises. Varsha Uday Ubanu, seasoned finance leader at Invisible Technologies, illuminates how budgeting, value-based pricing, and the human + AI dynamic underpin the delivery of bespoke AI solutions. The conversation dives deep into evolving SaaS metrics, structuring multi-threaded sales strategies, negotiating brand's role before profitability, and learning from past mistakes across tech, ad tech, and B2B AI.

Key Discussion Points & Insights

1. What Is a Forward Deployed Engineer (FDE), and Why Do They Matter?

• FDEs Defined: Not just professional services—FDEs are deeply embedded technical experts tuned to each customer's unique workflows and systems. Every enterprise client is effectively a “custom build.”

• “You need somebody to go sit, understand, explain explicitly, and build a solution for them using the modules that you have available that answers their specific use case...and that is what the forward deploy engineering motion is.”
  — Varsha, [07:53]

• FDEs drive outcomes, not merely capabilities. Their work is “n of 1” problem solving, fine-tuning models and processes to each client’s idiosyncrasies.

2. Enterprise AI Is All About Trust and Outcomes

• Rather than selling features, the key is delivering real, validated results—often before any contract is signed (through solution sprints).

• “The thing that we’re selling is we’re selling trust...you do it before a contract sometimes. If we can convince [enterprise customers] that we will deliver, we will be that partner...we know we’re set for multiple years.”
  — Varsha, [16:31]

• Early technical investment in a singular client is outweighed by the long-term opportunity size, as successful deployments often lead to years of expansion.

3. Value-Based & Predictable Pricing Models

• Invisible’s approach is highly tailored: pricing based on the unique value for each enterprise, hinging on context, data challenges, and “predictability.”

• “...at the end of the day the main thing you have to remember is, enterprises want value...But buying predictability is how I think about it.”
  — Varsha, [18:50]

• Value is rarely immediate or easily attributable—often tied to risk mitigation, cost savings, or prevented failures, not just incremental revenue.

• Flat fees are common to start, with more outcome-based or metered models evolving as measurement improves.

4. Sales & Pipeline as a Portfolio of Bets

• Revenue in this model isn’t a traditional funnel, but a “portfolio of bets.” Multiple parallel pilots, with disciplined up-front time-boxing (e.g., 8-week solution sprints).
• Optionality is powerful, but “unmanaged optionality is just burn”—discipline is key.
• “Multi-threading is helpful...[but] optionality with no discipline is just burn.”
  — Varsha, [23:46]

5. Resourcing, Incentivizing & Staffing Human Expertise

• Human annotation remains critical as AI deployment nears real-world decision points (esp. tech, healthcare, legal, finance).

• Payscales are set by market-clearing rates for expertise; more specialized skills command higher rates but often yield significantly higher quality.

• “The closer AI gets to a real decision, the more valuable the human expertise becomes.”
  — Varsha, [30:46]

• Invisible’s operational “muscle” is lightning-fast sourcing of domain experts globally, certificating their expertise as needed.

6. Key Metrics: Beyond ARR and Traditional SaaS Lenses

• Classic SaaS metrics like ARR/NRR don’t neatly apply; momentum, use case expansion, and customer value realization are greater barometers of success.

• “For us, momentum is very much use case expansion: at what rate are we able to land use cases and at what rate are we expanding?”
  — Varsha, [38:06]

• Focus is on input metrics that predict compounding value or decay—not just lagging output measures.

7. Headcount, Planning & the New P&L

• Traditional sales resource planning (e.g., AE quotas) is upended; account growth is driven more by technical resources (e.g., the ratio of ARR to FDEs).
• Forward planning orbits around expansion within accounts versus net-new logos.
• “It’s become how many clients are you at and what is your expansion potential within each one of them? That is the most important atomic unit.”
  — Varsha, [41:15]

8. Brand as a Strategic Lever, Not a Luxury

• Even pre-profitability, investing in brand is a must: it reduces friction across sales, procurement, and lowers overall CAC and risk.

• “[Brand] actually reduces friction across sales...from a finance perspective, honestly, it’s lowering my CAC, it’s lowering my risk. These aren’t soft metrics.”
  — Varsha, [43:57]

• Confidence and trust are paramount in enterprise AI, often standing in for features or hype.

9. Ad Tech Lessons & The Threat of Commoditization

• Understand and build organizational memory/differentiation to avoid commoditization, as seen in surveillance from the ad tech world.
• “Markets can commoditize. It’s a very hot industry today and tomorrow it’s commoditized...focus on the value that you’re adding and the differentiation you’re bringing.”
  — Varsha, [47:10]

Memorable Moments & Quotes

• “We sell on the art of the possible, we deliver on the art of reality.”
  — Varsha Uday Ubanu, [22:24]
• “Cash is king...Cash tells you how much time you actually have to fix your mistakes.”
  — Varsha, [51:10]
• “Yesterday’s price is not today’s price.”
  — Varsha, [01:01]
• “If you needed advice from a heart surgeon and they were going to charge you $50 an hour, you’re probably going to get what you pay for.”
  — CJ, [32:04]
• “Brand matters even before revenue and profitability are mature.”
  — CJ, [43:32]

Timestamps for Important Segments

| Timestamp | Topic / Segment | |-----------|-----------------| | 00:00–07:38 | The rise and role of forward deployed engineers (FDEs) | | 10:09–12:34 | Outcome-driven sales and solution sprints | | 16:31–18:09 | Selling trust & enterprise account expansion | | 18:50–22:18 | Value-based, predictable pricing in practice | | 23:46–25:25 | Pipeline as “portfolio of bets”; discipline in multi-threading | | 30:46–33:13 | Incentivizing/pay structure for human experts | | 34:10–38:06 | Rethinking metrics — momentum over ARR | | 39:23–41:15 | The evolving enterprise AI P&L; headcount planning | | 42:19–45:29 | Brand’s primacy in B2B AI; ad tech parallels | | 47:10–48:31 | Commoditization risk and the need for differentiation | | 51:10–52:00 | Cash flow wisdom and finance lessons |

Additional Insights & Advice

• Annual Planning: Metrics and planning cycles must adjust to the expansion-driven nature of enterprise AI; new revenue is a smaller piece than mining and compounding existing agreements.
• Tooling: Standard finance tools (NetSuite, Data Rails, Numeriq, Ramp for expense management), but discipline and expertise wielding them matter more than tooling alone.
• Expense Memes: Funniest expense claim? A Hinge dating app subscription!
  — Varsha, [52:57]

Listener Takeaways

• Trust & Outcomes are the product: In complex, high-touch AI deployments, the sale is as much about future delivery as present features.
• Financial discipline in experimentation: Time-boxing projects and refusing to drink the “Kool-Aid” for too long are lessons learned the hard way.
• Brand matters earlier than you think: Investing in it is not optional in enterprise AI—it’s an economic advantage, not a cost center.
• Metrics are being reinvented: Input and momentum metrics are better than output ones for a true read on business health.
• Commoditization is inevitable without clear differentiation: Build organizational knowledge and unique processes, not just products.

This summary preserves the crisp, candid, jargon-rich tone of startup finance leaders—helping ambitious listeners discern not just “what” to do, but “why” and “how” at the bleeding edge of tech finance.