Operationalizing AI - Part 2: Evaluating AI Across the Business

Where Finance Leads—Even Outside Finance

You don’t have to be an expert in every AI tool, but your role is to bring financial rigor to how these tools are evaluated and adopted. As a strategic partner, you help the business pressure-test assumptions, understand cost structures, and scale AI investments responsibly.

While Part 1 focused on finance operations, this section outlines where AI is gaining traction in other departments, and how finance leaders can guide smarter, more disciplined integration company-wide.

1. Product Development

AI is showing up in product roadmaps everywhere. Some features strengthen retention or pricing power, while others quietly burn through API credits without moving the needle.

Your role: 

Finance should be at the table early—shaping the conversation around pricing, packaging, and cost. If a feature relies on third-party LLMs with significant API fees, there should be a credible path to monetization. That’s not a product-only decision. It’s a financial one.

Questions to ask:

• Are we building toward a differentiated offering or reacting to market noise?
• Will this feature drive adoption, expansion, or product stickiness?
• ​​What customer behavior signals enough value to justify charging for this feature?
• At what point do variable costs start to erode margin?
• Does our billing system support the pricing model we need?

What to watch for:

• Usage-based expenses that grow faster than revenue
• Added functionality with minimal impact 
• Features shipped for competitive optics only

💡 If it doesn’t generate revenue or meet baseline market expectations, it’s overhead.

‍

2. Go-To-Market Strategy

AI is quickly becoming standard in sales and marketing, from lead scoring to outbound personalization. If accountability is lacking, these tools may inflate activity but fail to improve outcomes.

Your role:

You don’t own every GTM decision, but staying close to how sales and marketing are using AI helps you spot what’s actually working. When finance is looped in, it’s easier to connect those investments back to CAC, payback, and pipeline quality.

Questions to ask:

• Are AI tools improving CAC, payback period, and conversion rates?
• Is pipeline accuracy increasing with predictive insights?
• Are content and outreach platforms introducing any compliance risks?

What to watch for:

• Rising GTM spend with flat or worsening funnel conversion
• Inflated pipeline metrics from poorly trained models
• Shadow subscriptions bypassing procurement

💡 Activity isn’t the metric. Results are. Look for higher conversion rates, faster sales cycles, or a lower CAC.

‍

3. Internal Operations

AI is showing up in internal systems—automating help desks, routing tickets, flagging anomalies, and stitching workflows together. Lacking oversight, these deployments can lead to more complexity and bloat.

Your role:

Finance may not use each system directly, but it’s still your responsibility to evaluate the returns. Productivity gains should be weighed against the total cost of implementation, operational resilience, and measurable results.

Questions to ask:

• Are response times or support burdens improving in quantifiable ways?
• What’s the total cost per automation (including usage-based pricing, integrations, and support?)
• Are we reinforcing stable processes, or scaling dysfunction?
• Who’s tracking performance, and how often is it reviewed?

What to watch for:

• Automation that increases effort but not output
• Complex builds that solve narrow, low-impact challenges
• Recurring charges for systems with unclear goals

💡 Improvements should be measurable and durable, not theoretical or cosmetic.

‍

4. Workforce Planning

AI is redefining roles, consolidating functions, and shifting how work gets done. But headcount reductions alone don’t lead to efficiency. Automation only creates leverage when supported by intentional org design and thoughtful training.

Your role:

Model the full investment in AI-driven productivity—including tools, training, and oversight—and evaluate the downstream effects on team capacity and org structure. Focus on long-term effectiveness, not short-term savings.

Questions to ask:

• Are these new systems eliminating manual work or just moving it elsewhere?
• How does the cost of automation compare to the fully-loaded cost of FTEs?
• Are we upskilling existing employees or relying on new hires with AI-specific skills?

What to watch for:

• Workforce reductions before AI proves out
• Expensive platforms with low usage or ambiguous ownership
• Broken processes masked by technology
• Insufficient onboarding and change management

💡 Efficiency gains come from smarter systems and empowered teams, not cost-cutting in isolation.

‍

As a finance leader, your role is to steer AI adoption toward the most impactful areas, bring oversight to how tools are used, and ensure outputs stay grounded in reality. The goal isn’t speed for its own sake—it’s faster time-to-value, actionable recommendations, and fewer blind spots.

‍

Your Cross-Functional Advantage

AI experimentation is happening fast. You’re not leading every initiative, but you’re in the room for most of them, and that gives you a perspective few others share.

Use it. Your vantage point allows you to connect the dots and identify duplicative spend, misaligned priorities, and high-effort initiatives with low return. More than anyone, you can guide the company in scaling AI in ways that preserve margin and deliver measurable value.

‍What's Next in This Series

Part 1: From Number Crunching to Strategic Command covered how AI is redefining the work of the finance team, and what changes for you as a leader.

Part 2: Evaluating AI Across the Business explored how to assess AI investments in product, GTM, and operations through a financial lens.

Coming soon - Part 3: Leading AI Governance will focus on why finance should lead AI governance, and how to build the right controls for scale.

With this no-nonsense playbook, you’ll know how to lead your company through AI adoption with financial clarity, operational control, and strategic focus.

Download the playbook in full

‍

Billing and Revenue Recognition—Built for SaaS CFOs

As AI transforms finance, clean and scalable billing infrastructure is essential. Subscript extracts sales terms directly from your contracts, automates invoicing, and manages follow-ups, so revenue stays on track and nothing slips through the cracks.

🔎 Ready to see Subscript in action? Schedule a demo today.

‍

‍