How to Build the Internal Business Case for Private AI in 30 Minutes
Your CFO does not want a lecture on Mixture-of-Experts architectures. They want to know: what does it cost, what does it save, what breaks if we do nothing, and when can we start?
This is a 30-minute template to answer those four questions. Copy the worksheet. Forward the link to Benchmarks & ROI. Schedule the meeting.
Slide 1: Current state (the problem in one minute)
Adoption is already happening. 67% of employees use AI at work; only 18% of companies have formal AI security policies. 59% use unauthorized tools vs 16% on employer-approved tools.
Spend is already happening. 37% of enterprises spend $250K+/year on LLM APIs; 72% expect increases. Companies plan 1.7% of revenue on AI in 2026, double 2025 (BCG via Mavvrik).
Nobody owns the full picture. Engineering sees API bills. IT sees shadow AI. Legal sees DPAs that do not cover consumer ChatGPT tabs. Finance sees a line item with no ROI model.
One sentence for the slide: We are already paying for AI twice: official API spend and uncontrolled employee usage.
Slide 2: Risk (what inaction costs)
Data leakage. 43% of employees pasted confidential data into AI tools (Cyberhaven 2025). Samsung engineers leaked proprietary code within 20 days of ChatGPT access.
Breach premium. Shadow AI incidents add an estimated $670,000 to average breach costs (IBM data via Vectra).
Regulatory exposure. EU AI Act transparency and enforcement infrastructure activate August 2026. "We didn't know employees used AI" is not a defense.
One sentence: Uncontrolled AI is a security and compliance liability today, not a 2027 problem.
Slide 3: Opportunity (controlled AI pays back)
Productivity is real. OpenAI's State of Enterprise AI 2025 reports workers saving 40–60 minutes per day on average where AI is deployed with governance.
Routing saves money. Production LLM gateways report 40–70% inference cost reduction via task-level routing without quality loss on operational workloads.
Approval beats bans. 89% drop in unauthorized AI usage when approved alternatives exist.
One sentence: Give teams AI that is faster than shadow tools and safer than consumer tabs.
Slide 4: Economics (one worked example)
Use your numbers. Here is a conservative template:
| Line item | Your value | Notes |
|---|---|---|
| Monthly direct LLM spend | $________ | Anthropic + OpenAI + Azure OpenAI |
| Share that is routable (~70%) | $________ | Classification, RAG, drafts, extraction |
| Savings at 40% routing | $________ | Conservative vs RouteLLM / gateway benchmarks |
| Autark platform cost | $________ | From pricing (Autark Flash / Autark / Autark Deep tiers) |
| Net monthly savings | $________ | Before productivity gains |
Example (illustrative):
- $40K/mo direct API spend
- 70% routable = $28K addressable
- 40% savings = $11.2K/mo = ~$134K/year
Compare to Autark at $3/$6 per M tokens on your actual volume. Use the benchmarks page for eval-backed quality claims when procurement pushes back.
Slide 5: Rollout (30 / 60 / 90 days)
Days 1–30: API gateway
- Point staging apps at
https://api.autark.ai/v1(migration guide) - Run 1–5% production traffic on
model: "auto" - Establish dashboard baseline for spend and tokens
Days 31–60: Team pilot
- Roll out approved desktop/work agent for one department (e.g. sales or support)
- Publish AI acceptable-use policy (what data never goes into AI)
- Retire worst shadow-AI paths by offering something better
Days 61–90: Expand and prove ROI
- Expand routing to agent workloads and background jobs
- Present finance with before/after dashboard export
- EU customers: align with AI Act transparency requirements in product UX
One sentence: One API change first, team rollout second, company-wide governance third.
The ask (be specific)
Bad ask: "We need budget for AI."
Good ask: "Approve $X/month for Autark API + Y seats for Autark Work. We project $Z annual savings on inference plus reduced shadow-AI risk. Pilot completes in 90 days with dashboard ROI review."
Attach:
- This memo
- Benchmarks & ROI
- Trust Center for security review
- Contact for enterprise VPC/pricing if needed
Objection cheat sheet
"Can't we just use ChatGPT Enterprise?"
You still need routing for app/agent API spend, EU data controls for regulated data, and a story for non-ChatGPT workloads.
"We'll build it in-house."
Building routing, eval, failover, and compliance infrastructure is a full-time team. That's the gap Autark closes.
"Let's wait for prices to drop."
Prices are dropping on open models. Without a routing layer, you do not capture the savings. Your bill stays tied to whichever flagship you pinned.
The bottom line
The business case for private AI is not futuristic. Employees already use AI. Invoices already arrive. Incidents already happen.
The only question is whether you govern and optimize the pipeline, or pay flagship prices and accept shadow risk while someone else figures it out.
Thirty minutes. Four slides and a worksheet. Forward it today.