Cost-Out With AI: Automating SG&A and Back-Office in Portcos

Table of Contents

1. Why SG&A Automation Matters Now
2. The Cost-Out Math: Where AI Delivers Real Payback
3. Finance Operations: Invoice-to-Cash and Expense Automation
4. Customer Support Automation Without Gutting Service
5. Procurement and Vendor Management at Scale
6. The Implementation Roadmap: 12 Weeks to Payback
7. Governance, Risk and Compliance in Automated Operations
8. Common Pitfalls and How to Avoid Them
9. Measuring Success: KPIs That Matter
10. Next Steps: Starting Your Cost-Out Program

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Why SG&A Automation Matters Now

Selling, General and Administrative (SG&A) costs are the untapped lever in portco value creation. Most PE-backed companies carry bloated back offices inherited from their legacy operations or built during growth phases when headcount was cheap and efficiency was optional. Finance teams manually code invoices. Support teams repeat the same answers 100 times a day. Procurement still uses email and spreadsheets. These aren’t edge cases—they’re the norm.

The cost-out opportunity is real. Research from The Hackett Group shows that generative AI can drive profound reductions in SG&A cost and staffing, with potential cost cuts of 30–40% in finance, HR, and procurement functions. But the real win isn’t just headcount reduction. It’s shifting your team from task execution to exception handling, strategy, and growth. You keep your best people. You cut the grunt work.

For PE firms and their portfolio companies, SG&A automation is a repeatable playbook. Deploy it once, learn the pattern, roll it across the portfolio. A $100M revenue portco with 15% SG&A spend ($15M annually) can realistically cut $4–6M in the first 18 months—with payback on implementation in 8–12 weeks.

The barrier to entry is no longer technology. Large language models (LLMs) are commodity. Agentic AI frameworks are open-source or cheap. The barrier is operational: knowing where to start, building the business case, and executing without breaking the business. That’s what this guide covers.

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The Cost-Out Math: Where AI Delivers Real Payback

The Unit Economics of Back-Office Automation

Before you build anything, you need the math. Back-office automation payback is straightforward:

Payback Period = (Implementation Cost) / (Monthly Cost Savings)

For a typical $100M revenue company:

• Finance operations (AP/AR, expense, reconciliation): 3–5 FTE at $80–120K all-in. Monthly cost: $20–50K. Agentic automation handles 60–80% of routine work. Savings: $12–40K/month. Implementation cost: $50–150K. Payback: 4–8 weeks.

• Customer support (first-line, tier-1 routing, FAQ): 5–8 FTE at $50–80K all-in. Monthly cost: $20–50K. AI-first support handles 40–60% of inbound volume. Savings: $8–30K/month. Implementation cost: $40–100K. Payback: 6–12 weeks.

• Procurement (RFQ, vendor communication, contract review): 2–3 FTE at $90–140K all-in. Monthly cost: $15–35K. Agentic workflows handle 50–70% of routine sourcing and negotiation. Savings: $7–25K/month. Implementation cost: $60–120K. Payback: 8–16 weeks.

Combined across three functions, a mid-market portco saves $27–95K/month with $150–370K in implementation cost. Blended payback: 6–12 weeks. After payback, you’re running $300K–$1.1M in annual savings with no incremental headcount.

Why These Numbers Hold

These aren’t theoretical. McKinsey’s guidance on generative AI in the back office emphasises that the highest-ROI use cases are high-volume, repetitive, rule-based work with clear success metrics. Finance operations, support, and procurement are textbook examples.

The payback holds because:

1. You’re not replacing people; you’re automating tasks. A finance analyst spends 30–40% of their week on invoice coding, expense categorisation, and reconciliation. Automation removes that 30–40%, freeing them for analysis, forecasting, and process improvement.

2. Implementation is fast. You’re not building from scratch. You’re integrating with existing ERPs (SAP, NetSuite, Oracle), CRMs, and support platforms. A 12-week engagement covers discovery, pilot, and rollout.

3. The tools are proven. LLMs can reliably extract invoice data, classify expenses, route support tickets, and draft RFQs. Failure rates are low. Hallucination is manageable with guardrails.

4. Compliance is built in. When you automate with audit trails, versioning, and approval workflows, you actually improve control. Most companies’ manual processes are less compliant than automated ones.

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Finance Operations: Invoice-to-Cash and Expense Automation

The Finance Ops Baseline

Finance operations in a typical portco look like this:

• Accounts Payable: Invoices arrive via email, PDF, or portal. Someone opens each one, matches it to a PO, codes it to GL accounts, and routes it for approval. 50–100 invoices/day per FTE. Error rate: 5–10%. Cycle time: 10–15 days.

• Accounts Receivable: Invoices go out. Someone chases payment 30, 60, 90 days later via email. They reconcile bank deposits to AR subledger. DSO drifts to 45–60 days. Bad debt provisions creep up.

• Expenses: Employees submit receipts via Expensify or email. Finance codes them, matches to project codes, and processes reimbursement. Cycle time: 2–4 weeks. Compliance is spotty.

• Reconciliation: Month-end is chaos. AP/AR teams spend 5–10 days matching sub-ledgers to GL, chasing missing invoices, and investigating variances.

This is where agentic automation delivers.

Agentic Invoice Processing

Deploy an AI agent that:

1. Ingests invoices from email, portals, and OCR scans.
2. Extracts data (vendor, amount, line items, PO, dates) with 98%+ accuracy.
3. Matches to POs in your ERP and flags 3-way mismatches.
4. Codes GL accounts based on vendor, amount, and historical patterns. Learns from finance team corrections.
5. Routes for approval based on amount, vendor, and approval matrix.
6. Posts to GL automatically for approved invoices.
7. Flags exceptions (missing PO, amount variance, new vendor) for human review.

Result: 80–90% of invoices process without human touch. Finance team reviews exceptions (10–20% of volume) and handles complex cases. Cycle time drops from 10–15 days to 2–3 days. Error rate falls to <1%. One FTE handles 2–3× the volume.

Expense and Reimbursement Automation

Deploy an agent that:

1. Ingests expense reports from Expensify, Concur, or email.
2. Extracts receipt data (merchant, amount, date, category).
3. Codes to GL and cost centre based on employee, merchant, and policy rules.
4. Flags policy violations (out-of-policy vendors, amounts, dates).
5. Routes for approval based on amount and approver matrix.
6. Processes reimbursement automatically for approved reports.
7. Reconciles to bank and matches to expense sub-ledger.

Result: Expenses process in 3–5 days instead of 2–4 weeks. Employees get reimbursed faster. Compliance improves because the agent enforces policy consistently. Finance team shrinks by 20–30% in the expense function.

Month-End Reconciliation and Close Automation

Deploy an agent that:

1. Pulls GL balances from your ERP (SAP, NetSuite, Oracle).
2. Pulls sub-ledger balances from AP, AR, and payroll systems.
3. Reconciles automatically using historical matching rules and variance analysis.
4. Flags variances >$5K or >5% for investigation.
5. Drafts reconciliation memos explaining variances and proposing adjustments.
6. Suggests accruals for unbilled services, accrued expenses, and cut-offs based on historical patterns.

Result: Month-end close compresses from 10–15 days to 5–7 days. Finance team spends time on substantive close work (reserves, write-offs, forecasting) instead of reconciliation grunt work. One senior person can oversee what used to take 3–4 people.

AR and Collections Automation

Deploy an agent that:

1. Monitors AR aging in real time.
2. Drafts and sends collection emails to customers with overdue invoices, personalised by payment history.
3. Logs customer responses and escalates based on rules (e.g., >90 days = manager escalation).
4. Reconciles bank deposits to AR subledger automatically.
5. Flags unusual patterns (partial payments, payment delays, new disputes).

Result: DSO improves by 5–10 days. Collections team shrinks by 20–30%. Bad debt provisions fall because you catch issues earlier.

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Customer Support Automation Without Gutting Service

The Support Ops Problem

Customer support is a cost centre that directly impacts retention and NPS. Most companies automate support badly—chatbots that frustrate customers and deflect to humans after three turns. That’s not cost-out; that’s brand damage.

Done right, agentic support automation improves both cost and quality. Here’s how.

Tier-1 Automation: FAQ, Troubleshooting, and Account Queries

Deploy an AI agent that:

1. Ingests your knowledge base: FAQs, troubleshooting guides, account documentation.
2. Understands customer intent from email or chat: “How do I reset my password?” vs. “I can’t log in and I have a deadline.”
3. Drafts an answer with the right tone and specificity. For FAQ questions, it answers directly. For complex issues, it gathers context and escalates.
4. Handles handoff gracefully: “I’m escalating you to Sarah in support, who specialises in this. She’ll follow up within 2 hours.”
5. Logs the interaction for analytics and training.

Result: 50–70% of inbound support volume is handled without a human. Tier-1 team shrinks by 40–50%. Customers with simple issues get answers in minutes, not hours. Your best support people focus on complex, high-value issues where they actually move the needle on retention.

Tier-2 Automation: Context Retrieval and Escalation

Deploy an agent that:

1. Retrieves customer context from your CRM, billing system, and support history.
2. Drafts personalised responses that reference the customer’s account, usage, and history.
3. Escalates intelligently to the right specialist (billing, technical, success) based on issue type and customer segment.
4. Tracks SLA compliance and flags breaches before they happen.

Result: Tier-2 agents spend 30–40% less time gathering context and can handle 20–30% more volume. First-contact resolution improves because the agent pre-loads relevant information.

Proactive Support: Prediction and Outreach

Deploy an agent that:

1. Monitors usage patterns in your product.
2. Predicts churn risk based on declining engagement, feature adoption, or support ticket volume.
3. Drafts proactive outreach to at-risk customers: “We noticed you haven’t used feature X in 30 days. Here’s how it can help you…”
4. Suggests upsells to customers who are hitting usage limits or feature gaps.

Result: Churn falls by 5–10%. Revenue per customer grows. Support team becomes a revenue driver, not just a cost centre.

Support Quality and Compliance

When you automate support, you actually improve quality metrics:

• Consistency: The agent gives the same answer every time. No more variance based on who’s on shift.
• Tone: You can tune the agent to match your brand voice and culture.
• Compliance: All interactions are logged with timestamps and content. Audit trails are automatic.
• Training: Every interaction is a training signal. The agent learns from corrections.

The risk: over-automation. If your agent escalates too early or too late, you waste value. The fix is tuning. Start with a 70/30 rule: the agent handles 70% of issues, escalates 30%. Measure CSAT on both buckets. Adjust the threshold. Over 8–12 weeks, you’ll find the sweet spot (typically 50–70% handled, 30–50% escalated).

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Procurement and Vendor Management at Scale

The Procurement Baseline

Procurement in a typical portco is a bottleneck:

• RFQ process: Someone writes an RFQ email, sends it to 3–5 vendors, waits for responses (5–10 days), manually compares quotes, and negotiates.
• Vendor communication: Ongoing email threads about delivery, pricing, contract terms, and SLAs.
• Contract review: Legal and procurement review contracts, negotiate terms, and file them.
• Vendor onboarding: Multiple systems (ERP, insurance portal, tax forms, bank details). Manual data entry.
• Invoice matching: Invoices arrive. Someone matches them to PO, contract, and receipt. Disputes are common.

This process takes 4–8 weeks for a routine vendor onboarding. Complex deals take 12+ weeks. Cost: 2–3 FTE.

Agentic RFQ and Vendor Selection

Deploy an agent that:

1. Ingests your RFQ template and procurement rules (e.g., “always get 3 quotes”, “must be ISO 9001 certified”).
2. Drafts an RFQ based on the request: quantity, specifications, delivery date, payment terms.
3. Sends RFQs to your approved vendor list (or new vendors if needed).
4. Tracks responses and chases non-respondents automatically.
5. Extracts quote data (price, delivery, terms, certifications) from vendor emails and PDFs.
6. Compares quotes side-by-side, calculating total cost of ownership (including delivery, taxes, payment terms).
7. Recommends the best vendor based on your rules (lowest cost, fastest delivery, best terms, etc.).
8. Drafts a negotiation email if the top quote is above budget: “Your quote is 10% above our target. Can you improve delivery or terms?”

Result: RFQ cycle compresses from 4–8 weeks to 1–2 weeks. Procurement team shrinks by 30–40%. You get better pricing because you’re comparing more options and negotiating systematically.

Contract and Vendor Onboarding Automation

Deploy an agent that:

1. Ingests your contract templates (NDA, MSA, SLA, pricing addendum).
2. Drafts a contract based on the vendor, product, and terms.
3. Flags non-standard terms (payment terms, liability, IP ownership, termination) for legal review.
4. Sends the contract to the vendor and tracks signature status.
5. Onboards the vendor once signed: creates ERP master record, sends to insurance portal, requests tax forms, sets up bank details, configures payment terms.
6. Sets up alerts for renewal dates, price escalations, and SLA reviews.

Result: Vendor onboarding compresses from 4–8 weeks to 1–2 weeks. Legal and procurement spend time on exceptions, not routine processing. Vendor data is clean and consistent across systems.

Spend Analytics and Cost Reduction

Deploy an agent that:

1. Aggregates spend data from your ERP, credit cards, and invoicing systems.
2. Identifies duplicate vendors (same company, different names).
3. Finds consolidation opportunities (e.g., you have 5 logistics vendors; could you use 2?).
4. Benchmarks your spend against market data and suggests better vendors.
5. Identifies maverick spend (off-contract purchases) and suggests alternatives.
6. Calculates savings opportunities and drafts business cases for renegotiation.

Result: Procurement team identifies $500K–$2M in savings opportunities. You consolidate vendors, improve terms, and reduce maverick spend. Payback is immediate.

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The Implementation Roadmap: 12 Weeks to Payback

Phase 1: Discovery and Scoping (Weeks 1–2)

Objective: Understand your baseline, identify the highest-ROI use cases, and build the business case.

Activities:

1. Process mapping: Document current state for finance ops, support, and procurement. Measure volume, cycle time, cost, and error rate.
2. Bottleneck identification: Where are the manual handoffs? Where do people wait? Where do errors happen?
3. Cost analysis: Calculate the cost per transaction for each process. Finance ops: cost per invoice, cost per expense, cost per reconciliation. Support: cost per ticket, cost per escalation. Procurement: cost per RFQ, cost per vendor onboarded.
4. ROI calculation: Estimate automation potential (% of volume that can be automated), cost savings, and payback period.
5. Stakeholder alignment: Get buy-in from finance, support, and procurement leaders. Identify champions and blockers.

Deliverable: A 10–15 page scoping document with process maps, cost analysis, ROI projections, and a phased roadmap.

Phase 2: Pilot Design (Weeks 3–4)

Objective: Design and build a pilot for the highest-ROI use case. Typically, this is finance operations (AP or expenses).

Activities:

1. Use case refinement: Define the exact scope. Example: “Automate invoice coding and approval for invoices <$10K from approved vendors.”
2. Data preparation: Extract sample data from your ERP, accounting system, and email. Clean and label it for model training.
3. Integration planning: Map your ERP, CRM, and support platform to the agent. Identify API endpoints and authentication.
4. Agent design: Define the agent’s workflow, decision trees, and escalation rules.
5. Guardrails and safety: Define what the agent can and cannot do. Example: “Agent can approve invoices <$5K. Invoices $5–10K go to manager. Invoices >$10K go to CFO.”

Deliverable: A technical design document with agent workflow, integration diagrams, and test cases.

Phase 3: Build and Test (Weeks 5–8)

Objective: Build the agent, integrate it with your systems, and validate accuracy and performance.

Activities:

1. Agent development: Build the LLM-based agent with your chosen framework (LangChain, LlamaIndex, Anthropic, OpenAI). Integrate with your ERP and support systems.
2. Testing: Run the agent on historical data. Measure accuracy (% of invoices correctly coded, % of support tickets correctly routed, % of RFQs correctly drafted).
3. Tuning: Adjust prompts, guardrails, and decision rules based on test results. Target >95% accuracy on routine cases, <5% escalation rate on exceptions.
4. User training: Train finance, support, and procurement teams on the new workflow. Show them what the agent does, what they need to review, and how to provide feedback.
5. Parallel run: Run the agent alongside the current process for 2–4 weeks. Compare outputs. Measure CSAT and error rates.

Deliverable: A tested agent with >95% accuracy, integration with your ERP, and trained users.

Phase 4: Rollout and Optimisation (Weeks 9–12)

Objective: Deploy the agent to production and optimise for cost and quality.

Activities:

1. Go-live: Switch from parallel run to production. Monitor closely for the first week. Have a rollback plan.
2. Monitoring: Track key metrics: volume processed, accuracy, escalation rate, cycle time, cost per transaction.
3. Optimisation: Adjust the agent based on real-world performance. Example: if escalation rate is 15% instead of 5%, investigate why and retrain.
4. Expansion: Once the first use case is stable, roll out to the next function (support or procurement).
5. Change management: Help users adjust to the new workflow. Address concerns. Celebrate wins.

Deliverable: A production agent handling 60–80% of volume, with documented metrics and a roadmap for the next phases.

Timeline and Effort

• Total duration: 12 weeks.
• Team size: 2–3 engineers, 1 product manager, 1 business analyst, plus 20–30% of your finance/support/procurement team.
• Cost: $150–370K (varies by scope and complexity).
• Payback: 6–12 weeks after go-live.

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Governance, Risk and Compliance in Automated Operations

The Compliance Imperative

When you automate finance, support, and procurement, you’re automating regulated work. You need governance. But here’s the good news: automation actually improves compliance compared to manual processes.

Why?

1. Audit trails: Every decision is logged with a timestamp, user, and rationale. You can trace any transaction back to the original input.
2. Consistency: The agent applies the same rules every time. No variance based on who’s on shift or what mood they’re in.
3. Control: You can set guardrails (e.g., “agent can approve invoices <$5K; must escalate >$10K”) that are enforced automatically.
4. Compliance checks: The agent can validate every transaction against your policies and regulations before posting.

Finance Compliance

For finance operations, focus on:

• GL coding accuracy: The agent must code invoices to the correct GL account. Audit by sampling 10% of transactions monthly. Target: >99% accuracy.
• Approval workflows: The agent must route invoices through the correct approval chain based on amount and vendor. Audit by verifying that all approvals are logged.
• Segregation of duties: The agent must not be able to both approve and post invoices. Build this into the workflow.
• Reconciliation: The agent must reconcile AP sub-ledger to GL monthly. A human must review and sign off.
• Documentation: Keep records of agent decisions, corrections, and escalations for audit purposes.

When you’re ready to pursue SOC 2 or ISO 27001 compliance, PADISO’s security audit service can guide you through the process via Vanta, ensuring that your automated processes meet audit standards.

Support Compliance

For customer support, focus on:

• Data privacy: The agent must not expose customer data in responses. Audit by reviewing escalations and complaints.
• Tone and brand: The agent must match your brand voice and avoid offensive or discriminatory language. Review a sample of agent-generated responses monthly.
• SLA compliance: The agent must route tickets to meet SLA targets. Track SLA compliance weekly.
• Escalation quality: When the agent escalates to a human, it must provide full context. Audit by measuring time-to-resolution and CSAT for escalations.

Procurement Compliance

For procurement, focus on:

• Vendor approval: The agent must only source from approved vendors (unless explicitly authorised to find new vendors). Audit by reviewing new vendor onboardings monthly.
• Contract terms: The agent must not agree to non-standard terms without approval. Define what “standard” means and build guardrails.
• Conflict of interest: The agent must not source from vendors with conflicts of interest. Maintain a conflict-of-interest database and check before sourcing.
• Pricing integrity: The agent must not accept inflated quotes without escalation. Set pricing thresholds and escalation rules.

Risk Management and Guardrails

Deploy guardrails that prevent the agent from making high-risk decisions:

1. Amount thresholds: Agent can approve invoices <$5K, must escalate $5–10K, must escalate >$10K.
2. Vendor whitelists: Agent can only source from pre-approved vendors unless explicitly authorised.
3. Policy enforcement: Agent must enforce your company policies (e.g., “no single-vendor sourcing”, “must get 3 quotes for >$50K spend”).
4. Escalation rules: Define when the agent must escalate to a human. Examples: unusual vendor, unusual amount, policy violation, customer complaint, etc.
5. Monitoring and alerting: Set up alerts for high-risk transactions (e.g., invoices >$100K, invoices from new vendors, invoices with policy violations).

Audit Readiness

If you’re pursuing SOC 2 or ISO 27001 compliance, automation actually helps. Document:

1. Agent design and decision logic: How does the agent make decisions? What rules does it follow? What guardrails are in place?
2. Testing and validation: How did you validate that the agent works correctly? What error rates are acceptable?
3. Monitoring and alerting: How do you monitor the agent’s performance? What alerts do you have in place?
4. Incident response: If the agent makes a mistake, how do you catch it and fix it?
5. Change management: How do you update the agent’s rules and logic? Who approves changes?

These controls are easier to document with automation than with manual processes. When your auditor asks, “How do you ensure invoice coding accuracy?” you can show them the agent’s decision logic, test results, and audit logs. That’s a lot more credible than “our finance team is careful.”

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Common Pitfalls and How to Avoid Them

Pitfall 1: Automating the Wrong Process

The problem: You automate a process that’s already efficient or has low volume. You spend $100K to save $5K/year.

The fix: Start with discovery. Measure volume, cycle time, cost, and error rate for every candidate process. Rank by ROI. Start with the highest-ROI process. For most companies, that’s invoice processing or first-line support.

Pitfall 2: Over-Automating and Losing Quality

The problem: You automate 100% of support tickets, but your CSAT drops because customers get frustrated with the bot.

The fix: Start conservative. Automate 50–60% of volume. Measure CSAT and quality metrics. Expand gradually. For support, start with FAQ and troubleshooting. Escalate complex issues to humans. For finance, start with invoices <$5K from approved vendors. Escalate everything else.

Pitfall 3: Ignoring Integration Complexity

The problem: You build a great agent, but it can’t integrate with your ERP. You end up with manual data entry, defeating the purpose.

The fix: In the design phase, map your systems and APIs. Identify integration points early. Allocate 20–30% of implementation effort to integration. Have your IT team involved from day one.

Pitfall 4: Poor Change Management

The problem: You deploy the agent, but your finance team doesn’t trust it. They do the work manually anyway. No cost savings.

The fix: Involve your team early. Show them the agent in action. Address concerns. Train them on the new workflow. Celebrate wins. Start with a pilot with volunteers, not a mandate. Build trust before scaling.

Pitfall 5: Ignoring Accuracy and Hallucination

The problem: The agent makes mistakes. It codes invoices to the wrong GL account. It escalates legitimate tickets to the wrong department. It drafts RFQs with wrong specifications.

The fix: Test thoroughly before go-live. Aim for >95% accuracy on routine cases. Build in human review for high-risk transactions (e.g., invoices >$10K, escalations to VIP customers). Monitor accuracy post-go-live and retrain the agent if accuracy drops.

Pitfall 6: Underestimating Training and Support

The problem: You deploy the agent, but your team doesn’t know how to use it. Adoption is low.

The fix: Allocate 10–15% of implementation effort to training and change management. Train your team before go-live. Have a support person on call for the first 2–4 weeks. Create documentation and FAQs. Celebrate wins and share metrics.

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Measuring Success: KPIs That Matter

Finance Operations KPIs

• Volume processed: # of invoices, expenses, or reconciliations processed by the agent per month.
• Accuracy: % of transactions that are correctly coded, approved, and posted without human correction.
• Cycle time: Days from invoice receipt to posting (target: 2–3 days vs. current 10–15 days).
• Cost per transaction: Cost to process one invoice or expense (target: 50–70% reduction).
• Escalation rate: % of transactions escalated to humans (target: 10–20%).
• Headcount: FTE required to process the same volume (target: 30–50% reduction).
• Error rate: % of transactions with errors that require correction (target: <1%).

Support Operations KPIs

• Volume handled: # of tickets resolved by the agent per month.
• First-contact resolution: % of tickets resolved without escalation (target: 50–70%).
• Escalation quality: % of escalations with full context provided (target: >90%).
• CSAT: Customer satisfaction score (target: maintain or improve).
• NPS: Net Promoter Score (target: maintain or improve).
• Cycle time: Hours from ticket receipt to resolution (target: 50% reduction for agent-handled tickets).
• Cost per ticket: Cost to resolve one ticket (target: 40–60% reduction).
• Headcount: FTE required to handle the same volume (target: 30–50% reduction).

Procurement KPIs

• RFQ cycle time: Days from RFQ to vendor selection (target: 1–2 weeks vs. current 4–8 weeks).
• Quote comparison: # of quotes obtained per RFQ (target: increase from 2–3 to 3–5).
• Vendor onboarding time: Days from contract signature to first invoice (target: 1–2 weeks vs. current 4–8 weeks).
• Cost savings: $ saved through better negotiation and consolidation (target: 5–15% of spend).
• Maverick spend: % of off-contract purchases (target: reduce from 10–20% to <5%).
• Headcount: FTE required to manage the same vendor base (target: 30–40% reduction).
• Compliance: % of spend from approved vendors (target: >95%).

Financial Impact

For a $100M revenue company with 15% SG&A spend ($15M annually):

• Finance ops: 3–5 FTE × $100K = $300–500K cost. Automate 70% → save $210–350K/year.
• Support: 5–8 FTE × $70K = $350–560K cost. Automate 60% → save $210–336K/year.
• Procurement: 2–3 FTE × $120K = $240–360K cost. Automate 50% → save $120–180K/year. Plus 5–10% spend reduction = $750K–1.5M/year.

Total annual savings: $1.3–2.4M. Implementation cost: $150–370K. Payback: 6–12 weeks. ROI: 300–800% in year one.

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Next Steps: Starting Your Cost-Out Program

For PE Firms and Portfolio Companies

If you’re running a cost-out initiative across your portfolio, here’s the playbook:

1. Identify the portfolio company with the highest SG&A spend and the most manual processes. Start there. Success in one company makes the next one faster.

2. Run a discovery sprint (1–2 weeks). Map finance ops, support, and procurement. Measure volume, cycle time, cost, and error rate. Calculate ROI for each function.

3. Pick the highest-ROI use case. Usually, it’s invoice processing or first-line support. Commit 12 weeks and $150–250K.

4. Build and deploy. Follow the 12-week roadmap above. Measure everything. Celebrate wins.

5. Replicate. Once you’ve proven the model in one company, roll it out across your portfolio. Each subsequent company should be 20–30% faster and cheaper because you’ve learned the playbook.

6. Measure portfolio impact. Track total savings, FTE reduction, and cycle time improvements across all companies. Report to your LPs.

For Operators at Mid-Market and Enterprise Companies

If you’re modernising your operations with agentic AI, here’s the approach:

1. Audit your current state. What’s your finance ops cycle time? What’s your support CSAT? What’s your procurement cost per transaction? Measure baseline metrics.

2. Identify the highest-impact use case. For most companies, it’s finance ops (invoice processing) or support (first-line triage). Start there.

3. Build the business case. Calculate the cost of the current process, the potential savings from automation, and the payback period. Get CFO and COO buy-in.

4. Run a pilot. Implement one use case in 12 weeks. Measure results. If the ROI is there, expand to the next use case.

5. Scale systematically. Once you’ve proven the model, roll out to other functions (procurement, HR, legal, etc.). Build a centre of excellence for agentic automation.

For Founders and Non-Technical Leaders

If you’re building a startup or scaling a company without a technical team, here’s what you need to know:

1. Agentic automation is not science fiction. It’s real, it’s proven, and it’s affordable. Large language models are commodity. The barrier to entry is operational, not technical.

2. You don’t need to build it yourself. You can partner with a venture studio or AI agency to design, build, and deploy the agent. PADISO’s AI & Agents Automation service can help you identify the highest-ROI use cases and implement them in 12 weeks.

3. Start with discovery. Before you commit to building anything, run a 2-week discovery sprint. Map your processes, measure costs, and calculate ROI. PADISO’s AI Quickstart Audit is a fixed-fee 2-week diagnostic that tells you where you actually are, what to ship first, and what 90 days could unlock.

4. Get a fractional CTO or AI advisor. If you don’t have technical expertise on your team, hire a fractional CTO or AI advisor. They can guide you through the decision-making process, help you evaluate vendors, and oversee implementation. PADISO offers fractional CTO and AI advisory services in Sydney and other major cities.

5. Focus on outcomes, not technology. Don’t get distracted by which LLM to use or which framework to build with. Focus on the business outcome: cost reduction, time-to-ship, or quality improvement. The technology is a means to that end.

Getting Started

If you’re ready to explore cost-out with agentic AI, here are your next steps:

1. Book a 30-minute call with a PADISO AI advisor. They’ll ask about your current processes, pain points, and cost structure. They’ll identify the highest-ROI use cases and give you a rough timeline and budget.

2. Run a discovery sprint. If the initial call is promising, invest 1–2 weeks in a detailed discovery. Map your processes, measure costs, and build a business case. This costs $5–10K but saves you from investing in the wrong thing.

3. Build the business case. Calculate the cost of the current process, the potential savings from automation, and the payback period. Get stakeholder buy-in from your CFO, COO, and relevant team leads.

4. Commit to a 12-week pilot. Pick one use case (usually finance ops or support). Allocate $150–250K and 12 weeks. Measure results. If the ROI is there, expand.

The cost-out opportunity is real. The technology is proven. The barrier is execution. If you’re ready to move, reach out to PADISO today.

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Conclusion

SG&A automation with agentic AI is the most predictable value-creation lever in the PE playbook. It’s not about replacing people; it’s about eliminating grunt work and freeing your team to do high-value work. The math is straightforward: 6–12 week payback, 30–40% cost reduction, and improved quality and compliance.

The companies that win are the ones that move fast. Start with discovery. Identify the highest-ROI use case. Build and deploy in 12 weeks. Measure everything. Replicate across your portfolio. By the time your competitors are still debating whether to automate, you’ll have already cut $1–2M in SG&A costs and improved your operations.

The future of SG&A is automated. The question is: will you lead it or follow it?

Based in Sydney, PADISO has built agentic automation for finance ops, support, and procurement across 50+ companies. We know the playbook. We know the pitfalls. We know how to execute. If you’re ready to cost-out your SG&A, let’s talk. Book a call with our team today.