The AI Value-Creation Plan: A Template for Deal Teams

Table of Contents

1. Why AI Value-Creation Plans Matter
2. The Core Framework: Four Pillars
3. Pillar One: AI-Driven Revenue Acceleration
4. Pillar Two: Cost Reduction via Automation
5. Pillar Three: Operational Efficiency and Speed
6. Pillar Four: Risk Mitigation and Compliance
7. Building the Initiative Roadmap
8. Assigning Owners and Accountability
9. EBITDA Targets and Financial Modelling
10. Milestones, Tracking, and Deal Room Governance
11. Common Pitfalls and How to Avoid Them
12. Implementation Roadmap for Your Team

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Why AI Value-Creation Plans Matter

Private equity deal teams have spent the last three years watching artificial intelligence reshape business models. McKinsey’s research on the economic potential of generative AI shows that generative AI alone could add trillions to global economic output. But for deal teams, the real question is sharper: How do we bake AI into the investment thesis, measure it, and hit it?

A traditional value-creation plan focuses on revenue uplift, cost cuts, and bolt-on acquisitions. An AI value-creation plan goes further. It identifies where agentic AI, workflow automation, and AI-native platform re-architecting can unlock value that competitors cannot replicate quickly. It assigns owners. It ties initiatives to EBITDA targets. It builds milestones into the hold period.

Without a structured AI value-creation plan, deal teams often end up with:

• Scattered AI experiments that consume budget but don’t move the needle on EBITDA
• No clear owner for AI initiatives, leaving them orphaned between the CTO and the CFO
• Vague timelines that slip because AI projects lack concrete acceptance criteria
• Missed compliance gates that derail enterprise sales when SOC 2 or ISO 27001 audits fail
• Technology debt that compounds when AI is bolted onto legacy systems instead of baked into platform strategy

This guide provides a template that deal teams can adapt in their first 100 days post-close. It covers the four pillars of AI value creation, shows how to structure initiatives, and walks through financial modelling and milestone tracking.

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The Core Framework: Four Pillars

An effective AI value-creation plan rests on four pillars. Not every portfolio company will activate all four equally, but the framework helps you identify which ones matter most for your thesis.

Pillar One: Revenue Acceleration

AI can expand addressable market, improve win rates, and unlock new use cases. Revenue uplift is often the highest-impact lever but also the hardest to model conservatively.

Pillar Two: Cost Reduction

Automation, process mining, and agentic workflows cut operational costs. Unlike revenue initiatives, cost cuts are measurable and often achievable within 6–12 months.

Pillar Three: Operational Efficiency

AI reduces time-to-market, shortens sales cycles, and improves product velocity. These don’t hit EBITDA directly but enable revenue and cost initiatives.

Pillar Four: Risk Mitigation

AI-native compliance frameworks, security audit readiness, and governance reduce downside risk and unlock enterprise deals. This pillar is often overlooked but critical for regulated industries.

Bain’s research on generative AI in private equity emphasises that PE firms applying AI systematically across sourcing, diligence, and portfolio value creation are outpacing peers. The difference is structure. A template forces rigour.

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Pillar One: AI-Driven Revenue Acceleration

Revenue uplift from AI typically falls into three categories: expanding the customer base, increasing customer lifetime value, and opening new product lines.

Expanding the Customer Base with AI-Native Sales

AI can accelerate sales cycles and improve qualification. For example, an agentic AI system that handles inbound lead qualification, product demos, and contract negotiation can reduce sales cycle time by 30–50%. This doesn’t change the total addressable market, but it compresses the time-to-revenue and improves sales team productivity.

Concrete example: A B2B SaaS company with a 90-day sales cycle and 50-person sales team could use AI agents to qualify and demo to 2–3x more prospects. If your model assumes a 20% productivity uplift across the team, that translates to 10 additional closed deals per quarter at your average contract value (ACV). At $100k ACV, that’s $4M in incremental annual revenue.

Increasing Customer Lifetime Value with AI-Driven Upsell

AI can identify upsell and cross-sell opportunities faster than humans. By analysing customer usage patterns, support tickets, and product telemetry, an AI system can flag which customers are ready for a higher tier or complementary product. This reduces sales friction and improves expansion revenue.

Concrete example: A fintech platform with 1,000 customers, $5M net revenue retention (NRR), and 40% gross margin could use AI to identify 20% of the customer base as upsell-ready. If you move those customers from tier 2 to tier 3 (a $50k–$100k annual lift), and 40% convert, that’s $400k–$800k in incremental annual revenue within 6 months.

Unlocking New Product Lines

AI can enable entirely new products that were not economically viable before. For example, a vertical SaaS company might use generative AI to build a customer-facing AI assistant, creating a new revenue stream and deepening customer stickiness.

Concrete example: An insurance broker platform could launch an AI-powered claims assistant that helps customers file claims faster. The assistant reduces support costs by 25% and increases customer satisfaction, which improves retention by 5 percentage points. At $10M revenue and 80% gross margin, a 5-point retention lift is worth $400k–$600k in incremental NPV over the hold period.

Modelling Revenue Uplift Conservatively

When building your AI revenue thesis, use conservative assumptions:

• Adoption lag: Not all customers adopt AI features immediately. Assume 30–50% adoption in year one.
• Willingness to pay: Customers may not pay a premium for AI features. Model a 10–20% price lift, not 50%.
• Competitive response: Competitors will copy your AI moves. Build in a 12–18 month window of competitive advantage.
• Execution risk: AI projects slip. Add a 20% execution risk buffer to timelines.

PwC’s AI in the enterprise resource centre emphasises that organisations deploying AI strategically see revenue uplift, but only when AI is tied to customer outcomes, not just technology novelty. Your revenue plan must answer: “How does this AI initiative make the customer’s business better?”

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Pillar Two: Cost Reduction via Automation

Cost reduction is the most predictable pillar of AI value creation. Agentic AI, workflow automation, and process mining can cut costs across customer support, back-office operations, and content creation.

Customer Support Automation

AI agents can handle 50–70% of customer support tickets, reducing headcount requirements and improving first-contact resolution rates.

Concrete example: A SaaS company with 500 customers, 10 support staff, and $200k annual support cost per FTE could deploy an AI support agent. If the agent handles 60% of tickets and reduces support FTE by 3–4 people, that’s $600k–$800k in annual savings. Implementation takes 8–12 weeks. Payback period: 6–9 months.

Back-Office Process Automation

AI can automate invoice processing, expense approvals, payroll reconciliation, and other high-volume, rules-based tasks. Process mining tools can identify bottlenecks and opportunities for automation.

Concrete example: A mid-market company with $100M revenue and 300 employees spends 2,000 hours annually on invoice processing (20 staff × 100 hours). An AI invoice automation system costs $50k to implement and $30k annually to operate. If it automates 70% of invoices, you save 1,400 hours annually, or $70k in labour cost. Payback: 6 months.

Content and Knowledge Work Automation

Generative AI can draft emails, summarise meetings, generate reports, and create first drafts of content. This doesn’t eliminate headcount but improves productivity by 20–30%.

Concrete example: A professional services firm with 50 consultants billing $300/hour spends 10% of their time on non-billable admin work. If AI reduces non-billable time by 4 percentage points, each consultant gains 80 billable hours annually. At $300/hour, that’s $24k per consultant, or $1.2M for the firm. Implementation cost: $100k. Payback: 1 month.

Modelling Cost Reduction Conservatively

When building your cost thesis:

• Avoid headcount elimination claims. Instead, frame cost savings as FTE reduction through natural attrition and redeployment.
• Account for implementation overhead. AI projects consume engineering and operational time. Budget 3–6 months of 50% effort from a senior operator.
• Model phased rollout. Don’t assume all cost savings hit in month one. Phase automation across departments over 6–12 months.
• Include training and change management. Staff need to learn new workflows. Budget 10–20% of implementation cost for training.

Deloitte’s generative AI guide for busy executives notes that organisations realising cost savings from AI invest heavily in change management and governance. Without it, staff resist automation and projects fail.

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Pillar Three: Operational Efficiency and Speed

Operational efficiency initiatives don’t directly hit EBITDA but enable revenue and cost initiatives. They’re also critical for competitive positioning.

Accelerating Product Development

AI can speed up code generation, testing, and deployment. A platform engineering team using AI-assisted development can ship features 30–40% faster. This doesn’t increase revenue directly, but it lets you hit market windows faster and respond to competition more quickly.

Concrete example: A SaaS company with a 6-month feature roadmap could compress it to 4 months using AI-assisted development. This lets you launch a competitive feature before a rival, protecting market share. The value is defensive (avoided churn) rather than additive (new revenue), but it’s real.

Improving Data Quality and Analytics

AI-powered data pipelines can clean, enrich, and integrate data across systems faster than manual processes. Better data enables better decisions, faster.

Concrete example: A fintech company spends 2 weeks monthly on data reconciliation and cleansing. An AI data platform reduces this to 3 days. The 9 days of freed capacity lets the analytics team build more insights, which improves product decisions and reduces time-to-insight from 2 weeks to 3 days. This compounds over the hold period as the team ships more features, faster.

Strengthening Engineering Velocity

Fractional CTO leadership combined with AI-native architecture can improve engineering velocity by 40–50%. This includes better hiring, clearer technical strategy, and modern tooling.

PADISO’s fractional CTO services help portfolio companies build investor-ready technical strategies and accelerate hiring. A fractional CTO can often identify 2–3 quick wins in the first 30 days that unlock velocity: clearing technical debt, adopting AI-assisted development, or improving deployment frequency.

Modelling Operational Efficiency

Operational efficiency is harder to quantify than cost cuts, so model it conservatively:

• Time savings first, then money. Model how many hours are freed up, then convert to FTE or revenue impact.
• Avoid double-counting. If an AI initiative saves time that lets the team ship features faster, model that as revenue impact in Pillar One, not as cost savings in Pillar Two.
• Build in lead time. Operational improvements take time to compound. Don’t expect full impact until 6–12 months post-implementation.

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Pillar Four: Risk Mitigation and Compliance

Risk mitigation is the most undervalued pillar of AI value creation. Getting compliance right unlocks enterprise deals and reduces downside risk.

SOC 2 and ISO 27001 Audit Readiness

Enterprise customers require SOC 2 Type II or ISO 27001 certification. Many portfolio companies lack these. Implementing a compliance-first architecture and using tools like Vanta can get you audit-ready in 8–12 weeks, not 6–12 months.

Concrete example: A B2B SaaS company with $5M ARR and no SOC 2 certification is losing 20% of enterprise deals to compliance objections. Implementing SOC 2 via a structured audit process costs $50k–$100k and takes 10 weeks. Once certified, you can pursue enterprise customers with $50k–$100k ACVs that were previously off-limits. If you close 5 additional enterprise deals in year one, that’s $250k–$500k in incremental revenue. ROI: 2.5–5x in year one.

PADISO’s security audit service uses Vanta to accelerate compliance. Vanta automates evidence collection and audit preparation, reducing the manual work that typically delays certification.

AI-Native Governance and Risk Management

As you deploy more AI, you need governance frameworks that manage model risk, bias, and regulatory exposure. This is especially critical in regulated industries like financial services, insurance, and healthcare.

Concrete example: A financial services company deploying AI for underwriting needs to implement model governance, bias testing, and explainability frameworks to comply with APRA CPS 234 and ASIC RG 271. Building this governance upfront costs $100k–$200k in consulting and tooling but prevents regulatory breaches that could cost millions. For regulated industries, this is a necessary cost, not optional.

PADISO’s AI advisory for financial services helps Australian banks and fintechs build APRA-compliant AI strategies. Similarly, AI for insurance clients in Australia need conduct risk monitoring and underwriting AI that’s compliant with APRA and LIF requirements.

Data Privacy and GDPR Compliance

If your portfolio company processes customer data across geographies, GDPR and local privacy laws apply. AI amplifies data risk because models are trained on customer data. Building privacy-first architecture upfront prevents costly breaches and regulatory fines.

Concrete example: A B2B SaaS company with European customers processes 100GB of customer data monthly. A GDPR-compliant data architecture costs $50k to implement but prevents a potential €20M fine if a breach occurs. Expected loss avoided: €20M × 5% (probability of breach over 5 years) = €1M. ROI: 20x.

Modelling Risk Mitigation Value

Risk mitigation is often modelled as downside protection, not upside creation. But it unlocks upside by enabling enterprise sales and preventing regulatory fines.

• Enterprise deal unlock: Model how many enterprise deals you lose today due to compliance gaps. Calculate the revenue value of closing those deals post-compliance.
• Regulatory risk reduction: Model the expected loss from a regulatory breach (fine × probability × impact on valuation). Subtract the cost of compliance to get net risk reduction.
• Competitive positioning: Compliance-first positioning is increasingly a competitive advantage. Tier-one customers expect it. Model this as a defensibility premium.

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Building the Initiative Roadmap

Once you’ve identified opportunities across the four pillars, structure them into a roadmap. A good roadmap has three characteristics: sequencing (which initiatives depend on others), ownership (who’s accountable), and milestones (what success looks like).

Sequencing Initiatives

Some initiatives are prerequisites for others. For example:

• Foundation initiatives (Months 0–3): Security audit readiness, technical debt cleanup, data infrastructure improvements. These enable faster execution downstream.
• Quick wins (Months 1–6): Customer support automation, invoice processing automation, sales cycle compression. These build momentum and fund larger initiatives.
• Strategic initiatives (Months 6–18): New product lines, platform re-architecture, enterprise expansion. These require foundation work to be stable.
• Scaling initiatives (Months 12–36): International expansion, adjacent verticals, M&A integration. These leverage the foundation and quick wins.

Sample Initiative Roadmap

Initiative	Pillar	Timeline	Owner	EBITDA Impact	Milestone
Support agent deployment	Cost	Months 2–4	VP Ops	$600k annual savings	60% ticket automation by Month 4
Sales cycle compression	Revenue	Months 1–6	VP Sales	$4M incremental ARR	30% cycle time reduction by Month 6
SOC 2 certification	Risk	Months 1–3	CISO	$250k deal unlock	Audit-ready by Month 3
Platform re-architecture	Efficiency	Months 3–12	CTO	40% velocity uplift	New architecture in production by Month 9
Enterprise sales program	Revenue	Months 6–18	VP Sales	$8M incremental ARR	10 enterprise customers by Month 18
Data platform upgrade	Efficiency	Months 4–9	VP Data	50% analytics cycle time reduction	New platform in production by Month 9

This roadmap shows clear sequencing: foundation work (SOC 2, platform re-architecture, data platform) happens first, enabling quick wins (support agent, sales cycle) and strategic initiatives (enterprise sales) downstream.

PADISO’s platform development services can accelerate platform re-architecture and data infrastructure work. For companies in the Bay Area, platform engineering in San Francisco is available; for companies in Melbourne, platform development in Melbourne serves regulated industries like insurance and retail.

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Assigning Owners and Accountability

The single biggest predictor of AI initiative success is clear ownership. Without an owner, initiatives drift.

Owner Profiles

Each initiative needs an owner with three characteristics:

1. Authority: The owner can make decisions without escalation (within budget).
2. Skin in the game: The owner’s incentives are aligned with the initiative’s success (e.g., bonus tied to EBITDA impact).
3. Bandwidth: The owner has 50%+ capacity allocated to the initiative during the critical path.

Typical Owner Assignments

• Revenue initiatives: VP Sales or VP Product (with CTO as technical partner)
• Cost initiatives: VP Operations or CFO (with CTO as technical partner)
• Efficiency initiatives: CTO or VP Product (with CFO as finance partner)
• Risk initiatives: CISO or Chief Compliance Officer (with CTO as technical partner)

For portfolio companies without a CTO, a fractional CTO can serve as the technical owner. PADISO offers fractional CTO services in Sydney, Melbourne, Brisbane, and the US (San Francisco, New York). A fractional CTO brings investor-ready technical strategy, engineering hiring, and vendor/AI independence to the table.

Accountability Structures

Tie owner compensation to initiative outcomes:

• Revenue initiatives: 20–30% of bonus tied to revenue targets.
• Cost initiatives: 20–30% of bonus tied to EBITDA impact.
• Efficiency initiatives: 10–20% of bonus tied to velocity metrics (e.g., feature deployment frequency).
• Risk initiatives: 10–15% of bonus tied to compliance milestones.

This alignment ensures owners prioritise initiatives that matter to the deal thesis, not just their functional area.

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EBITDA Targets and Financial Modelling

All initiatives roll up to an EBITDA target. A good AI value-creation plan ties each initiative to a specific EBITDA impact and builds a consolidated financial model.

Building the Initiative-Level P&L

For each initiative, build a simple P&L:

Line Item	Year 1	Year 2	Year 3	Hold Period NPV
Revenue impact	$4M	$8M	$12M	$20M
Cost savings	$600k	$800k	$800k	$2M
Implementation cost	($200k)	—	—	($200k)
Operating cost	($100k)	($150k)	($150k)	($400k)
EBITDA impact	$4.3M	$8.65M	$12.65M	$21.4M

This shows how each initiative compounds over the hold period. Year 1 impact is modest (implementation drag), but by Year 3, you’re realising full run-rate benefit.

Consolidating to Portfolio EBITDA

Once you’ve modelled each initiative, consolidate to portfolio EBITDA:

Scenario	Year 0	Year 1	Year 2	Year 3	Exit Multiple	Exit Value
Base case (no AI)	$10M	$10.5M	$11M	$11.5M	8x	$92M
AI case	$10M	$14.3M	$19.65M	$24.15M	9x	$217M
Upside case	$10M	$16M	$23M	$30M	10x	$300M

This shows how AI value creation compounds. In the base case, EBITDA grows 5% annually. In the AI case, it grows 40%+ annually. At exit, the AI case is worth $125M more than the base case. That’s the value-creation thesis.

Stress Testing and Sensitivity Analysis

AI initiatives carry execution risk. Stress-test your model:

• Execution delay: What if initiatives slip 6 months? Model a 50% reduction in Year 1 impact.
• Lower adoption: What if customers adopt AI features at 20% instead of 50%? Model a 60% reduction in revenue impact.
• Competitive response: What if competitors copy your AI moves faster? Model a 12-month window of competitive advantage instead of 24 months.

A robust model shows that even under stress, AI initiatives create meaningful value. If your model breaks under modest stress, the thesis is too aggressive.

BCG’s framework on value creation in private equity emphasises that the best PE firms are disciplined about modelling assumptions and stress-testing. AI is no different. Be conservative, stress-test, and show your work.

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Milestones, Tracking, and Deal Room Governance

A value-creation plan is only as good as its execution. Build governance and tracking into the deal room from day one.

Milestone Framework

For each initiative, define quarterly milestones:

Support Agent Initiative (Example)

Quarter	Milestone	Owner	Success Criteria
Q1	Agent architecture designed and approved	CTO	Design doc reviewed by board
Q2	Agent deployed to 10% of tickets	VP Ops	60% of deployed tickets resolved without escalation
Q3	Agent deployed to 50% of tickets	VP Ops	Support costs down 30%, CSAT maintained
Q4	Agent deployed to 100% of tickets	VP Ops	Support costs down 50%, ticket volume handled +70%

Milestones are specific, measurable, and tied to owners. This forces discipline.

Deal Room Governance

Establish a deal room cadence:

• Weekly ops calls (30 min): Initiative owners report status, blockers, and next week’s priorities. No deep dives.
• Monthly value-creation reviews (90 min): Detailed review of each initiative’s progress against milestones. Discuss risks and course corrections.
• Quarterly board meetings (120 min): Present consolidated progress, EBITDA impact, and updated exit thesis to the board.

This cadence keeps initiatives visible and prevents drift.

Tracking Dashboard

Build a simple dashboard that tracks each initiative’s status:

Initiative	Owner	Q1 Target	Q1 Actual	Status	EBITDA Impact (Annual)	Notes
Support agent	VP Ops	60% automation	55% automation	On track	$600k	Slight delay in training; expect to hit 60% by mid-Q2
Sales cycle	VP Sales	25% reduction	20% reduction	At risk	$4M	Sales team adoption slower than expected; need additional training
SOC 2	CISO	Audit-ready	Audit-ready	Complete	$250k deal unlock	Ahead of schedule; audit scheduled for Month 4

This dashboard is reviewed weekly in ops calls and monthly in value-creation reviews. It keeps the team focused and surfaces issues early.

Red Flags and Course Correction

Define red flags that trigger escalation:

• Milestone miss: If an initiative misses a quarterly milestone by >20%, escalate to the board.
• Owner change: If an initiative loses its owner, reassign immediately and notify the board.
• Budget overrun: If implementation cost exceeds budget by >30%, reassess the initiative’s ROI.
• Adoption lag: If customer adoption is <50% of plan after 6 months, pivot the approach.

When red flags occur, the deal room has 48 hours to develop a course-correction plan. This might mean extending timelines, increasing resources, or deprioritising the initiative.

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Common Pitfalls and How to Avoid Them

We’ve seen hundreds of portfolio companies attempt AI value-creation plans. Here are the most common pitfalls and how to avoid them.

Pitfall 1: Too Many Initiatives, No Prioritisation

Problem: Deal teams identify 10–15 AI initiatives and try to pursue them all simultaneously. The team gets overwhelmed, initiatives slip, and nothing ships.

Solution: Prioritise ruthlessly. In the first 100 days, identify the top 3–5 initiatives that will move the needle on EBITDA. Sequence the rest. A focused roadmap with 3–5 initiatives is better than an unfocused roadmap with 15.

Pitfall 2: Unclear Ownership

Problem: AI initiatives fall between functions. The CTO thinks the VP Sales owns sales cycle compression. The VP Sales thinks the CTO owns the tooling. Neither takes ownership, and the initiative stalls.

Solution: Assign a single owner with authority and skin in the game. Make ownership explicit in writing. Tie compensation to outcomes.

Pitfall 3: Vague Success Criteria

Problem: An initiative’s success is defined as “improve customer satisfaction” or “modernise the platform.” No one knows what done looks like, so the project never ends.

Solution: Define success as specific, measurable outcomes. “Improve support CSAT from 75 to 85” or “reduce support cost per ticket from $50 to $25.” This forces clarity.

Pitfall 4: Ignoring Execution Risk

Problem: The deal team models 50% cost savings from automation but doesn’t account for change management, training, or staff resistance. When the initiative ships, adoption is 30%, and savings are 15%.

Solution: Budget 20–30% of initiative cost for change management and training. Model adoption conservatively (30–50% in year one, 70–80% by year two). Account for execution risk in timelines.

Pitfall 5: Chasing Hype Over Value

Problem: The team pursues an AI initiative because it’s trendy (e.g., “build a ChatGPT-like assistant”) without validating that it creates value. The initiative ships but doesn’t move the needle on revenue or cost.

Solution: Start every initiative with a customer problem, not a technology. Ask: “What customer problem does this solve?” and “How much is it worth?” If you can’t answer both clearly, deprioritise.

Pitfall 6: Forgetting Compliance

Problem: The team builds an AI system that works great but fails a security audit because it wasn’t designed with compliance in mind. Now you have to rebuild it.

Solution: Bake compliance into the architecture from day one. For regulated industries, involve the CISO or Chief Compliance Officer in the design phase. Use compliance-first tools like Vanta to stay audit-ready.

EY’s AI insights emphasise that governance and risk management are foundational to successful AI deployment. Compliance isn’t a post-implementation concern; it’s a design constraint.

Pitfall 7: No Measurement Framework

Problem: Six months into the hold period, the deal team can’t articulate how much value each AI initiative has created. Milestones were hit, but EBITDA didn’t move as expected.

Solution: Build a measurement framework from day one. Define how you’ll measure each initiative’s impact (revenue, cost, EBITDA, customer satisfaction, time-to-ship). Assign someone to track metrics weekly. Review quarterly.

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Implementation Roadmap for Your Team

You now have a framework. Here’s how to implement it in your first 100 days post-close.

Days 1–10: Discovery and Prioritisation

Objective: Identify the top 3–5 AI value-creation initiatives.

Actions:

1. Conduct 15–20 customer interviews to understand pain points. Ask: “What takes too long? What costs too much? Where do you need better data?”
2. Audit current operations. Where is time spent? Where are bottlenecks? Use process mining tools to identify automation opportunities.
3. Map initiatives to EBITDA. For each opportunity, estimate revenue impact, cost savings, and implementation effort.
4. Prioritise. Select 3–5 initiatives that are high-impact and achievable within 6–12 months.

Days 11–30: Planning and Ownership

Objective: Build detailed plans for each initiative and assign owners.

Actions:

1. Develop initiative charters. For each initiative, document: objective, success criteria, timeline, owner, budget, and risks.
2. Assign owners. Meet with each owner to confirm their commitment and align on compensation incentives.
3. Build financial models. For each initiative, model revenue impact, cost savings, and EBITDA impact over the hold period.
4. Consolidate to portfolio EBITDA. Show how each initiative contributes to the overall exit thesis.

For technical initiatives, consider engaging a fractional CTO or AI advisory partner to validate the technical approach and timeline. PADISO’s AI advisory services can help you build a technically sound strategy in your first 30 days.

Days 31–60: Governance and Execution

Objective: Launch initiatives and establish deal room governance.

Actions:

1. Establish deal room cadence. Schedule weekly ops calls and monthly value-creation reviews.
2. Build tracking dashboard. Set up a simple spreadsheet or tool to track each initiative’s progress.
3. Launch quick wins. Start with the initiatives that are highest-impact and lowest-risk. Ship something in the first 60 days to build momentum.
4. Communicate progress. Share weekly updates with the board. Celebrate wins, surface risks early.

Days 61–100: Course Correction and Scaling

Objective: Course-correct based on early results and scale successful initiatives.

Actions:

1. Review early results. Which initiatives are on track? Which are at risk? Why?
2. Course-correct. Adjust timelines, budgets, or ownership as needed. Kill initiatives that aren’t working.
3. Scale quick wins. If an initiative is working, accelerate it. If a support agent is saving 30% of support cost in month two, scale it across all support channels.
4. Prepare for next wave. Identify the next cohort of initiatives to launch in months 4–6.

Ongoing: Measurement and Refinement

Objective: Continuously measure impact and refine the plan.

Actions:

1. Track metrics weekly. Update the dashboard with progress against milestones.
2. Review EBITDA impact monthly. As initiatives ship, measure actual EBITDA impact against plan.
3. Adjust the thesis. If initiatives are outperforming, update your exit thesis upward. If they’re underperforming, adjust downward.
4. Communicate to stakeholders. Share quarterly updates with the board and investors. Show progress, celebrate wins, surface risks.

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Summary and Next Steps

An AI value-creation plan is a structured approach to identifying where AI can create value in a portfolio company, assigning owners, and tracking impact over the hold period. It differs from traditional value-creation plans by explicitly baking AI into the investment thesis and tying initiatives to EBITDA targets and milestones.

The framework rests on four pillars:

1. Revenue Acceleration: AI-driven sales, upsell, and new products.
2. Cost Reduction: Automation and process efficiency.
3. Operational Efficiency: Faster product development and better data.
4. Risk Mitigation: Compliance and governance.

A good plan has clear ownership, specific milestones, and disciplined tracking. It’s conservative in assumptions, stress-tested, and tied to compensation. Most importantly, it’s executable—not a 50-page consulting deck, but a living document that the team uses daily.

Your Next Steps

1. Download the template. Use the initiative roadmap and financial modelling framework provided in this guide as a starting point.
2. Conduct discovery. Spend 1–2 weeks interviewing customers, auditing operations, and identifying opportunities.
3. Prioritise ruthlessly. Select 3–5 initiatives that move the needle on EBITDA.
4. Assign owners and build plans. For each initiative, document objectives, success criteria, timelines, and budgets.
5. Establish governance. Set up weekly ops calls and monthly value-creation reviews. Build a tracking dashboard.
6. Launch and measure. Ship quick wins early, measure impact, and course-correct based on results.

For portfolio companies needing technical support, PADISO can help. Whether you need AI advisory and strategy, fractional CTO leadership, platform engineering, security audit and compliance, or custom software development, we work with deal teams to execute AI value-creation plans that hit EBITDA targets.

For regulated industries, PADISO also offers specialised AI advisory for financial services and insurance in Australia, ensuring your AI initiatives are compliant with APRA, ASIC, AUSTRAC, and LIF requirements.

The teams that win are the ones that move fastest and most disciplined. Use this template to move both.