Maai Services Group: Our Five-Phase Framework for Driving Portfolio Value The AI Paradox: Broad Adoption, Minimal Impact

Artificial intelligence is now a board-level priority.

Global AI investment is on track to reach $200B by 2025, with McKinsey estimating that generative AI alone could contribute $4.4 trillion annually to the global economy.

83% of companies list AI as a priority, and 78% report deploying it in at least one business function.

Yet only 1% of executives describe their AI programs as “mature”—meaning AI is fully embedded into workflows and consistently producing measurable results.

This gap between adoption and impact—The AI Paradox—is the defining challenge of AI in business today. In too many organizations, AI sits at the edges: a collection of disconnected tools, isolated pilots, and individual experiments.

The issue is rarely the technology itself. Since 2022, over 346,000 AI tools have entered the market. Without a coordinated strategy, companies make reactive, tactical purchases. The result: tool sprawl, fragmented data, inconsistent adoption, and processes that look much the same as they did before.

For private equity and lower middle market (LMM) leaders, this is both a threat and an opportunity: • Threat: Portfolio companies remain inefficient, dependent on manual processes, and vulnerable to key-person risk—limiting margin expansion and value creation. • Opportunity: Firms that can systematically advance a company from fragmented experimentation to AI maturity can unlock measurable efficiency, resilience, and valuation premiums at exit. The Maai Five-Phase AI Maturity Framework was built for exactly this purpose: a prescriptive, investor-aligned roadmap for transforming operational performance through disciplined, phased AI integration. The Maai Five-Phase AI Maturity Framework

This framework is designed to be both diagnostic and prescriptive.

It provides a clear picture of where an organization stands, defines measurable “readiness gates” for progression, and ensures each step delivers concrete business value.

For investors and operators, it can be applied across the investment lifecycle:

• Due diligence: Assess operational readiness and value-creation potential. • First 100 days: Prioritize AI initiatives that drive immediate efficiency. • Value creation plan: Scale proven solutions across the portfolio. • Exit preparation: Demonstrate operational sophistication and resilience to buyers.

We illustrate the framework using two representative examples:

• Ficta Capital Partners (FCP): 40-person PE firm with $1.2B AUM. • AlphaCo Manufacturing: $75M revenue portco targeting a premium exit in 30 months. Phase 1: Pre-AI (Curiosity & Chaos) Mindset: Awareness of inefficiency as a value lever Characteristics: Spreadsheet-driven processes, knowledge trapped in individuals, reactive IT Impact: Long cycle times, error-prone workflows, high key-person dependency Readiness Gate: At least one high-volume, repetitive process documented with baseline time, cost, and error metrics

At this stage, organizations operate in “spreadsheet sprawl.” Knowledge is stored in people’s heads, and processes stall when key staff are unavailable.

FCP manages deal pipelines through manual file transfers and weekly reconciliation calls. AlphaCo’s AP team manually processes 2,000 invoices per month with a 3% error rate.

Risks: Process debt is invisible until revealed in diligence—or when a key employee leaves. Next Step: Conduct a process inventory, quantify inefficiencies, and identify high-ROI automation candidates. This builds a baseline for both operational improvement and valuation conversations. Phase 2: Chat-Augmented (The Illusion of Progress) Mindset: Individual productivity gains without enterprise control Characteristics: Ad hoc use of public LLMs (ChatGPT, Claude, etc.) Impact: Faster drafting and research, but increased security and compliance risks Readiness Gate: 30%+ of staff using chat tools weekly and requesting standardized prompts/SOPs

Here, employees see personal time savings but workflows remain unchanged. Sensitive data risks increase due to lack of governance.

FCP drafts an NBO cover letter with GPT-4—saving time but exposing client data.

AlphaCo’s operations lead uses Claude to solve a production issue, but the solution is never logged in the official system.

Risks: Lost institutional knowledge, IP exposure, and inconsistent outputs. Next Step: Deploy a vetted prompt library, implement formal data security policies, whitelist approved endpoints, and track usage as a precursor to structured tool adoption. Phase 3: Point Tools (Islands of Apps) Mindset: Function-level optimization Characteristics: Department-specific AI tools Impact: Efficiency gains within departments, but data silos and unclear ROI Readiness Gate: Leadership demands unified ROI tracking and a central source of truth

Organizations invest in specialized AI-powered SaaS tools for functions like legal, marketing, and finance.

FCP uses Emma Legal, Otter.ai, and Writer.com—valuable individually but not connected.

AlphaCo deploys vision-based QC, but results require manual transfer to MES.

Risks: License sprawl, duplication, context-switch fatigue, and no enterprise-level view of value creation. Next Step: Require all tools to feed a centralized data warehouse, consolidate overlapping licenses, and develop KPI dashboards to measure adoption, time savings, and quality improvements. Phase 4: Connected Workflows (Process Intelligence) Mindset: Enterprise orchestration Characteristics: API-linked workflows automating multi-step, cross-functional processes Impact: 30–60% enterprise-wide time savings, higher quality, immutable audit trails Readiness Gate: Three or more automated workflows with 3x+ ROI

Here, siloed tools are replaced by integrated, event-driven processes. Work moves through the organization without manual handoffs, and performance metrics update in real time.

At FCP, incoming contracts trigger Emma Legal clause extraction → V7 Labs risk tagging → CRM integration → LLM-generated risk summaries. Analyst time drops from 45 minutes to 8.

At AlphaCo, IoT data streams into Snowflake, AutoML predicts machine drift, and maintenance tickets are auto-generated in ServiceNow—reducing downtime by 22%.

Value: Documented, automated workflows materially increase operational resilience and can add 0.5–1.0x to EBITDA multiples at exit. Phase 5: AI Employees (The Autonomous Enterprise) Mindset: Operational autonomy Characteristics: Multi-agent systems executing end-to-end workflows with human oversight for exceptions Impact: Human teams focus on strategy, exceptions, and innovation Readiness Gate: Staff transition from executing to reviewing processes

AI agents now function as digital employees, initiating and completing complex workflows without manual triggers.

At FCP, an agent reviews nightly flash reports, flags covenant risks, drafts lender updates, and queues them for partner review.

At AlphaCo, a “Virtual COO” agent monitors demand signals, optimizes production schedules, manages inventory, and issues daily shift plans.

Value: Structural margin improvement, scalability, and premium valuations. Requirements: Implement an LLM orchestration framework, re-skill staff as AI Product Owners, and adopt advanced governance (role-based access, cryptographic verification, anomaly detection). Four Cross-Phase Pillars for Sustainable AI Maturity 1. Data Governance: Clean, accessible, and unified data to power AI reliably. 2. Talent & Culture: Upskilled teams capable of adopting and improving AI systems. 3. Ethical AI: Built-in governance to protect trust and mitigate risk. 4. Leadership & Strategy: Active C-suite ownership to align AI with enterprise value goals. The ROI of AI Maturity • Productivity: Free high-value talent for strategic work. • Process Automation: Shorten cycle times, reduce errors, and lower costs. • Valuation Impact: Mature AI operations can increase EBITDA multiples by 0.5–1.0x. Conclusion: Moving From Experimentation to Enterprise Value

The majority of companies stall in the early phases of AI adoption—generating isolated gains without enterprise-level impact. For investors and operators, this is the moment to act.

Advancing through the Maai Five-Phase AI Maturity Framework is not about collecting tools—it is about executing a disciplined roadmap that links AI investment directly to operational performance and valuation growth.

At Maai Services Group, we partner with private equity firms, their portfolio companies, and LMM leaders to accelerate this journey—moving businesses from fragmented experimentation to fully integrated, autonomous operations that command a premium at exit. Next Step: Schedule a discovery call to determine your organization’s current AI maturity phase, quantify near-term ROI opportunities, and chart a clear, phase-by-phase plan to full maturity.