This brief is confidential and prepared exclusively for American Industrial Partners senior leadership and operating partners. Contents are intended to support strategic planning and portfolio value creation discussions. Not for distribution.

Executive Summary

American Industrial Partners has spent nearly four decades proving that industrial value creation comes from engineering discipline and operational transformation — not financial engineering. That thesis has delivered a Net IRR exceeding 25% since inception, attracted Blackstone GP Stakes' strategic minority investment in January 2025, and built a portfolio of 29 active platforms generating $28 billion in aggregate annual revenue across 240+ manufacturing and distribution facilities. AIP is, by any measure, one of the most operationally sophisticated industrial investors in global private equity.

AI is the natural next chapter of that story. The same engineering-centric DNA that distinguishes AIP from financially-oriented peers — half its team in operations, 17 of 29 partners from engineering or operating backgrounds, Justin Fish leading an Industry 4.0-focused Operations team — is precisely the organizational substrate that makes industrial AI adoption work at scale. The operating model is in place. The portfolio depth is unmatched. The Blackstone ecosystem now provides procurement leverage and cross-portfolio learning at a scale available to virtually no competitor.

The question is not whether AIP should pursue AI. It's how quickly the Firm moves from individual observations about AI potential to a systematic, portfolio-wide capability.

Key findings from this analysis:

• AIP's engineering-centric team is a structural AI advantage. No large-cap industrial PE firm has half its partnership in operations with 17 of 29 partners from engineering or operating backgrounds. This provides the technical credibility to drive AI adoption on factory floors — something financially-oriented competitors cannot replicate with bolt-on digital consultants.
• The portfolio spans the widest industrial AI deployment surface in PE. Twenty-nine active platforms across 10+ end markets — from additive manufacturing (ADDMAN) to defense logistics (V2X) to aluminum production (Commonwealth Rolled Products) — create more diverse and defensible AI use cases than any single-sector competitor.
• ADDMAN and V2X represent near-term, high-conviction AI anchors. ADDMAN's generative design and process AI can reduce design cycle time by 30–50% and print failures by 20–40%. V2X's $12B+ DoD backlog and defense technology integration capabilities position the company directly in the path of defense AI modernization budgets growing at 20–30% annually.
• Repeatable AI patterns exist across multiple portfolio companies. Predictive maintenance, demand forecasting, route optimization, and process quality AI are not bespoke solutions — they are portfolio-wide capabilities deployable across manufacturing archetypes with standardized playbooks.
• AI-enhanced due diligence could reduce AIP's diligence timeline by 30–60% while improving carve-out complexity modeling — directly supporting faster commitment in competitive processes where AIP's carve-out expertise is its primary differentiation.
• Competitors are moving. Brookfield PE has declared AI its "third value creation pillar." KPS Capital acquired Innomotics to embed Siemens-heritage digitalization capabilities. CORE Industrial Partners invests with an explicit Industry 4.0 thesis. The window to establish AI leadership is open — but it is narrowing.

Competitive Analysis & AI Positioning

Peer Firm Benchmarking

Three-Tier Adoption Framework & AIP's Position

Industrial PE firms currently cluster into three adoption tiers — and the distance between tiers is widening. AIP's engineering-centric team, Operating Agenda infrastructure, and Blackstone ecosystem access position it to advance into the AI-Native tier faster than any financial-operations peer, provided it formalizes an AI practice within the next 6–12 months.

AIP's primary competitive risk comes from two directions: KPS Capital Partners, which acquired Innomotics's Siemens-heritage OT capabilities and operates comparable manufacturing scale ($19.5B AUM, 202 facilities); and Brookfield PE, which has the most mature systematic AI deployment among large-cap industrial investors. CORE Industrial Partners, while smaller, builds AI fluency into its deal thesis from the outset — an approach that attracts AI-native operating talent and technology-forward deal flow that AIP must match to remain competitive for the best industrial assets.

Portfolio Company AI Applications

AIP's 29 active portfolio companies span ten industrial end markets — a breadth that creates more varied AI use cases than virtually any peer. The analysis below is structured by manufacturing archetype rather than individual company, identifying where AI creates the most concentrated EBITDA impact and where patterns repeat across multiple platforms.

Defense & Aerospace

V2X — Defense Mission Solutions

With approximately 14,000 employees, $3.4B+ in revenue, a $12B+ backlog, and operations in 28 countries, V2X is AIP's largest and most strategically significant platform for AI. Formed from the 2022 merger of Vectrus and Vertex, V2X provides mission-critical logistics, base operations, and technology integration to the U.S. DoD and allied governments. Defense AI spending is growing at 20–30% annually, and V2X's incumbent contractor relationships and technology integration capabilities position the company directly in the path of that spending.

The highest-value AI applications at V2X span four domains. Defense logistics AI — optimizing supply chain management, inventory positioning, and spare parts forecasting across military installations worldwide — could reduce logistics costs by 15–25% while improving readiness rates across customer bases. Predictive maintenance for vehicle fleets, aircraft support equipment, and base infrastructure enables condition-based maintenance that extends asset life and reduces unplanned downtime. V2X's recent GMR (Global Mission Relay) contract win positions the company for AI-driven CJADC2 modernization — one of DoD's highest-priority technology programs. And AI-enhanced cybersecurity, aligned with DoD's zero-trust architecture mandates, is table stakes for maintaining cleared facility status and pursuing new defense technology work.

ADDMAN — Additive Manufacturing

ADDMAN is AIP's most technology-forward platform and the portfolio's clearest demonstration that AI and advanced manufacturing are not separate capabilities — they are the same capability. Serving space, motorsport, medical device, and robotics applications, ADDMAN operates at the intersection of physical manufacturing and computational design. AI deployments at comparable additive manufacturing facilities have demonstrated 30–50% reductions in design cycle time, 20–40% reductions in print failures, and 15–25% improvements in material efficiency.

Generative design AI explores thousands of part geometries to identify optimal structures for weight, strength, and manufacturability — reducing design iteration from weeks to hours. Machine learning models that optimize laser power, scan speed, and layer parameters for each material-geometry combination reduce print failures and improve part consistency. Real-time in-process quality monitoring — using AI analysis of melt pool characteristics and thermal signatures — detects defects during printing rather than post-process, eliminating the cost of scrapped parts. For a platform serving aerospace-grade tolerances and medical device standards, this is not incremental improvement; it's a competitive moat.

Ascent Aerospace & Veoneer

Ascent Aerospace's tooling and fixture manufacturing for aerospace OEMs benefits from generative AI in fixture design, reducing engineering cycle time and optimizing material usage for complex geometries. Dimensional compliance AI — computer vision and measurement automation — supports verification of tooling accuracy to aerospace-grade tolerances at scale. Veoneer's advanced driver assistance systems (ADAS) are inherently AI-driven products; the opportunity here is in improving the AI within the product itself (sensor fusion, pedestrian detection, collision avoidance algorithms) as much as in the manufacturing process.

Advanced Manufacturing & Industrial Technology

Current (ex-GE) — Intelligent Lighting & Environments

Current's commercial and industrial LED lighting infrastructure is, at its core, an IoT data collection network. The AI opportunity is in treating that installed base as a platform rather than a product: AI-driven occupancy sensing and environmental optimization, energy consumption prediction and optimization in commercial and industrial facilities, and predictive maintenance for lighting infrastructure that schedules replacements before failure. The EBITDA impact comes from two directions — operational efficiency for Current's own manufacturing, and value-added analytics services that differentiate the product and support pricing power in a commoditizing market.

Heavy Process & Materials Manufacturing

Commonwealth Rolled Products — Aluminum

Commonwealth Rolled Products is a continuous-process manufacturer of flat-rolled aluminum — precisely the manufacturing environment where AI delivers the highest and most predictable ROI. Rolling mill AI models that optimize rolling speed, temperature profiles, and reduction schedules for each alloy specification can yield 1–2% improvements in material efficiency; at Commonwealth's production volumes, that improvement translates directly to millions in annual EBITDA. Surface quality prediction AI — identifying likely surface defects from upstream process variables before they reach final inspection — enables proactive parameter adjustments that reduce scrap and rework. Predictive maintenance of rolling mill bearings, hydraulics, and drives prevents the unplanned shutdowns that are the most expensive disruptions in continuous-process manufacturing.

Pittsburgh Paints Company (ex-PPG)

The Pittsburgh Paints business combines a 125-year brand with the operating complexity of a multi-channel architectural coatings manufacturer serving professional contractor and DIY consumer markets across the U.S. and Canada. AI color formulation — machine learning models that optimize pigment combinations to achieve target color specifications with fewer lab trials — reduces both R&D time and raw material costs. Demand forecasting AI, tuned by geography, channel, season, and color trend, improves inventory management across a product portfolio measured in thousands of SKUs. Retail analytics AI can optimize dealer and channel performance in ways that the prior corporate owner almost certainly did not fully develop, creating margin improvement opportunity that arrives with the business at acquisition.

Austin Powder — Explosives & Blasting

Austin Powder operates in one of the most heavily regulated manufacturing and distribution environments in any AIP portfolio company. Here, AI serves two distinct purposes. Blast optimization AI analyzes geological conditions and project parameters to optimize blast patterns — reducing material usage while improving fragmentation results for mining and construction customers. Safety compliance AI, tracking explosives manufacturing, storage, and transportation against overlapping regulatory frameworks, converts compliance from a labor-intensive manual process into an automated monitoring capability. The latter is not discretionary; it is a risk management imperative for a business with AIP's regulatory exposure profile.

Nexpera & Element 13

Both platforms benefit from the established industrial AI playbook for continuous chemical and metals processing: AI optimization of reaction conditions and catalyst usage in chemical manufacturing (Nexpera), and energy consumption, alloy composition, and casting quality optimization in aluminum production (Element 13). For energy-intensive processes, even modest AI-driven improvements in energy management produce meaningful EBITDA impact given current industrial energy costs.

Natural Resources & Energy

Boart Longyear — Drilling Services & Products

Boart Longyear is a global leader in drilling services for mining and natural resources exploration — a business where AI in the drill string itself is rapidly becoming a competitive differentiator. Machine learning models that analyze geological data and drill bit performance in real time to optimize weight-on-bit, rotation speed, and fluid flow increase penetration rates and extend bit life. Predictive maintenance AI monitoring drill rig vibration and temperature patterns prevents unplanned rig downtime in remote locations where logistics costs are high. The longer-term trajectory for Boart is toward semi-autonomous drilling operations — a capability that reduces labor requirements and improves safety in some of the most hazardous work environments in the portfolio.

Enviva — Wood Pellet Production

Enviva, the world's largest producer of industrial wood pellets, emerged from restructuring with a global supply chain that spans timber procurement, transportation, and utility customer delivery. AI supply chain optimization — coordinating timber procurement across sourcing regions, transportation logistics, and inventory management — directly addresses the margin structure of a business where feedstock cost and logistics are the primary cost drivers. Pellet quality prediction AI, analyzing feedstock properties and drying conditions to optimize energy content and durability, reduces quality variance that affects contract pricing. Energy trading analytics AI can improve the timing and pricing of sales to utility customers in markets where demand varies with weather, carbon credit pricing, and renewable energy policy.

Global Cellulose Fibers (ex-International Paper)

Acquired from International Paper in early 2026 for approximately $1.5 billion, Global Cellulose Fibers enters AIP's portfolio as a continuous-process pulp manufacturer with an established equipment base. Pulp process AI — controlling chemical pulping, washing, and bleaching parameters to maximize yield and quality of absorbent fluff pulp — is among the highest-ROI applications in the portfolio. Predictive maintenance in a continuous 24/7 operation is mandatory rather than aspirational; unplanned shutdowns in a pulp mill carry outsize cost consequences. The AI readiness assessment for Global Cellulose Fibers, conducted during the carve-out Operating Agenda development phase, will determine how quickly these applications can be deployed on the inherited IT/OT infrastructure.

Agriculture & Food Technology

Grain & Protein Technologies (ex-AGCO)

Grain & Protein Technologies manufactures grain storage, handling, drying, and protein production equipment serving global agricultural markets. IoT-connected grain monitoring — AI-powered sensors tracking temperature, moisture, and condition in storage systems — prevents spoilage and optimizes drying energy consumption for equipment customers. Predictive maintenance analytics applied to the installed equipment base creates a recurring aftermarket revenue opportunity that the prior corporate owner had limited incentive to develop as a standalone business. Precision agriculture integration connects Grain & Protein Technologies' infrastructure data with broader farm management platforms, adding analytical value that supports customer retention and cross-sell opportunities. This is an aftermarket AI opportunity as much as a manufacturing AI opportunity.

Aker Qrill Company

Marine ingredient production from krill harvest involves supply variability that AI can help manage. Harvest optimization AI — using marine ecosystem data and satellite imagery to inform harvest operations — improves consistency of feed supply. Processing quality AI controlling krill extraction parameters maximizes nutritional content and product quality in what is a specification-sensitive end market.

Services, Logistics & Distribution

RelaDyne's national lubricant and fuel distribution network is a textbook AI route optimization and demand prediction opportunity — reducing fleet miles and improving delivery reliability across an industrial customer base with predictable replenishment patterns. Brock's industrial scaffolding and maintenance services business benefits from AI workforce scheduling that deploys field labor more efficiently and safety monitoring AI that reduces incident rates on complex industrial job sites. Optimas, distributing fasteners globally, has a demand forecasting and inventory optimization opportunity across thousands of SKUs and a geographically dispersed customer base. SEACOR's maritime logistics platforms benefit from vessel route optimization and fuel consumption prediction. Strike's pipeline and infrastructure construction business can apply AI to project estimation and resource allocation in ways that improve bid accuracy and delivery margin.

Repeatable Patterns Across the Portfolio

While each portfolio company presents unique AI opportunities, six patterns appear across multiple platforms. These are the building blocks of a portfolio-wide AI playbook — solutions that can be developed once at the archetype level and deployed with company-specific calibration across the full portfolio. Formalizing these patterns into standardized Operating Agenda modules, developed centrally by AIP's Operations team, is the most capital-efficient way to generate AI value at the portfolio's scale.

Deploying these six patterns across AIP's 29 platforms — even partially, even in Phase 1 — represents a material and compounding EBITDA improvement opportunity across the portfolio's $28 billion revenue base. The engineering talent to direct this deployment already exists within AIP's Operations team. What the Firm needs is the formal structure, standardized tooling, and cross-portfolio coordination mechanism to convert isolated observations into systematic execution.

Internal Firm AI Applications

The same AI capabilities that drive value at the portfolio company level also apply directly to AIP's own deal sourcing, diligence, and portfolio management operations. For a firm that prides itself on analytical rigor and operational depth, AI integration into the Transactions and Operations teams is not a future initiative — it's a near-term productivity and competitive advantage.

Deal Sourcing & Screening

AIP's deal sourcing advantage rests on two pillars: deep relationships with Fortune 500 corporations that become motivated sellers of non-core industrial assets, and the operational credibility to evaluate and improve complex carve-outs that generalist buyers cannot underwrite. AI reinforces both pillars. Corporate carve-out intelligence systems that continuously scan earnings calls, SEC filings, analyst reports, and news feeds for divestiture signals can enable AIP to approach sellers before assets reach competitive auction processes — preserving the relationship-led advantage that has defined AIP's pipeline for decades.

Turnaround and distress screening — AI analysis of financial, operational, and credit indicators across manufacturing companies to identify candidates meeting AIP's target criteria ($500M+ revenue, $0–$350M EBITDA, U.S./Canada/developed markets focus) — expands the team's effective coverage of the industrial universe without proportional headcount growth. End market intelligence AI, tracking macro trends and regulatory shifts across AIP's ten target end markets, supports sector allocation decisions with faster, more comprehensive data than any manual research process.

AI-Accelerated Due Diligence

AIP's specialty in complex carve-outs from major corporations (GE, PPG, International Paper, AGCO, Domtar, L-3) involves a diligence process that is inherently more complex than standard buyouts. TSA mapping, standalone cost structure modeling, IT separation risk assessment, and Operating Agenda development require significant analytical capacity from a team that must simultaneously manage multiple processes. AI tools for each of these workstreams — document analysis, contract NLP for TSA identification, carve-out complexity scoring, and Operating Agenda generation — directly address AIP's most resource-intensive diligence bottlenecks.

The most differentiating application is an Operating Agenda generator: an AI tool trained on AIP's dataset of 125+ acquisitions that benchmarks target company metrics against prior transformations, identifies analogous historical situations, and generates first-draft Operating Agenda frameworks with estimated improvement trajectories. This does not replace AIP partners' judgment — it gives them a richer starting point, faster, in processes where speed to conviction is a competitive advantage.

Portfolio Monitoring & Early Warning

Managing 29 active portfolio companies across 240+ facilities is a coordination challenge that grows nonlinearly with portfolio size. An AI-powered industrial dashboard — aggregating manufacturing KPIs (OEE, yield, safety incident rates, energy consumption, labor productivity) across all facilities into a unified portfolio view — gives AIP's Operations team real-time visibility that is currently impossible to maintain manually. Anomaly detection models flag early indicators of execution risk across Operating Agenda milestones before they become quarterly surprises. This is the kind of monitoring capability that converts AIP's philosophy of "lead by serving" from a qualitative commitment into a data-driven operating system.

Value Creation Planning & Fund Operations

AI assistance in LP reporting — synthesizing operational, financial, and strategic data across 29 active platforms into customized quarterly communications — reduces the administrative burden on partners while improving the depth and consistency of LP engagement. For Fund IX fundraising, the ability to demonstrate a systematic AI value creation thesis, backed by performance data across the portfolio, is an LP relations asset that competitors without a formal AI practice cannot replicate. Knowledge management AI, surfacing relevant precedents from AIP's 125+ acquisition history during new deal evaluation, converts institutional memory from a tacit resource held by individual partners into an organizational capability that scales with the team.

AI Advisory Services

Compoze Labs provides AI advisory services purpose-built for operationally intensive businesses — including PE-backed industrials at every stage of the investment cycle. Each service area below is described in the context of AIP's specific portfolio composition and value creation objectives.

The outcome of a well-structured AI advisory engagement is not a technology deployment — it is an organizational capability. The table below summarizes the three categories of measurable outcome that AIP should expect from a systematic advisory engagement across the portfolio.

Conclusion & Recommended Next Steps

American Industrial Partners has built one of the most operationally sophisticated industrial investment platforms in global private equity. The Firm's engineering-centric DNA, Operating Agenda framework, and $28 billion portfolio across 10+ industrial end markets represent a structural advantage that generalist PE firms cannot replicate. AI is the most powerful operational tool of this decade — and the conditions for AIP to pursue it systematically have never been better aligned.

The five priority initiatives below translate the analysis in this brief into an ordered action plan. Each builds on AIP's existing strengths rather than requiring organizational transformation:

1. Formalize an Industrial AI Practice within Justin Fish's Operations team, with a designated AI Practice Lead and a mandate to develop standardized AI deployment playbooks by manufacturing archetype. This converts AIP's engineering-centric team composition from a latent AI advantage into an active one — and creates the coordination infrastructure needed to move from 29 independent pilots to a systematic portfolio program.
2. Launch Phase 1 AI pilots at four high-conviction platforms: ADDMAN (generative design and process optimization AI), V2X (defense logistics and predictive maintenance AI), Commonwealth Rolled Products (rolling mill process and quality AI), and Grain & Protein Technologies (IoT-connected grain monitoring and aftermarket intelligence). Target measurable ROI within 6 months to build the empirical case for portfolio-wide deployment.
3. Deploy AI-enhanced deal sourcing and diligence tools — corporate carve-out intelligence, turnaround screening, and an Operating Agenda generator trained on AIP's 125+ acquisition dataset. This directly strengthens the competitive moat in carve-out and distressed situations where AIP's edge is analytical depth and speed to conviction.
4. Engage the Blackstone GP Stakes ecosystem for cross-portfolio AI vendor procurement, technology platform sharing, and knowledge transfer. The January 2025 partnership provides access to a $1 trillion+ business ecosystem; converting that relationship into a tangible AI acceleration advantage requires deliberate activation, not passive access.
5. Establish a portfolio-wide AI governance framework addressing V2X's ITAR/DFARS and zero-trust requirements, Austin Powder's explosives compliance obligations, Enviva's and Global Cellulose Fibers' environmental regulatory exposure, and OT cybersecurity standards across 240+ manufacturing and distribution facilities. AIP's regulatory complexity, managed proactively, becomes a moat — not a constraint.

The competitive window is real and it is narrowing. Brookfield PE has declared AI its third value creation pillar. KPS Capital has embedded Siemens-heritage digitalization capabilities through Innomotics. CORE Industrial builds an AI thesis into every new deal it underwrites. AIP has spent four decades proving that operational excellence creates industrial value. AI is the next evolution of that proof.

The engineering talent is in place. The Blackstone ecosystem is available. The portfolio breadth creates the most diverse and defensible AI deployment surface of any industrial PE firm. The conditions for AIP to establish manufacturing AI leadership are as favorable as they will ever be.