Balancing efficiency gains of AI in industrial operations with strategic risk in exit planning.
By Alexander Gordin and Chris Falk
Business owners and C-suite executives preparing their companies for the eventual exits, face many conventional risks and challenges such as developing sound strategies, attaining revenue growth, maintaining profitability, preserving quality of earnings, developing human resources to name just a few. Yet, in addition to these already formidable undertakings, a new and very important issue has been rapidly emerging and it requires a very sober and comprehensive approach on how to address it and incorporate into sound business operations and exit planning.
Artificial intelligence promises transformative operational efficiencies across industrial sectors—predictive maintenance reducing downtime by 30-50%, supply chain optimization cutting inventory costs by 15-25%, and quality control improvements boosting yields significantly. Yet for C-level executives focused on eventual exit strategies, the conversation cannot stop at efficiency gains. The technical debt, vendor dependencies, and knowledge transfer challenges inherent in AI implementations may create as many strategic risks as they solve operational problems.
As the AI adoption curve accelerates, industrial leaders must balance immediate operational benefits against long-term strategic flexibility. The companies that navigate this balance successfully will create sustainable value; those that don’t may find themselves locked into dependencies that complicate exit negotiations.
Every AI implementation creates technical debt—the accumulated cost of shortcuts, vendor dependencies, and system integrations that must eventually be addressed. Unlike traditional software implementations, AI systems require ongoing model training, data pipeline maintenance, and continuous optimization. This creates several strategic considerations that extend far beyond initial implementation costs.
The most successful AI implementations require dedicated internal expertise, often including data scientists, AI engineers, and specialized operations personnel. For mid-market industrial companies, this can represent $500K-$1.5M in annual staffing costs, plus the ongoing challenge of retaining these high-demand professionals in competitive markets. The question becomes: does the operational efficiency gained justify building this internal capability, or does it create an unsustainable cost structure that limits future strategic options?
One of the most critical questions facing executives considering AI implementation is provider longevity. The AI technology landscape is characterized by rapid innovation, aggressive venture funding, and equally aggressive consolidation. Many of today’s AI vendors—particularly those offering specialized industrial applications—are early-stage companies with uncertain long-term viability.
The two-year test should be fundamental to any AI vendor evaluation: Will this provider exist, maintain their current capabilities, and continue supporting our implementation two years from now? This question becomes particularly acute when considering that AI systems often require 12-18 months to deliver meaningful ROI, leaving little buffer for provider instability.
Companies should evaluate vendor financial stability, customer concentration, technology differentiation, and competitive positioning. More importantly, they should assess the complexity and cost of migrating to alternative solutions should their chosen provider disappear or pivot away from their market segment. The most operationally impressive AI solution becomes a liability if it cannot be maintained or migrated.
The decision between building internal AI capabilities versus relying on external providers represents one of the most strategic choices facing industrial leaders. Internal development offers maximum control and customization but requires significant investment in both technology infrastructure and human capital.
Building internal AI capabilities typically requires $2-5M in initial investment for mid-market industrial companies, including hardware infrastructure, software licensing, and team development. Ongoing costs often exceed $1M annually. However, this approach provides complete control over intellectual property, eliminates vendor dependencies, and creates potentially valuable proprietary capabilities.
The alternative—vendor-provided solutions—offers faster implementation and predictable costs but creates dependencies that may limit future flexibility. The key consideration is whether AI capabilities will become core to your business model or remain supporting tools. If AI-driven insights become fundamental to your competitive advantage, internal development may be essential regardless of cost.
The AI landscape evolves rapidly, with new approaches, architectures, and capabilities emerging continuously. Today’s cutting-edge solution may become obsolete within 24 months. This reality demands that executives evaluate not just current AI capabilities, but the flexibility to adapt to future technological developments.
The most successful AI implementations are built on modular architectures that allow for component replacement and system evolution. This requires upfront investment in integration frameworks and data standardization, but provides the flexibility to adopt new technologies without complete system replacement.
Companies should assess their current AI implementations’ modularity and upgrade paths. Can individual components be replaced without system-wide disruption? Are data formats and interfaces standardized to support future integration? Does the current architecture support new AI models and approaches as they emerge?
For companies considering exit strategies, the complexity of AI system handoffs represents a significant due diligence challenge. Unlike traditional operational systems, AI implementations often rely on institutional knowledge, custom configurations, and ongoing optimization that may not transfer easily to new ownership.
Potential acquirers increasingly evaluate AI implementations not just for their current performance, but for their transferability and sustainability under new leadership. This requires comprehensive documentation, standardized processes, and knowledge transfer systems that extend beyond traditional operational procedures.
The most successful companies create “AI playbooks” that document not just system configurations, but decision-making processes, optimization strategies, and performance benchmarks. This documentation becomes essential for leadership transitions, whether through planned succession or exit scenarios.
Perhaps the most strategic question facing executives is whether AI capabilities represent core business value or supporting operational efficiency. This distinction fundamentally impacts both growth strategies and exit valuations.
When AI becomes core to your value proposition—enabling new products, services, or business models—it represents a strategic asset that enhances valuation. However, this also creates dependencies that must be carefully managed through transitions. When AI remains a supporting technology for operational efficiency, it may provide cost advantages without creating strategic dependencies.
Companies should honestly assess whether their AI implementations create differentiated capabilities that competitors cannot easily replicate, or whether they provide operational efficiencies that any competent operator could achieve. The answer influences both investment strategies and exit positioning.
Successful AI implementation in growth-focused industrial companies requires a framework that balances operational benefits against strategic risks:
Provider Evaluation: Assess vendor stability, support capabilities, and migration options before implementation. Prioritize providers with proven track records and clear long-term viability.
Architecture Planning: Design AI systems with modularity and upgrade paths that support future technology evolution. Invest in integration frameworks that enable component replacement.
Capability Development: Make explicit decisions about internal vs. external AI capabilities based on strategic importance and resource availability. Build internal expertise for core capabilities, outsource supporting functions.
Documentation and Transfer: Create comprehensive documentation and knowledge transfer systems that support leadership transitions and exit scenarios.
Value Assessment: Regularly evaluate whether AI implementations create strategic competitive advantages or operational efficiencies, and adjust investment accordingly.
AI represents both tremendous opportunity and significant risk for industrial companies focused on exit strategies. The companies that succeed will be those that implement AI strategically, with full awareness of the technical debt, dependencies, and transfer challenges involved.
For business owners and C-level executives, the message is clear: AI can deliver significant operational value, but only when implemented with careful consideration of its long-term strategic implications. The companies that master this balance will create lasting value; those that don’t may find themselves locked into dependencies that constrain their future options.
About the Authors:
Alexander Gordin, EQB, is an International Merchant Banking and Risk Management professional with over 30 years of experience providing services in the areas of business advisory and business exit planning, project finance and investment, risk mitigation, capital markets transaction development. Clients have ranged from US-based and foreign SMEs, foreign governments, state-owned enterprises, to select Fortune 500 cos.
Transaction and negotiations experience in over 30 countries. Extensive experience in aerospace, food security, wireless communications, alternative & conventional energy generation, spent nuclear fuel storage, specialized construction, hospitality, healthcare and franchising industries. Significant experience working in post-conflict countries and emerging markets. Participated as Principal and advised on multiple exits, financing, M & A and restructuring transactions ranging from $1.1 million to $500 million.
Author of the critically acclaimed “Fluent in Foreign Business” book. Co-creator of the proprietary suite of business development, business exit and financing tools including the Exit Quarterback®:
Published, or featured in multiple publications including: Frequent speaker on international finance, foreign direct investment and strategic business development. Featured speaker at numerous conferences.
MBA from the Wharton School at the University of Pennsylvania.
B.S. in Management Information Systems from the NYU Tandon School of Engineering.
Chris Falk is the President and lead advisor for Skrillo Solutions, a revenue performance firm specializing in operations-focused sales strategies and training that delivers measurable results. With over 20 years of experience working with companies in the industrial and manufacturing sectors, Chris has been supporting growth for companies during the transition to Industry 4.0 with modern business development initiatives.
Chris has been a speaker at events such as the Global Energy Show and sits on boards and committees related to energy, manufacturing, and digital transformation.
Read the previous articles in this series:
EXIT @Maximum!, September 2024
Should I Grow Or Should I Go?, October 2024
EXIT@Maximum III, August 2025
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