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The key questions each executive needs to ask regarding AI

  1. Global increase in profitability: AI will drive $15.7 trillion in global economic gains by 2030 (PWC)
  2. Automation: 45% of current work activities can be automated with existing technologies (McKinsey)
  3. Cost reductions: AI enables 40-50% cost reduction in customer service operations (McKinsey)
  4. Less mistakes, better accuracy: Machine learning models achieve 99% accuracy in specific diagnostic tasks (PWC)
  5. Better decision quality: AI-driven decision-making improves strategic outcomes by 35%
  6. Point of no return: AI capabilities have surpassed human-level performance in many cognitive tasks
  7. AI adoption tipping point: Many industries have reached AI adoption tipping point, making non-adoption an existential threat
  8. Proprietary competitive advantage permanently at risk: AI transformation is essential for maintaining competitive relevance and avoiding market obsolescence

All data to be understood as indicative
  1. Machine Learning: Enables systems to learn from data, improving accuracy steadily; for example, feature engineering, hyperparameter tuning, and ensemble methods are key strategies to enhance model performance (Source:
  2. Deep Learning: Powers breakthroughs in image and speech recognition, with 98% accuracy or higher (Source: Google)
  3. Natural Language Processing: Facilitates human-like text understanding and generation as Generative AI and LLMs have amply demonstrated
  4. Computer Vision: Enables visual data interpretation with superhuman accuracy in several tasks; number of tasks increasing
  5. Robotics and Autonomous Systems: Automate physical tasks, increasing productivity by 200% (Source: McKinsey)
  6. Quantum Computing: Promises significant speedups for specific AI algorithms (e.g. Grover's and Shor's algorithms)
  7. Edge AI: Brings intelligence to devices, reducing latency by 90% and enhancing privacy (Source: McKinsey)

All data to be understood as indicative
Feedback, resonance, and recoupling mechanisms:

  1. Data-Algorithm Flywheel: More data improves algorithms, better algorithms generate more valuable data
  2. AI-Human Collaboration: AI augments human capabilities, humans refine AI, creating a virtuous cycle
  3. Cross-domain AI Application: Insights from one field accelerate progress in others by 40%
  4. AI-driven Scientific Discovery: AI accelerates research, leading to better AI tools, compounding progress
  5. Network Effects in AI Ecosystems: Each new user/application improves the entire system
  6. AI-Enhanced Hardware Design: Better AI creates more efficient hardware, enabling more powerful AI

All data to be understood as indicative
  1. Unprecedented speed: AI model capabilities double every 3.4 months, outpacing Moore's Law
  2. Ever increasing data base: Data generation grows at 61% CAGR, fueling exponential AI improvement
  3. Exponential learning: AI-to-AI learning accelerates knowledge acquisition compared to human-guided learning
  4. Quantum wild card: Quantum-AI integration promises 1,000,000x speed increases in specific computational tasks
  5. Combined AI (aka combined arms): Mix and combination of AI techniques creates combinatorial explosion of new capabilities
  6. Supercharged sciencentific discovery: AI-driven scientific discovery accelerates technological progress across all fields by 40%

All data to be understood as indicative
  • Change in underlying economics: Shift from diminishing to increasing returns to scale in AI-intensive industries
  • Network effects: they amplify value creation, each new user adding value
  • Race to zero marginal costs: Marginal cost of digital goods approaches zero, revolutionizing pricing strategies
  • Personalised pricing: AI-driven personalization allows perfect price discrimination, maximizing consumer and producer surplus
  • Elastic supply curves: Supply curves become more elastic as AI optimizes production and resource allocation
  • Winner-take-most markets: Emergence of winner-take-most markets due to data network effects and AI capabilities
  • Proprietary algorithms: Superior algorithms that create True Alpha (information that is commercially beneficial and only one party has access to) will create huge differences. Remember Google. Imagine the perfect employee - employer matching algorithm
  • User and data network effects: More users, providing more data will lead to better algorithms and better algorithmic outcomes (e.g. predictions, matching etc.). Potentially enhanced by synthetic data.
  • Transaction cost reductions: As AI assistants will take over more and more daily tasks and negotiate those tasks between them, de facto transaction costs will be reduced.
  • New principal agent problems (explainable AI): Over the next five years AI will become more and more agent like. Regulation will require explainability. Explainability will be linked to liability. This is a key challenge and potential road block for many companies regarding implementation.
  • Signficantly higher productivity levels: Automation by AI will lead midterm to much higher productivity levels.
  • Comparative AI advantage: Comparative advantage will not only refer to traditional production factors but also to domain specific intelligence - for individuals, groups, companies and countries.
  • Parallel hyper acceleration: Likely, on from 2026 latest this developments will happen mostly in parallel.

All data to be understood as indicative
  1. Dynamic strategies: AI necessitates a shift from the traditional planning cycle over several years to dynamic, continuously evolving strategies. Can your IT infrastructure handle this?
  2. Redefinition of core competences: 70% of companies need to redefine their core competencies to incorporate AI capabilities. Example: The call center company must develop a core competence for AI agent training.
  3. Strategy as code: AI enables real-time strategy adjustments based on predictive analytics and fully integrated corporate wide data flows
  4. Industry boundaries transcending competition: Customer needs amplified by AI experiences define future industry boundaries; AI creates new market opportunities, requiring broader competitive scanning
  5. Better foresight: AI-driven scenario planning improves strategic decision-making accuracy 
  6. Permanent short notice adaptation of value product and services bound value propositions:  Many companies must realign their value proposition to be able to leverage AI-enhanced products and services; what remains stable are Purpose, vision and brand. That what creates meaning. Do you have sufficiently deep ressources allocated to brand building? It will be one of the few future competitive differentiators.
  7. Preemptive M&A strategy: Strategic partnerships and potentially acquisitions of AI technology or AI technology companies  become critical; an M&A buying list that is ideally monthly updated is likely a future necessity
  8. AI integration capabilities: Most companies must develop dynamic capabilities to rapidly integrate and reconfigure AI assets. This not only refers to software or hardware but nearly all corporate functions.

All data to be understood as indicative
  • AI-powered personalization creates sticky customer relationships, increasing retention rates between 15% to 25%
  • Proprietary AI algorithms become key differentiators: Proprietary AI algorithms are driving significant efficiency gains across various industries, with estimates ranging from a 35% increase in sales due to Amazon's recommendation engine to a potential $1 billion annual savings for Netflix through personalized recommendations, while Ocado reports a 3.5x increase in warehouse efficiency with AI-powered robotics (McKinsey).
  • AI accelerates innovation cycles, allowing for rapid adaptation to market changes and pre-emption strategies for market entry
  • Advanced AI capabilities create significant barriers to entry in numerous industries: "More extensive comptue infrastructure allows better foundation models, better foundation models invite more developers that build better applications, better applications lead to larger deployment which generates revenues that brings capital to invest in even more infrastructure to build even larger foundation models, etc. That self-reinforcing growth cycle appears very real in AI. And it means that the big get bigger and the small get smaller as they lackthe scale to generate positive network effects or cost-related economies of scope." (Madrona)
  • AI-optimized pricing strategies can increase profit margins over traditional methods as the tend to set supracompetitive prices
  • AI-powered ecosystem orchestration creates new moats, increasing switching costs by several factors long term; in markets with high switching costs initial market share disributions tend to propagate long term; thus future profitability is determined by initial or early market share
  • Continuous learning AI systems create cumulative intelligence advantage leading to widening performance gaps over time.
  • AI enables hyper-personalization at scale leading to higher percentages of repurchases
  • AI-driven business model innovation outpaces traditional approaches by at least 5x (personal experience)

All data to be understood as indicative
  1. The GREAT EQUALIZIER: AI commoditizes previously unique capabilities, eroding traditional competitive advantages
  2. Time to obsolences will radically shrink: Rapid AI advancement quickly makes existing business models obsolete
  3. First-mover-takes the most: Likely, AI-driven market entrants rapidly capture market share, disrupting incumbent leaders
  4. Strategic blindspots: Over-reliance on AI creates strategic blindspots, increasing vulnerability to unforeseen threats
  5. Unsustainable Investment: AI arms race might lead to unsustainable investment levels, especially in head-to-head races in clearly defined niche markets, eroding profitability
  6. Very broad democratisation of services: AI democratizes previously scarce capabilities, grinding down traditional barriers to entry
  7. Dramatic acceleration: Rapid AI progress compresses timeframes by 3x to 5x - a new frame of reference is required
  8. AI leverage: AI-powered market entrants can achieve scale several times faster than traditional companies
​
All data to be understood as indicative
1. Technological Breakthroughs and Technological Convergence

  • Emergence of transformer models enabling breakthrough performance in NLP tasks
  • Significant advancements in deep learning and neural network architectures
  • Development of more efficient training algorithms and techniques
  • Development of synthetic data generation techniques to slash training costs
  • Synergies between AI and technologies like 5G, IoT, and blockchain
  • Breakthroughs in transfer learning, allowing models to adapt to new tasks more quickly
  • Advancements in reinforcement learning, enabling AI to excel in complex decision-making scenarios
  • Progress in few-shot and zero-shot learning, reducing the need for large labeled datasets
  • Integration of AI with robotics and automation expanding real-world applications
  • Accelerated scientific discoveries in fields like genomics and materials science
  • Accelerated market discovery, product development and market entry

2. Increase in Computational Power coupled with Algorithmic Improvement

  • Exponential growth in GPU and TPU capabilities, enabling faster and more complex AI training
  • Cloud computing making high-performance AI infrastructure accessible to businesses of all sizes
  • Edge computing allowing AI deployment on resource-constrained devices

3. Business Adoption Readiness

  • Quantifiable benefits in terms of cost reduction and revenue growth
  • Development of robust AI platforms and services by major tech companies
  • Ever increasing number of VC financed, of AI-focused startups offering specialized solutions
  • Evolving regulatory frameworks providing clearer guidelines for AI deployment
  • Government initiatives and funding supporting AI research and adoption tax breaks
I. Market Dynamics and Industry Transformation

  1. First-Mover Advantage: Early AI adopters are gaining a 20-30%* revenue growth advantage, creating data network effects that make it harder for laggards to catch up.
  2. Industry Disruption: AI-native startups and cross-industry applications are putting 35%* of traditional business models at risk of disruption within 5 years (likely more)
  3. Ecosystem Dominance: 40% of industry value* is expected to shift to AI-orchestrated ecosystems by 2030, companies outside key AI ecosystems risk significant disadvantages
  4. Global Competition: 70% of executives* believe AI will significantly impact global competitive positioning, driving cross-border collaborations and national AI initiatives.

II. Operational Excellence and Innovation

  1. Operational Efficiency: AI-enabled competitors are achieving 20-30% cost reductions* in core operations, roughly doubling speed-to-market.
  2. Predictive Power: AI-driven predictive capabilities are providing 30-40% accuracy improvements in forecasting
  3. Innovation Speed: AI is accelerating R&D cycles by 20% - 30%*, in exceptional cases up to 50%*
  4. Pricing Power: AI-powered dynamic pricing is providing 5-10% margin improvements*

III. Customer-Centric and Stakeholder Considerations

  1. Customer Expectations: 70% of customers* now expect AI-driven personalization, with non-AI companies seeing substantially higher churn rates.
  2. Brand Perception: Companies seen as AI leaders enjoy substantially 25% higher brand value* on average, positively supporting consumer preference and talent attraction.
  3. Sustainability and ESG: Companies leveraging AI for ESG are seeing 20% higher stakeholder trust scores*, with 55% of consumers* preferring AI-driven sustainability efforts.
  4. Investor Pressure: Companies with clear AI strategies are seeing 2x to 3x higher valuations*, with more than 80% of institutional investors* considering AI capabilities a key factor in investment decisions.

IV. Strategic Resources and Risk Management

  1. Data Supremacy Race: 80% of companies* view data as a critical competitive asset, driving a new wave of strategic M&A activity.
  2. AI Talent Wars: Demand for AI talent is outstripping supply by 3:1*, with AI leaders attracting top talent 2x more effectively*.
  3. Regulatory Preparedness: Early AI adopters are better positioned to comply with and influence evolving AI regulations, potentially creating barriers for others.
  4. Cybersecurity Arms Race: Companies with AI security are 3x more resilient* to cyber threats, with 90% of CISOs* believing AI is essential for future cybersecurity.

* back-on-the envelope estimates
  1. Bias: AI bias leading to discriminatory outcomes in many of deployed systems* creating new liability risks and delays regarding implementation
  2. Cybersecurity: Cybersecurity threats increase annually by 20% to 50%*, also due to AI-powered attacks
  3. Job replacement and loss: Approx. 30% of jobs* at high risk of automation by 2030, causing workforce disruption
  4. Hallucinations: AI hallucinations in large language models occur in 3-5% of outputs
  5. Compliance challenges: Regulatory compliance challenges with many new AI laws enacted globally in 2023 and 2024
  6. Monopoly risk: Potential for AI-driven market concentration

* Back-on-the envelope estimates
  • Revenue impact: AI-driven product recommendations and customer relationship management increase e-commerce revenue by up to 35%*
  • Cost impact: Predictive maintenance reduces equipment failures by up to 70%*
  • R&D efficiency increase: AI in R&D accelerates time-to-market by 20%, rarely up to 40%*
  • Front-line efficiency: Natural language processing improves customer service efficiency by up to 50%*
  • Supply chain optimisation: AI optimizes supply chain logistics, reducing costs by up to 20%*
  • Increased customer lifetime value (B2C): AI-powered personalization increases customer lifetime value by up to 30%*
  • Real options: AI enables creation of entirely new markets and business models
  • Value chain integration: AI-driven process reengineering can unlock 30% - 50% efficiency gains across the value chain (but incurring substantial risks)*
  • Bottom line: Rule of thumbs for fully integrated AI infrastructure after three years (in respect to baseline, varies widely across markets and industries, requires focused assessment): 15% higher turnover, 5% price increase; 10% - 15% less cost, doubling of EBIT possible, different growth trajectory; 2x valuation
  • SPATHA has calculated those typical P&L impacts for several companies using an integrated three statement model; as part of your advisory assigenments we are happy to take you through the details

* Back-on-the envelope calculation, SPATHA estimate
  1. Revenue: Revenue growth potential of 15-25% through AI-driven product innovations and market expansion
  2. Cost: Cost reduction of 10-30% in operations through AI-powered automation and optimization
  3. Margin: Doubling of EBIT margin possible due to AI efficiencies (if applied according to a comprehensive transformation program)
  4. Working Capital: Working capital optimization by 10-15% through AI-enhanced forecasting and inventory management, better supply chain co-ordination and other aspects
  5. Free Cash Flow: Initially limited reduction, after approx 18 month notable increas, long term 2x FCF in comparison to baseline is possible
  6. R&D: R&D efficiency increase of 30-40%, accelerating time-to-market for new products
  7. Valuation: Potential 1,5 x- to 2x increase in share price due to AI-driven competitive advantages
​
  • AI Maturity Assessment: Conduct an AI  maturity assessment across all business units within 60 days
  • Comprehensive AI strategy: Develop a comprehensive AI strategy aligned with overall business objectives
  • Unique Talent: Invest in AI talent acquisition and training programs for existing staff
  • Data quality: Establish data governance protocols to ensure high-quality AI training data (technical, volume, legal, compliance)
  • Pilot projects: Launch 3-5 high-impact AI pilot projects in key business areas within 6 months
  • Centre of Excellence: Create an AI Center of Excellence to drive innovation and best practices
  • AI-first culture: Implement an AI-first culture change program to ensure organization-wide adoption
  • Network effects: AI systems improve exponentially with more data and users, creating a feedback loop that becomes nearly impossible for competitors to match over time.
  • Proprietary data ecosystem: Build an AI-powered platform that continuously collects and analyzes unique data, creating a knowledge base that becomes more valuable and harder to replicate over time.
  • Brand loyalty: Creation of hyper-personalized customer experiences. Those dramatically increase switching costs, making it emotionally and practically challenging for customers to leave.
  • Leveraging path dependency: Early choices in AI development might have outsized effects on future capabilities, locking in advantages that become increasingly difficult to overcome.
  • Pricing power: Faster innovation, better product, faster go-to-market, faster scaling, economies of scale and scope, lower price base - substantially increased pricing power.
  • Regulatory advantage: Analysis of complex regulatory environments by AI allows to become standard setters, creating high barriers to entry.
  • Innovation pipeline: Establish an AI system that consistently analyses customer pain points and generates and develops new product ideas. This will create a self-reinforcing cycle of innovation that keeps the company ahead of competition.
  • Strategic foresight: Evoluationary simulation of market participants. E.g. train one LLM per competitor, let the LLMs play many many rounds (years) of market interaction. Analyse the different outcomes and different decisions taken.
  • Ecosystem lock-in: For example, create an AI-based supply chain optimisation platform so interconnected and efficient that it becomes the de facto standard in the industry, locking in customers, suppliers, and partners.
  • Positive Feedback Loops: More data leads to better AI, attracting more users, generating more data, and so on, leading to winner-take-all dynamics. 
(examples only, no claim in respect to comprehensiveness is made)
  1. Bias and fairness: AI systems can invent, perpetuate or amplify existing biases. C-level decision-makers risk legal challenges, reputational damage, and erosion of customer trust. Regular audits of AI systems for bias and diverse representation in AI development teams are crucial measures to mitigate these risks.
  2. Transparency and explainability: Many AI systems are difficult to analyse, monitor and interpret. This makes it challenging to understand their decision-making processes. Executives may face regulatory and legal scrutiny. They might need to  defend AI-driven decisions in legal or ethical disputes. Investing in explainable AI technologies and maintaining comprehensive documentation of AI system designs and decision processes are important steps.
  3. Accountability: Determining responsibility for AI mistakes can be complex. Decision makers and companies are exposed to legal and financial risks. C-level executives may face personal liability if proper oversight measures are not in place. Establishing clear chains of responsibility and implementing robust governance frameworks are mandatory.
  4. Privacy and data protection: AI systems often require vast amounts of data, raising concerns about data collection, usage, storage and recycling. Executives risk severe penalties, loss of consumer and shareholder trust should data protection regulations be violated. Implementing strict data governance policies while regularly assessing compliance are non-negotiables.
  5. Autonomy and human oversight: Balancing AI efficiency with human judgment is necessary. Over-reliance on AI likely leads to critical errors, while under-utilization will surely result in missed opportunities. Human oversight and intervention in AI systems, especially for high-stakes decisions, need to be carefully crafted and unchangeable implemented. Blockchain technology offers apt tools to achieve this.
  6. Job displacement: AI-driven automation will certainly lead to significant job losses. Social unrest and negative publicity are a near certainty. C-level decision-makers may face backlash from employees and the public if job losses are not managed carefully. Common sense and personal interest suggest investing in reskilling programs and gradually implementing AI solutions. Ethically, this is mandatory.
  7. Security and safety: Ensuring AI systems must be secured against attacks. They must safe for human interaction. Executives, risking major security breaches, physical harm to individuals, will be confronted with painful legal consequences if these aspects are neglected. Regular security audits and adherence to evolving AI safety standards are necessary measures.
  8. Power concentration: The concentration of AI capabilities in a few entities could lead to monopolistic practices and global power imbalances. C-level decision-makers in dominant AI companies may face antitrust scrutiny and geopolitical pressures. Promoting industry collaboration, supporting AI democratization efforts, and engaging in responsible AI development practices are important considerations.
  1. Negative environmental impact: Growing energy demand, data centers' carbon emissions, and e-waste from rapid hardware turnover present significant environmental challenges.
  2. Extreme personalisation: This process will likely result in filter bubbles, and echo chambers, potentially atrophying human cognitive skills (especially when linked with unemployment). Reality and virtual worlds increasingly blurr. Rationality, truth and facts will further loose relevance.
  3. Geopolitics: Already today we face an AI arms race of the superpowers. The winner of this race will have short term advantages, long term likely dominate the new world order. This could lead to a new normal of permanent low intensity conflict.
  4. Fairness: AI-driven economies will further concentrate wealth, cause structural unemployment, and challenge workforce reskilling efforts.
Thank you for reading! Happy to discuss this with you in person. Please call on ++49 177 84 52 53 0 or write to ruben.bach@spathacapital.com. Looking very much forward to discuss this topic in repsect to your company.
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