By Jacques Bughin
The generative AI landscape has rapidly evolved, turning emerging concepts into proven drivers of business value. Jacques Bughin revisits ten key AI investment opportunities and examines how advances such as agentic orchestration, synthetic data, and verticalized LLMs are reshaping competitive strategies. He provides practical insights to help executives capture AI’s full potential.
Given the transformative evolution of AI, the landscape has since evolved profoundly—maturing some areas into proven profit centers, introducing new AI paradigms like agentic orchestration and verticalized LLMs
By July 2024, or one year ago, we provided a visionary framework identifying ten key generative AI investment and impact opportunities for enterprises. Given the transformative evolution of AI, the landscape has since evolved profoundly—maturing some areas into proven profit centers, introducing new AI paradigms like agentic orchestration and verticalized LLMs, and anchoring AI as a fundamental driver of the way to competitive.
This article revisits and expands on those opportunities, weaving in recent advances in agentic AI orchestration, multi-agent coordination, and robotic augmentation. It provides an integrated perspective on how C-suite leaders should act decisively to capture AI’s full value and competitive edge.
The List
1. AI embedded deeply in business processes
AI’s value translates when integrated end-to-end into workflows. Deutsche Telekom’s “Ask Magenta” chatbot, an AI-powered assistant, offloads 70% of fiber-optic customer support queries, boosting customer satisfaction scores by 15 percentage points and reducing operational costs significantly. Similarly, Walmart’s European logistics AI enhances inventory forecasting and route planning, achieving a 30% cut in stock-outs and millions in annual savings.
Management insight: The recent case experience of rolling out AI states that only cross-functional AI operating models are able to deliver on the AI promises.
2. The Rise of Agentic AI Orchestration: autonomous yet coordinated AI workforces
Agentic AI—AI systems capable of independent decision-making, planning, and goal-directed execution—is rapidly scaling in enterprises. Ampcome, a European logistics AI platform, has demonstrated how multi-agent systems can autonomously coordinate routing, dispatching, and inventory management, achieving operational cost cuts of over 40%. Their agents combine Retrieval-Augmented Generation (RAG), pulling real-time data from complex sources, with autonomous decision-making, showcasing how agentic AI elevates from a reactive tool to a proactive orchestration framework
Wells Fargo’s corporate banking divisions implemented custom AI agents using Google’s Agentspace to unlock new efficiencies—bankers now spend significantly less time hunting for contract clauses or foreign exchange policies. Agents query hundreds of thousands of documents in seconds, enabling client-facing staff to focus on relationships and advisory. Their success underscores the necessity of deep integration with up-to-date internal data and human oversight for high-risk decisions
In manufacturing, Siemens embodies agentic orchestration’s physical extension. Their “Industrial Copilots” coordinate AI agents managing product design, production planning, real-time plant analytics, and robotic task execution, forming an intelligent operational swarm. Pilot factories report up to 50% productivity gains and improved machine uptime, thanks to modular agent orchestration layers that coordinate human and robot collaboration. This architecture allows seamless integration of third-party agents, laying a foundation for scalable AI ecosystems
A 2024 global survey involving 1,650 senior execs revealed 94% acknowledge process orchestration as crucial for AI success, highlighting that without this nervous system, agentic AI deployments often fail or stall. Governance frameworks mandating explainability and audit trails per the EU AI Act further emphasize the human oversight required in agentic ecosystems.
Management insight: Hence, agents are here to stay and expand, but executives must prioritize investing in agent orchestration platforms, employee reskilling to manage AI interaction, red-teaming AI systems for risk, and establishing compliance protocols to unlock agentic AI’s full potential.
3. Synthetic Data
(At least, European) Leaders face stark regulatory constraints on data use. Synthetic data has emerged as a powerful solution to accelerate AI innovation without compromising privacy. Pfizer harnesses synthetic patient datasets to accelerate drug discovery timelines by 15%, sidestepping patient-identifying information. European fintech startups achieve 30% better fraud-detection model accuracy using synthetic customer profiles while maintaining GDPR compliance.
Top e-commerce companies are now using synthetic customer data to offer personalized shopping experiences. This new method is changing the retail world as retailers struggle with the challenge of offering personalization while still protecting customer privacy. Synthetic data solves this by creating detailed customer profiles without invading privacy. Big retailers such as Target  have seen huge boosts in sales with synthetic customer data., through a radical change in its marketing.
Management insight: Companies must embed synthetic data into their data strategies, engaging domain-focused vendors like MOSTLY AI and Hazy, while collaborating across legal and data science teams to ensure scalable and compliant synthetic data pipelines.
4. Responsible AI as a governance and trust Lever
The EU AI Act’s regulatory regime makes automated AI fairness, transparency, and auditability a competitive boundary in sectors such as banking and energy. European banks employing AI auditing tools reduced regulatory compliance costs by 75%, signaling that responsible AI directly impacts enterprise efficiency. Iberdrola strands regulatory workflows with AI-enhanced monitoring, both accelerating internal processing and promoting customer trust.
Management insight: Leadership mandates are shifting toward establishing dedicated AI ethics and compliance functions, integrating AI transparency by design, and proactively communicating responsible practices externally.
5. Sustainable AI
Reducing AI’s energy footprint has become urgent amid EU Green Deal commitments. Nordic cloud providers lead by cutting AI compute energy consumption by half using custom silicon and renewable power. Mercedes-Benz integrates AI for eco-driving assistance, tightly aligning vehicle AI with sustainability goals
Management insight: Top management teams must demand energy transparency, embed green compute into procurement criteria, and align AI infrastructure strategies with corporate ESG objectives.
6. Multi-Modal & Industry-Specific LLMs
Sanofi’s drug discovery harnesses unique vertical LLMs trained on clinical, chemical, and genomic data, trimming development phases by roughly 20%. Similarly, AI start-ups such as LegalFly, are  fine tuning LLMs for lawyers, boosting document analysis speed and accuracy by 35%.
Management insight: Forward-looking firms invest in domain-specific data assets and collaborate openly with academic and industry partners to continuously evolve their vertical AI capabilities.
7. MLOps—The Backbone for Reliable AI Deployment at Scale
Many organizations suffered a high rate of AI pilot failures until MLOps tools matured. Maersk’s MLOps infrastructure now drives near 90% success on production AI deployment, a leap from under 20%. Renault slashed model retrack costs by over 60% through rigorous ML governance.[9]
Management insight: Governance that unifies IT, data science, and business teams around model monitoring, drift detection, and remediation is now a board-level imperative.
8. AI Cybersecurity: Defending and advancing with AI
Vodafone leverages AI to shrink cyber incident response times fourfold, cutting false alerts by 30%. Dutch financial institutions use generative AI to accelerate phishing detection and regulatory compliance, tripling incident handling speed.
Management insight: Senior leaders must fund AI-augmented cyber defense programs and conduct regular threat simulation exercises.
9. Robotic Augmentation
The boundary between digital and physical is dissolving. Siemens’ copilot factories, GE Healthcare’s autonomously calibrated devices, and Bavaria’s robotic logistics fleets show how agentic orchestration is extending into robotics—fusing multi-agent ecosystems with physical action.
Management insight: Best practices prioritize pilot sites where robotic augmentation can deliver compounded gains—productivity, uptime, and regulatory assurance.
10. Data, Talent, and Ecosystem as Strategic Assets
Without serious investment in these complements, no AI strategy can sustain competitive advantage.
An AI moat will depend on orchestrating three scarce resources: domain data, partner ecosystems, and reskilled workforces. Without serious investment in these complements, no AI strategy can sustain competitive advantage.
Management insight:Â Build European data consortia, scale workforce reskilling, and establish venture-style partnerships to access external AI innovation at speed.
Beyond the List – The Recipe?
The best AI adoption journey for companies in 2025—beyond just focusing mechanically on the “10 opportunities”—is about strategic selectivity, speed, and bold reinvention.Â
Strategic focus: own some, partner for others
Top-performing firms do not try to own all AI capabilities. Instead, they:
- Prioritize building proprietary AI where it creates a unique competitive advantage, especially domain-specific AI and core orchestration platforms.
- Leverage third-party technologies and platforms for commoditized AI functions (e.g., infrastructure, foundation LLMs, synthetic data vendors).
- Adopt a hybrid build-buy-partner model to accelerate value capture and manage risk.
Speed and front-Loading matter
Enterprises that acted swiftly are outpacing cautious wait-and-see approaches. Successful adopters move rapidly from pilots to scaled deployment, investing early in data infrastructure and MLOps to avoid costly retrofits.
Conclusions
The best journey is selective ownership combined with strategic partnerships, rapid—but disciplined—scaling, and organizational transformation. Companies that own only critical AI capabilities, integrate AI deeply into business processes, and reskill their workforce while front-loading governance and infrastructure investments will lead. Those who delay or attempt to do everything internally risk lagging.
Jacques Bughin
