Best of LinkedIn: Strategy & Consulting CW 22/ 23

The strongest signals are clear: enterprises are no longer debating whether AI matters, but how to scale it responsibly, embed it into operating models, and turn it into measurable advantage. Consulting firms are positioning themselves around deployment, governance, resilience, and transformation ownership. The two-week narrative is less about technology hype and more about the strategic systems needed to make AI, data, infrastructure, and organizational change work at scale.

Date

June 11, 2026

Strategy & Consulting

Thomas Allgeyer

Methodology: Every two weeks we collect most relevant posts on LinkedIn for selected topics and create an overall summary only based on these posts. If you´re interested in the single posts behind, you can find them here: https://linktr.ee/thomasallgeyer. Have a great read!

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Enterprise AI Execution

AI is increasingly framed as operating infrastructure rather than a standalone productivity tool
The strategic question is shifting from “what can AI do?” to “how can AI be deployed responsibly across core operations?”
Enterprise AI bottlenecks are described as execution-related, especially decision speed, governance, prioritization, funding, ownership, and operating model readiness
AI value is linked to resilience and competitive advantage, not only ROI
Companies that learn faster, scale responsibly, and align leadership around clear AI priorities are positioned to gain the strongest advantage

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Agentic AI and Enterprise Architecture

Agentic AI was one of the most visible product and architecture themes
Snowflake Summit highlighted the move from experimental AI toward the “Agentic Enterprise,” with context, orchestration, and observability as the foundation
SAP Sapphire signalled a shift from passive enterprise software toward autonomous enterprise execution
SAP’s agent-to-agent AI collaboration, Joule Studio 2.0, and new enterprise agents across ERP, finance, HR, procurement, analytics, and project management stood out as major product signals
Model selection was reframed as a workflow and cost decision, not a generic race to pick the single “best” model
Technology architecture was described as one connected system, where decisions across layers must be made together rather than in isolation

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New Partnerships and Platform Moves

KPMG and Anthropic announced a global alliance embedding Claude into KPMG Digital Gateway, positioning AI directly inside client delivery workflows
KPMG’s “Digital Gateway Powered by Claude” signals a move from AI as an external assistant toward AI as part of the consulting delivery platform
PwC expanded its Anthropic alliance with Claude Code, Claude Cowork, a joint Center of Excellence for agentic AI, and cybersecurity response use cases
PwC’s AI positioning was reinforced through Forrester recognition around AI strategy, governance, testing, controls, and Trust AI capabilities
Bain invested in the OpenAI Deployment Company, with focus on deploying AI into critical operations, private equity portfolios, automation, product development, and supply chain optimization
BCG announced an Anthropic partnership linked to a broader $500 million commitment by 2030 to use AI for social impact across education, healthcare, workforce development, and poverty reduction
SAP announced up to €300 million investment in France for sovereign cloud and trusted business AI, including a sovereign cloud and AI region near Paris
Kuwait’s KD 825 million telecommunications network development project was positioned as a strategic digital infrastructure milestone supporting connectivity, smart cities, cloud, and AI-enabled growth

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Governance, Trust and Assurance

AI governance emerged as a board-level strategy topic
Boards were urged to understand the hidden assumptions AI systems introduce into decisions, incentives, values, and risk appetite
Responsible AI was framed as a condition for scaling, not a post-deployment control layer
Independent testing and measurement were positioned as essential because consistent AI outputs are not automatically correct
AI assurance, controls, and defensible governance were repeatedly linked to enterprise trust
Testing was reframed as a tool for informing risk decisions rather than creating false confidence

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Consulting Model and Talent

Consulting is not positioned as being replaced by AI, but structurally reshaped by it
Human judgment, client relationships, industry context, and execution discipline remain the main sources of consulting value
AI reduces the advantage of scale alone, making proprietary knowledge, expert networks, and applied domain depth more important
Junior consultant development was raised as a strategic risk, with premature AI dependency described as creating “psychological debt”
The future consulting model is framed as AI-assisted consulting, where technology increases leverage but does not replace judgment
Forward-deployed engineering and hands-on AI bootcamps signal a shift toward more technical, build-oriented consulting delivery
Leadership development, continuous learning, and talent reinvention are becoming part of the consulting firms’ own transformation agenda

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Strategy Under Volatility

Volatility was described as the new operating environment rather than an exceptional disruption
Adaptability was positioned as more relevant than static resilience, especially for European businesses
Supply chain resilience was framed as a strategy and competitive advantage topic, not an operations issue
Complexity in supply chains was linked to governance failure, not only external disruption
Geopolitical risk was positioned as something central banks and corporations must embed into scenario planning
Geoeconomic fragmentation was framed as a measurable economic cost, with implications for inflation, growth, and strategic planning
Strategic shocks are increasingly relevant for corporate strategy, not only military or geopolitical analysis

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Financial Services, Insurance and Risk

Banks were described as keeping the customer account but losing the “moment of decision” to AI assistants
Banking transformation was framed as requiring clearer ownership and modernization, not simply more controls
Climate resilience in banking was presented as a known strategic need, but with institutional process gaps still unresolved
Insurance was positioned as a key public-private resilience actor, helping governments and businesses anticipate disruption
Fraud risk was highlighted as underestimated, with controls and speak-up culture positioned as enterprise priorities
Actuarial work was framed as being redesigned by AI rather than replaced by it

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Data, Metrics and Performance Management

Cohort analysis was positioned as a sharper SaaS performance lens than total revenue
KPI selection was framed as a core executive and analytics challenge, with poor metric design creating low adoption or decision paralysis
Data foundations, semantic context, lineage, and clean dashboards were presented as prerequisites for useful decision-making
Compensation models in professional services were highlighted as a hidden performance lever affecting behavior, retention, and value creation
Sustainability was positioned as a business value driver rather than a trade-off with growth
AI in tax delivery was framed as shifting work toward higher-value activities

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Infrastructure, Sovereignty and Frontier Technology

AI constraints were framed as physical as well as digital, with resilient infrastructure becoming central to AI scaling
Sovereign cloud and trusted AI gained strategic relevance through SAP’s France investment
Quantum computing was positioned as moving from research topic to national strategic priority following major US government investment
Telecommunications infrastructure was highlighted as a foundation for cloud, AI, smart cities, and national digital growth
Architecture decisions were framed as strategic choices that shape long-term flexibility, resilience, and execution capability

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Leadership and Change

Transformation success was linked to honest disagreement, genuine ownership, and leaders willing to work across organizational boundaries
Employee AI engagement was described as dependent on leadership clarity after the initial excitement fades
Strategic agility and innovation were positioned as leadership requirements in an age of rupture
Leaders are expected to navigate contradiction, uncertainty, stakeholder complexity, and trust at the same time
The strongest change narratives connected technology ambition with clear operating ownership and measurable delivery impact

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