The Strategy Industrial Complex: Crafting a Corporate Fantasy in the Age of GenAI

In the rapidly evolving landscape of enterprise software, a strategy industrial complex, consisting of two influential players dominate not by creating solutions, but by orchestrating a self-sustaining cycle of corporate strategy: trend-setting research firms and management consultants. These architects of insight have mastered the art of constructing a parallel universe that thrives in boardrooms and PowerPoint presentations, yet often remains detached from implementation realities. Together, they form a symbiotic alliance that perpetuates corporate narratives around GenAI and enterprise software — generating excitement but often falling short of delivering meaningful outcomes.

The Symbiotic Alliance : At the heart of this phenomenon is a carefully orchestrated partnership between research firms that define industry trends and management consultancies that drive transformation programs. Their collaboration creates a feedback loop fueled by buzzwords, budgets, and the fear of obsolescence, shaping corporate priorities while often prioritizing strategy over execution.

Trend-Setters: Shaping the Corporate Imagination - Research firms act as oracles, issuing reports that define emerging technologies like GenAI. Their publications feature frameworks such as “Hype Arcs” or “Vendor Matrices,” introducing concepts like “GenAI-powered hyperautomation” or “AI-driven digital twins for enterprise resilience.” These terms are prescriptive, framing the future in ways that compel corporate action. For example, positioning GenAI at the “Peak of Inflated Expectations” creates urgency while highlighting potential to transform enterprise software through automated decision-making or personalized customer experiences. These forecasts create a shared language that executives, consultants, and vendors can rally around, but they often gloss over practical challenges like data infrastructure needs or model governance expertise.

Transformation Gurus: Turning Trends into Plans - Management consultancies capitalize on research firm trends, translating forecasts into actionable strategies through workshops facilitated by senior strategists. These sessions bring together executives and IT leaders to “co-create” transformation journeys using exercises like “visioning sessions” and “capability mapping.” In GenAI contexts, workshops might focus on deploying AI-powered chatbots or optimizing supply chains with predictive analytics. While effective at fostering consensus, these sessions often prioritize alignment over feasibility, producing roadmaps that look robust on paper but struggle with technical realities like legacy system constraints or computational costs.

The Self-Reinforcing Cycle: The consultant-analyst ecosystem operates as a perpetual cycle. Workshops generate consensus, leading to multimillion-dollar budgets for GenAI initiatives. These budgets fund consulting fees, software licenses, and vendor partnerships, with enterprise software vendors benefiting from research firm endorsements. However, a 2025 study revealed that 60% of enterprise GenAI budgets are allocated to consulting and planning rather than development and deployment, underscoring the disconnect between strategy and execution. The cycle completes with new reports and whitepapers. Research firms publish case studies highlighting “successful” GenAI implementations based on consultant-led engagements, while consultancies produce whitepapers citing research frameworks to justify recommendations. This mutual reinforcement creates inevitability, compelling continued investment in the narrative without external scrutiny.

GenAI’s Amplification Effect: The rise of GenAI has amplified the consultant-analyst dynamic as enterprises grapple with transformative yet complex technology. GenAI’s ability to generate text, images, and code promises to revolutionize enterprise software, but its rapid evolution and technical demands make it prime for consultant-analyst treatment. Research firms position GenAI as a game-changer with terms like “GenAI at the edge” and “AI-augmented software engineering,” creating urgency while downplaying implementation challenges. Consultancies offer transformation programs promising to harness GenAI’s potential through chatbots or AI-driven analytics, sold as strategic imperatives with little discussion of model bias, data privacy, or computational costs.

The Enterprise Software Conundrum : Enterprise software’s complex architectures and legacy systems provide fertile ground for this ecosystem. Unlike consumer applications, enterprise software involves multi-year implementations, cross-departmental coordination, and significant customization. GenAI adds complexity requiring enterprises to rethink data pipelines, governance frameworks, and user interfaces. Research firms simplify this through vendor matrices providing seemingly objective AI platform selection guides. Consultancies use these rankings to recommend vendors aligning with transformation narratives. However, many projects fail due to misaligned expectations and inadequate technical foundations, creating gaps between glossy workshop roadmaps and engineering realities. The consultant-analyst ecosystem excels at generating activity — reports, workshops, budgets — but its impact on outcomes is questionable. By prioritizing alignment over execution, it diverts resources from practical innovation. Engineers and product teams, best positioned to implement GenAI solutions, are sidelined in favor of strategists and advisors. Perhaps most damaging is the erosion of trust in GenAI as transformative technology. When hyped initiatives fail to deliver, executives become skeptical of future investments, slowing adoption of genuinely impactful solutions and stifling innovation.

Breaking the Cycle : To escape this fantasy, enterprises must shift focus from narrative to execution through key changes:

Prioritize Internal Expertise: Invest in building internal GenAI and software engineering capabilities rather than relying solely on external advisors. Hire data scientists, AI engineers, and DevOps specialists who can translate strategic goals into technical realities.

Demand Measurable Outcomes: Tie transformation programs to specific, measurable KPIs like cost savings, revenue growth, or process efficiency. Hold consultants and vendors accountable for tangible results, not presentations.

Foster Cross-Functional Collaboration: Create teams including engineers, product managers, and domain experts to ensure solutions are both feasible and impactful.

Embrace Iterative Development: Adopt iterative approaches starting with small-scale pilots and scaling based on proven results, aligning with agile principles, prioritizing rapid experimentation and continuous improvement.

The Strategy Industrial complex domination is a masterclass in corporate theater, weaving compelling fantasy that thrives on buzzwords, budgets, and fear of falling behind. In GenAI and enterprise software contexts, this consultant analyst complex generates excitement but often fails to deliver tangible outcomes. By understanding this cycle’s mechanics — reports begetting roadmaps, roadmaps begetting workshops, workshops begetting budgets, and budgets begetting reports — enterprises can break free. The real world, where engineers build, customers engage, and products ship, demands focus on execution over alignment and outcomes over narratives. As GenAI continues reshaping enterprise software, enterprises must look beyond glossy decks and strategic trends to invest in people, processes, and technologies driving meaningful change. Only then can they move from the consultant-analyst fantasy to the reality of innovation.

Labels: GenAI, The Strategy Industrial Complex