The AI 7th Wave
AI’s next wave demands bold pivots: empower teams with AI literacy, evaluate AI-native platforms, and safeguard your market ecosystem dominance
The technology sector stands on the brink of its subsequent great upheaval. Just as the rise of personal computing, the internet, mobile devices, and cloud platforms each redefined the competitive landscape, modern artificial intelligence—particularly generative and agentic forms—is poised to catalyse a “Seventh Wave.” Unlike earlier shifts, this transformation combines leaps in algorithmic capability with massive infrastructure demands, broadening its reach from core enterprise applications to consumer experiences, hardware architectures, and global services.
Enterprise Software: From Custom Suites to AI-Native Platforms
Traditional enterprise software faces three longstanding challenges: escalating licensing costs, vendor lock-in, and rigid architectures that inhibit agility. Now, generative AI threatens to upend this model in three ways:
Automated Development: AI code-generation tools (e.g., “TuringBots”) promise to undercut low-code/no-code platforms by synthesizing production-ready modules on demand.
AI-First Replacements: Beyond mere automation, agentic AI systems can reimagine ERP and CRM workflows, offering dynamic, conversational interfaces to enterprise data.
Continuous Adaptation: Unlike static legacy suites, AI-native systems learn from user interactions and evolving business processes, thereby reducing customisation overhead and the need for perpetual consulting fees.
While CIOs are intrigued by promises of greater responsiveness and lower TCO, internal scepticisms run deep. Development teams and IT leaders, invested in decades of legacy expertise, may resist wholesale migration. Yet, with generative AI’s productivity potential estimated at $2.6–4.4 trillion annually across corporate use cases, companies that delay risk ceding ground to more nimble challengers (source: mckinsey.com).
Hardware: The Compute Arms Race Intensifies
AI’s voracious appetite for compute cycles is already reshaping the hardware market:
Accelerated Server Upgrades: Gartner forecasts spending on specialised AI servers will double traditional server investments in 2025, reaching $202 billion for AI-optimised systems alone (source: crn.com).
Generative AI Budgets Soar: Global expenditure on generative AI is projected to hit $644 billion in 2025, a 76% year-over-year increase, underscoring the surge in training and inference workloads (source: venturebeat.com).
Evolving Architectures: While GPUs retain a dominant position, emergent accelerators (TPUs, neuromorphic chips) and memory innovations (HBM3) aim to mitigate data movement bottlenecks, a critical factor as model sizes continue to swell.
This hardware renaissance will sustain 8–10% annual growth in computing markets over the next half-decade, fueling both cloud-scale data centres and increasingly capable edge devices.
Technology Services: Transformation and Turbulence
Large consultancies face a dual mandate: defend legacy software engagements while building AI-centric practices. After overextension in the 2021–22 boom, firms like PwC and Deloitte must pivot:
Short-Term Disruption: Existing maintenance and upgrade contracts for ERP/CRM suites are expected to decline as clients reassess vendor roadmaps.
AI Enablement: The demand for AI governance, data pipeline modernisation, and model operations expertise will drive a rebound. McKinsey estimates that AI-accelerated R&D alone could unlock $360–560 billion in annual enterprise value (source: mckinsey.com).
Service Growth Outlook: With overall IT spend predicted to rise 9.8% in 2025—mainly on AI initiatives—technology services may see growth rates climb from ~3.6% in 2025 to 5–6% annually through 2030 (source: technologymagazine.com).
Success will favour firms that move beyond systems integration toward end-to-end AI solution delivery and change management.
Networks and Infrastructure: The Circulatory System of AI
An estimated 70% of AI workloads will reside in private and public clouds, placing stress on global networks:
Bandwidth Demands: The constant flow of prompts and model outputs necessitates upgrades to backbone capacity, reinforcing investment cycles from telecom incumbents.
Edge Computing: Low-latency applications (e.g., real-time inference in manufacturing or autonomous vehicles) will spur hybrid architectures combining edge nodes with centralised training hubs (source: ft.com).
5G/6G Integration: Next-generation wireless standards will be critical for distributed AI services, creating new revenue streams for carriers and equipment manufacturers.
The Consumer Tech Front: Defensive Manoeuvres and New Frontiers
Big Five Dynamics
Google will leverage its search and ads moat by integrating AI-driven relevance engines and bundling cloud services, while selectively acquiring promising startups.
Meta can leverage its trove of user-generated content to refine models, but it must guard against “AI fatigue” and reputational risks in moderation.
Amazon aims to re-architect commerce around AI-powered personal shopping assistants and supply chain optimisation, leveraging AWS as a launchpad for novel retail experiences.
Microsoft continues its dual strategy of investing in OpenAI and embedding Copilot-style agents across Office and Azure, unafraid to cannibalize legacy franchises.
Apple remains the outlier, with AI features gradually being integrated into iOS and macOS; a marquee acquisition—potentially of Anthropic or Mistral—seems inevitable to close the gap.
The Second-Mover Advantage?
While first-movers capture buzz, a growing chorus argues for deliberate, regulated AI adoption. Risks surrounding transparency, security, and ethical lapses suggest that “fast follower” strategies may yield better long-term outcomes.
Strategic Imperatives for CIOs
Discern Genuinely AI-Native Solutions: Vet vendor claims rigorously; look for models fine-tuned on domain data and robust MLOps pipelines.
Elevate AI Literacy: Establish cross-functional “AI centres of excellence” to bridge business and technical teams, ensuring governance and compliance.
Pace Major Migrations: Delay large-scale ERP/CRM overhauls until AI-competent alternatives prove their ROI—likely emerging in 2026–27.
The Seventh Wave is not a mere extension of cloud or mobility—it is a fundamental shift in how technology creates and captures value. Incumbents armed with Buy, Block, Pretend, and Link tactics may delay disruption, but the economic and competitive pressures unleashed by AI’s unprecedented productivity gains are irresistible. Leaders who combine strategic patience with bold experimentation will navigate this wave most successfully.