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Read articleAI is beginning to rewrite software economics as agents reduce the importance of traditional interfaces and force vendors to prove where durable product value actually resides
We see technology companies balancing rapid AI growth with rising infrastructure costs, changing monetization and a more fluid competitive boundary between models, platforms and applications.
Technology and software enter September 2026 with enterprise AI spending still expanding rapidly but with customers demanding clearer economic returns. Agentic systems can increasingly operate across applications without users engaging directly with every interface, challenging seat-based SaaS economics and the value of software layers built primarily around workflow navigation. At the same time, model providers are moving toward applications while established software companies embed agents and expose proprietary data and actions. The competitive question is shifting from who can add AI fastest toward who controls indispensable data, workflows, distribution or infrastructure and can monetize that position sustainably.
Focus
AI is forcing software companies to question the economics of software itself
Agents can automate work previously performed inside applications, putting pressure on user-based pricing and established SaaS product boundaries.
Read nowStrategic Challenges
What happens to SaaS when AI reduces the number of humans using software?
The challenge is redesigning pricing and product architecture when autonomous agents increasingly perform tasks instead of individual users.
Read nowStrategic Impacts
Agentic AI is shifting software value from interfaces toward completed work
Products may increasingly compete on outcomes and orchestration rather than the number of seats, screens or workflow steps they support.
Read nowObserved Patterns
Software companies often add AI features while protecting pre-AI pricing assumptions
Copilots can improve the product while simultaneously undermining seat-based economics by reducing the human labor using it.
Read nowStrategic Challenges
What happens to SaaS when AI reduces the number of humans using software?
The challenge is redesigning pricing and product architecture when autonomous agents increasingly perform tasks instead of individual users.
Read nowStrategic Impacts
Agentic AI is shifting software value from interfaces toward completed work
Products may increasingly compete on outcomes and orchestration rather than the number of seats, screens or workflow steps they support.
Read nowObserved Patterns
Software companies often add AI features while protecting pre-AI pricing assumptions
Copilots can improve the product while simultaneously undermining seat-based economics by reducing the human labor using it.
Read nowIndustry Challenge
Software companies must grow AI revenue before compute costs and disruption catch up
Technology and software companies are racing to embed AI into products while facing higher compute costs, rapidly changing customer expectations and the risk that agentic systems undermine established SaaS interfaces and seat-based pricing. Competitive advantage can shift quickly as foundation models improve and switching costs fall in parts of the stack. The challenge is to translate AI adoption into durable economics. Vendors need to decide where proprietary models matter, where third-party infrastructure is sufficient and how pricing should evolve as customers pay for outcomes.
Future Outlook
Agentic software will shift value from interfaces toward orchestration and outcomes
The next software architecture will increasingly include autonomous agents that execute work across multiple applications, reducing the importance of traditional screens and workflows. This can create new platform layers around identity, data, orchestration, observability and governance. Product companies will need to redesign both architecture and monetization as usage becomes more machine driven. The strongest vendors will combine differentiated data and workflows with flexible model choices, cost transparency and enterprise-grade controls. AI-first products may expand software demand.
Market Outlook
AI and infrastructure spending are driving exceptional technology-market growth
Technology markets remain one of the strongest areas of global investment in 2026. Worldwide IT spending is forecast above $6 trillion, with data-center systems, cloud infrastructure and AI among the fastest-growing categories. AI spending itself is rising rapidly, while enterprise software faces a structural shift as agentic systems begin to challenge conventional SaaS licensing and interfaces. The market is expanding, but economics are diverging: infrastructure providers benefit from capacity demand, while software vendors face pressure to prove measurable AI value and manage compute costs.
POV
AI may destroy more software categories than it creates
When an agent can perform the workflow directly, some applications risk becoming infrastructure rather than destinations users need to open.
Read nowOur approach
Read technology markets through the shifting control points between models, data, applications, infrastructure and distribution
Our approach� begins with the customer outcome a technology enables and traces which layers of the stack are necessary to produce and monetize that outcome. We examine product differentiation, data access, switching costs, developer ecosystems, infrastructure economics and distribution together because AI can move value between these layers rapidly. Agentic workflows are assessed for their ability to bypass or commoditize existing interfaces. We then test business and product models against alternative AI architectures, helping distinguish durable control points from features whose value may migrate toward models, platforms or customers themselves.
The data and estimates presented are indicative and intended for illustrative purposes. Actual outcomes may vary based on each company’s specific context, market conditions, operating model, implementation choices, and the quality and consistency of execution, including actions undertaken by the client.
Keypillars
Explore the key pillars that define this capability and shape how we create focused, measurable business impact.
Technology economics
Examines recurring revenue, infrastructure cost, adoption, retention, scaling, and monetization across software and AI business models
Product ecosystems
Connects platforms, developers, cloud infrastructure, data, applications, and distribution across rapidly evolving technology markets
Innovation dynamics
Tracks AI models, computing architectures, open-source ecosystems, regulation, and changing technology adoption across industries
Can your technology business stay ahead when products, economics and competitive boundaries change this quickly?
Get in touch with our Technology, software and artificial intelligence team to address growth, product, technology and operating challenges.
Get in touchStrategic Framework
Explore our Strategic Framework
Autonomous AI agents are changing how work is executed, enabling adaptive processes that respond intelligently to changing conditions instead of following predefined rules.
Discover our framework01. Map ecosystem
Assess software categories, AI models, infrastructure, developers, platforms, customers, and value pools
06. Track evolution
Monitor model progress, software spend, adoption, developer activity, regulation, funding, and competitive launches
05. Define moves
Prioritize products, AI capabilities, platforms, ecosystems, monetization, partnerships, and market expansion
02. Trace disruption
Examine AI capability, cloud economics, open source, platform shifts, regulation, and changing buyer behavior
03. Assess position
Evaluate product portfolio, technology differentiation, distribution, data, ecosystem, retention, and unit economics
04. Model pathways
Test adoption, pricing, compute costs, platform shifts, regulation, competition, and technology trajectories
How we help
Support technology companies in building defensible growth as AI changes products, monetization and the layers of the software stack where value accumulates
We help software and AI businesses assess markets, product portfolios and competitive control points; redesign monetization as usage and agentic models evolve; and determine where data, models, applications or ecosystems create durable differentiation. Support can include platform strategy, growth, pricing, operating-model transformation, AI-native products, partnerships, M&A and capital prioritization. We also help companies identify where existing revenue models are exposed as agents alter how customers interact with software.
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Technology growth strategy
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Software portfolio strategy
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SaaS growth strategy
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AI product strategy
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AI platform strategy
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Cloud product strategy
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Developer ecosystem strategy
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Product-led growth
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Enterprise software GTM
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Software pricing strategy
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Cloud consumption economics
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Customer retention and expansion
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Technology R&D strategy
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Engineering productivity
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Technology platformization
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Technology cost transformation
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AI infrastructure strategy
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Technology M&A strategy
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Technology ecosystem partnerships
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Responsible AI operating model
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Technology talent strategy
Explore our FAQs
Find answers to the most common questions about this service, including key features, processes, and practical considerations. Explore our FAQs for additional insights and guidance.
AI, cloud platforms and changing distribution models are altering product differentiation, development economics and customer expectations.
Assess where AI changes customer value, product architecture and cost before adding features without a clear economic role.
It needs customer value and pricing that can support compute, development and operating costs as usage scales.
Compare customer need, strategic differentiation and economics while accounting for the speed at which technology can make assumptions obsolete.
When access to customers, infrastructure or core technology depends heavily on providers whose terms or capabilities can change.
Evaluate demand, localization, regulation, distribution and support economics rather than assuming digital products scale uniformly.
Proprietary capabilities, distribution, data, product integration and economics matter more than access to widely available models alone.
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