The rise of agentic AI transforms enterprise software economics

The shift from conversational to autonomous agentic AI is redefining market dynamics, pricing models, and strategic partnerships as enterprises seek measurable outcomes and efficient workflows within a rapidly evolving technological landscape.

The technology industry is moving decisively from a conversational AI era, defined by human-to-machine dialogue, into an agentic era in which software systems autonomously plan, execute and verify multi-step workflows across enter...

prise environments. What began as an exploration of what large language models can do has matured into a commercial reckoning: vendors and customers are now focused on unit economics, measurable outcomes and the orchestration of ecosystems rather than raw model scale.

Industry adoption and market size underscore the shift. According to McKinsey, 12% of enterprises had already deployed agentic AI across multiple functions and half were planning significant investments within six months, driving substantial increases in AI budgets. Industry studies project rapid expansion: a Berkeley report forecasts the autonomous agents market growing at a compound annual rate above 40% and reaching roughly $103.3 billion by 2034, while other estimates place adoption rates and budget allocations for agentic capabilities in the majority of organisations by the mid-2020s. Government and industry surveys cited in public sources also report widespread pilot activity and allocation of material portions of AI spend to agentic systems.

That commercialisation has reshaped monetisation. Early consumption models, which billed by tokens or compute usage, proved transparent but volatile for enterprise buyers and inconsistent for vendors. The industry is shifting toward outcome-based pricing, enterprise licences and hybrid structures that combine a predictable base fee with success fees or overage charges. Platform-as-a-Service approaches capture value at the orchestration layer, licensing the software that connects models, agents and enterprise APIs. According to the analysis in CIO Applications, Results-as-a-Service and performance-linked licensing align vendor incentives with customer ROI: when revenue depends on completed tasks, platforms are motivated to reduce unnecessary compute and improve agent efficiency.

Managing the cost structure of agentic reasoning has become a central engineering and commercial challenge. Autonomous agents impose a “reasoning tax”, computational overhead from multi-step planning, iterative reflection and self-correction, that can erode margins if matched with one-model-fits-all deployment. Vendors are adopting hierarchical architectures that rightsize compute: large, high-parameter models handle strategic planning while smaller, specialised “nano” models execute routine subtasks. This mix reduces average cost per task without sacrificing capability. The lead analysis stresses that as foundation models commoditise, proprietary data assets, industry-specific workflows and reliable orchestration become the key competitive moats.

Capital allocation is also shifting through deployment models. More regulated industries are embracing on-premise and edge deployments that transfer much of the raw compute burden to customers, enabling vendors to focus on higher-margin software licensing, orchestration and maintenance while meeting compliance and data governance demands. McKinsey and sector analyses indicate that tech services will see both displacement of legacy IT budgets and new growth opportunities servicing agentic implementations; forecasts suggest the total tech services market could expand by mid-single digits annually through to 2030 as providers adapt.

Platform economics hinge on interoperability and network effects. Agent value scales with integration into CRMs, ERPs and specialised databases; platforms that function as central orchestration hubs can capture disproportionate value by managing agent interactions and workflows rather than selling discrete agents. IDC projects material gains in supply-chain outcomes from agentic orchestration, estimating meaningful revenue uplifts and improved customer and partner satisfaction for large enterprises that adopt these platforms. Strategic partnerships and trust infrastructure play a complementary role: market commentary shows investment in interoperability standards, verification tooling and governance to reduce friction across ecosystems.

Practical deployment patterns reflect a pragmatic balance between automation and human oversight. The most economically sustainable platforms automate routine workflows while routing complex, high-risk edge cases to human experts. This human-in-the-loop approach reduces R&D and operational expense compared with pursuing full autonomy for every scenario, while delivering tangible productivity gains for customers. Industry reports and academic studies alike note that measurable task completion and workflow reliability, not headline model metrics, are becoming the primary basis for customer value and vendor pricing.

Risks and trade-offs remain. High compute costs, the complexity of enterprise integrations, and the need for dependable data governance create barriers to scale. The competitive battleground is shifting from raw model size to control over data, verticalised workflows and the ability to operate as the orchestration layer across heterogeneous enterprise systems. According to market research, organisations that can combine efficient architectures, interoperable ecosystems and outcome-aligned commercial models will be best positioned to convert rapid adoption into durable margins.

The agentic transition reframes software economics. Where the early AI era rewarded capability demonstrations and usage growth, the deployment phase rewards outcomes, integration and efficiency. Firms that align pricing with business results, optimise infrastructure across model tiers, and build trustworthy, interoperable platforms stand to capture the productivity gains that enterprises now expect from digital co-workers. The result is a new commercial discipline in which success is measured by autonomous delivery of business value rather than by raw compute or user seats.

Source: Noah Wire Services