Precision segmentation: How consulting firms can target clients’ sector and functional AI budgets
As autonomous agentic AI tools move from experimental pilots to live enterprise workflows, clients have arrived at a critical inflection point. The era of low-stakes proofs of concept (PoCs) and broad cost-cutting measures has given way to complex, high-stakes business transformation. And not before time! Enterprise leaders should no longer be asking what AI services can do; instead, they should be scrambling to operationalise them securely, integrate them deeply across the enterprise, and generate measurable top-line value from them.
Despite broader economic headwinds and tightened technology budgets, client demand for expert consulting guidance remains robust. As highlighted in our latest Market Trends report, The Global Technology Consulting Market in 2026–27, spending on technology consulting is projected to grow by 7.5% in 2026. This growth is anchored by a persistent boardroom mandate: CEOs and CFOs are under pressure to turn AI investments into enterprise-wide capability.
As market demand expands, consulting firms need to make sure they are hitting the mark. Enterprise clients do not buy generic AI strategy; they buy solutions to execution bottlenecks. In general, they are struggling with three primary hurdles:
- Speed of change and governance: Navigating the sheer pace of technological change alongside complex regulatory standards.
- Data readiness: Unifying legacy systems and poor underlying data architecture so enterprise data is fit for advanced AI applications.
- Talent constraints: Overcoming internal skill deficits to ensure workforces can operate alongside AI systems.
To capture this growing market, consulting leaders must acknowledge that an enterprise’s needs vary radically depending on its industry sector, the seniority of the buyer, and the specific functional buyer holding the budget.
Industry sectors demand distinct AI playbooks
Our H2 2026 Market Trends survey data across key industries reveals contrasts in client needs for external support. A one-size-fits-all approach ignores the operational realities of different sectors:
- Energy & resources: Organisations in this sector are heavily focused on managing the human side of automation. Their top priority is identifying skill gaps in AI literacy and managing cultural shifts as automated systems take over core operations. They also show the highest appetite for developing proprietary AI tools tailored to complex industrial operations.
- Financial services: Here, foundational infrastructure takes centre stage. Financial institutions prioritise fixing fractured data architectures, establishing robust data sovereignty, and mitigating risk. Facing strict regulatory scrutiny under frameworks like the EU AI Act, these are the most likely buyers to hire consultants primarily for risk management or AI ethics.
- Healthcare & pharma: Given intense pressure on margins, these buyers demand projects with a clear ROI. They seek consulting partners who can identify and validate high-impact enterprise use cases before committing capital.
Functional segmentation: winning the internal buyer
Even within a single enterprise, buying behaviors diverge sharply by job function and seniority level. C-suite leaders—particularly CEOs—are absorbed by organisational design, assessing how AI will alter their workforce composition, internal culture, and strategic direction over the next three to five years.
Further down into functional leadership, priorities split between technical foundations and commercial enablement:
- IT & operations: Data governance is their absolute focus. From the Market Trends technology report, we understand that 60% of enterprise tech buyers identified poor data quality and governance as the single largest bottleneck to scaling AI and agentic tools. IT leaders know that without clean, structured, and secure data, AI investments fail.
- Finance: While finance executives are most likely to want external support in identifying where their organisation has AI skill gaps, they are also increasingly stepping in as corporate risk guardians. They are more likely to be focused on ensuring AI deployments comply with emerging legal frameworks, guarding against model hallucinations, identity fraud, and liability from autonomous agents.
- Sales & marketing: Commercial leaders are less concerned with underlying data hygiene and far more interested in speed. They represent the primary buyers for bespoke, proprietary agentic tools designed to automate customer engagement and drive revenue.
What consulting leaders must do
The race for enterprises to adopt AI throws up a range of struggles: data architecture, internal talent shortages, and agreement on how best AI can meet a specific business challenge and generate ROI. Clients need consulting firms to work with them as co-delivery partners who build solutions alongside their internal teams and leave behind permanent capability.
Those firms that can align their offerings with sector-specific and functional realities will move from tactical vendors to indispensable strategic partners. Pushing a data governance outcome to a chief commercial officer is likely to fail, just as offering an automation tool to a chief strategy officer may miss the point.
What can firms do next?
To dive deeper into buyer trends, market sizing, and growth projections across sectors, speak to us about our upcoming research. We can help you build a clear picture of your clients’ AI needs through in-depth surveys and direct conversations with decision makers. You can also access our recently published Global Technology Consulting Market in 2026–27.