5 Ways to Optimize Application Sourcing Costs in an AI-Enabled Market
Application sourcing —software, support, labor and managed services — presents a prime opportunity to reduce spend and improve value realization.
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Information Technology
Application Service Areas
- Application Development
- Application Management
- Application Support
- DevOps
- ERP/CRM
- Testing and QA
- SAP
- Database
- Infrastructure Management
- Mobility
- Network
- Servers
- Service Desk
- Storage
- Workplace
Specialty Service Areas
- Cloud
- Consultancy
- Cybersecurity
- Data Analytics
- Digital and Automation
- IT Management and Cross Functions
- Project Management
- Service and Systems Integration
- Strategy Management
- Standard Role
- Years of Experience
- Core Skill/Domain
- Service Delivery Country
- Staff Location (Client vs. Vendor)
Finance and Accounting
Order to Cash Service Areas
- Accounts Receivable
- Billing
- Cash Application
- Credit and Collections
- Dispute and Deduction
- Order Management
- Accounts Payable
- Travel and Expenses
Record to Report Service Areas
- Cost Accounting
- Financial Planning and Analysis
- Fixed Assets
- General Accounting
- Customer Service
- Standard Role
- Years of Experience
- Core Skill/Domain
- Service Delivery Country
- Staff Location (Client vs. Vendor)
Procurement
Service Areas
- Category Management and Support
- Requisitioning
- Strategic Sourcing
- Tactical and Spot Buying
Key Parameters
- Standard Role
- Years of Experience
- Core Skill/Domain
- Service Delivery Country
- Staff Location (Client vs. Vendor)
Human Capital Management
Service Areas
- Benefits Administration
- HR Administration
- HR Systems Integration
- HR Technology
Key Parameters
- Standard Role
- Years of Experience
- Core Skill/Domain
- Service Delivery Country
- Staff Location (Client vs. Vendor)
Industries
Industry Groups
- Business Services
- Consumer Packaged Goods
- Energy
- Financial Services
- Healthcare and Pharmaceuticals
- Insurance
- Manufacturing
- Media and Telecom
- Public Sector
- Retail
- Travel, Transportation, and Leisure
Sample Rate Card Dashboard
The market has moved from ambition to accountability.
AI investment is accelerating, but results remain uneven. Only one in four initiatives is meeting revenue impact expectations, at an average spend of $1.3M per use case. Enterprises are no longer asking whether AI works. They are being asked to prove that it pays.
What We Deliver
AI strategy, governance and intelligence, built for execution.
Autonomous Enterprise
Operations built for autonomous execution, not retrofitted for it.
We help you identify where AI agents deliver the most value, restructure workflows around them and build the accountability models that keep autonomous execution auditable. The enterprises that win won't be the ones that reacted. They'll be the ones that designed for it first.
Autonomy-Level Pricing
Pricing that reflects how AI-enabled services are actually delivered.
We give enterprises transparent, benchmarkable pricing models that tag each resource unit with the autonomy level used to deliver it. As AI capability advances, your pricing keeps pace. Both buyers and providers can quantify what that progress is worth.
AI & Software Intelligence
Build-versus-buy decisions grounded in what AI is actually delivering.
We bring analysis of more than $2.6 billion in tracked AI spend to every sourcing decision. Procurement, technology and finance leaders get the independent intelligence to rationalize vendor portfolios and hold providers accountable to measurable outcomes.
AI Governance
Governance that accelerates AI adoption rather than constraining it.
We embed controls at the point of data creation, define accountability for autonomous actions and build adaptive frameworks that keep pace with AI without impeding it. Enterprises that get this right don't just manage risk. They build the trust that lets them scale faster.
AI Strategy
AI investment aligned to where impact is most achievable.
We ground strategy in research across 2,400 enterprise use cases, aligning investment to where impact is proven and designing the data, talent and governance foundations that move AI from pilots into the workflows that drive commercial results.
AI Maturity Index
A clear view of where you stand and a roadmap to where AI starts delivering.
We benchmark your AI readiness against peers across 75 countries, identify the dimensions holding you back and give you a personalized roadmap to close the gap.
The market today
Enterprise AI has moved out of IT and into the revenue line.
AI investment is shifting decisively toward revenue-generating functions. CRM automation, sales enablement and forecasting have replaced chatbots and IT productivity tools as the leading use case priorities, reflecting enterprise recognition that productivity gains alone do not satisfy board-level scrutiny. At the same time, use cases in production have doubled since 2024, and the portfolio is diversifying rapidly, with over 300 distinct function and industry-specific use cases now in active deployment.
ISG research across 2,400 enterprise use cases shows that the strongest AI returns are currently concentrated in compliance, risk management and quality control, not in the growth and cost outcomes most enterprises originally set out to achieve
The gap between where enterprises are investing and where AI is actually delivering is the defining commercial tension of 2025. Organizations that close it by targeting functions with structured, revenue-attributable data and clear ROI measures will establish performance benchmarks that compress the window for competitors still cycling through pilots. The standard is being set now.
Where enterprises are feeling the pressure
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Business outcomes are lagging AI ambitionEnterprises are scaling Al faster than they are realizing value from it. The number of use cases in production doubled between 2024 and 2025, yet only one in four initiatives is meeting revenue impact expectations, and broad cost savings remain elusive. At an average spend of $1.3M per use case, the ROI gap is sharpening board-level scrutiny and forcing a harder question: are we building Al for impact, or for activity?
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Data infrastructure exposing deferred investmentAl does fail in isolation. It fails on the foundations beneath it. Most enterprises are running modern Al on architectures built for reporting and compliance. Generative and agentic Al demand real-time contextually rich, governed data at the point of use. Without it, pilots stall and value dissipate before it reaches the business.
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The barrier to scale is organizational, not technicalOrganizational readiness as the bigger constraint on Al adoption, not talent or tooling. Workflows haven't been redesigned. Decision rights haven't shifted. Enterprises that treat Al as a pure technology deployment, without investing in the human side of adoption, consistently report underwhelming ROI.
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Agentic AI is outpacing governanceAs Al moves from generating outputs to executing tasks autonomously, the governance gap widens. Agentic Systems introduce a new class of risk that static compliance frameworks were never designed to catch. Governing what Al does, not just what it produces, is now a business-critical requirement.
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