Key takeaways
Margin pressure
AI value discipline
Delivery performance
Technology barriers
Workforce readiness
Key challenges in IT outsourcing
The IT outsourcing market is facing a decisive test: can providers turn rapid AI investment into measurable client value while controlling costs and improving delivery? Rising AI spending, legacy-system barriers, workforce skill gaps, and persistent service failures are increasing pressure on providers to strengthen productivity, innovation, and operational resilience.
Outsourcing value under margin pressure
Financial value is under increasing scrutiny as clients reassess external IT spending and providers face stronger pressure to demonstrate cost efficiency and innovation. In the UK and Ireland, 32% of organisations expect to increase external IT spending compared with 18% planning reductions, while 70% value outsourcing for access to emerging technologies, yet 38% consider insourcing more cost-effective and 27% question value for money. Provider performance is also under pressure, with 43% of organisations saying vendors fail to provide sufficient innovation and strategic challenge, while AI is prompting some clients to seek 25–30% contract price cuts. Revenue growth remains constrained, with India’s six largest IT-services firms forecast to achieve only 0.7–3.5% quarterly growth, reinforcing the need for providers to improve productivity, innovation, and measurable client value.
AI transformation faces a value test
AI-led transformation is accelerating, but rising costs and limited value measurement are making investment discipline increasingly important. In September 2026, 46.9% of enterprises reported AI spending above budget, while 60.9% of organisations reallocating budgets to cover overruns cut spending on external contractors and consultants. Only 12% consistently assessed AI value against cost, despite 48% reporting returns, highlighting a persistent gap between AI investment and measurable business impact.
Legacy systems hold back AI scale
AI integration is proving difficult as organisations struggle to connect new capabilities with existing technology environments. On 1 October 2026, a BearingPoint study found that only 13% of organisations were successfully advancing AI initiatives, while fewer than one-third had moved beyond pilot projects, with legacy-system integration identified as a major barrier. Technology governance is adding further complexity, with more than 60% of 1,600 senior decision-makers having paused or slowed AI adoption, 54% flagging unauthorised AI tools as a significant issue, and 81% citing unclear AI regulations as a material challenge. These constraints highlight the need for stronger integration, governance, and technology foundations to scale AI reliably.
AI skills gap threatens workforce readiness
Workforce readiness is becoming a critical constraint as AI capabilities advance faster than available talent. In India, only 1 in 6 IT professionals has AI skills despite nearly 2 million receiving AI upskilling, while just 300,000 have advanced AI capabilities. By 2030, 60% of employees are projected to require reskilling as AI automation affects routine work, while 56% of workers remain in the less-developed AI-skills group and only 51% report access to the learning resources they need.
Delivery failures expose outsourcing gaps
Delivery performance remains a major concern as outsourced IT operations generate frequent service failures and inefficient issue resolution. Among surveyed enterprise IT leaders, 94% reported SLA breaches linked to poor outsourced IT operations, while 34% of Level 1 issues were misrouted and 24% required rework or were resolved incorrectly. Engineers spent an average of 41 hours per month correcting lower-tier support work, further reducing the efficiency of higher-skilled teams. Operational complexity is also widespread, with 79% of surveyed organisations using two or more global systems integrators, increasing the need for stronger coordination and accountability across providers.
Next steps
IT outsourcing providers should focus on measurable client outcomes by linking AI investment, productivity improvements, and pricing models to demonstrable business value while strengthening delivery governance and provider accountability. Accelerating workforce reskilling, modernising legacy environments, tightening AI governance, and reducing operational fragmentation will be essential to scale AI profitably and deliver more reliable services.
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