10 Predictions for 2026
AI at the Core of Enterprise Transformation
In 2025, firms had to dig deep to find ways to optimize costs in the face of so much business uncertainty. But they didn’t just save those costs, they used those funds to accelerate investments into AI-related pilots.
This trend will continue – and accelerate – in 2026. Firms are quickly recognizing that they need to transform their operating model to turn AI pilots into business results. These transformations will impact every part of the business and every layer of the technology stack.
Here’s how ISG sees these dual mandates of cost optimization and business transformation playing out in 2026:
1. | Technology modernization will become a board-level priority.Across the Global 2000, legacy estates, bespoke applications and technical debt are surfacing as existential risks that constrain strategy, hide costs and slow the adoption of new technology, especially AI. Monolithic stacks and tightly coupled systems can’t sustain rapid AI-driven change. Generative AI is already reducing technical debt, but in 2026, organizations will accelerate this by decomposing their IT stacks into modular services, event-driven flows and API-liberated cores, so data and compute can be routed fluidly across clouds and edges. The change will unfold along two complementary paths. One vector is industry-specific business process transformation, focused on re-platforming, enriching data and applying agentic automation to high-value processes. The other vector is the modernization of entrenched platform estates (e.g., SAP, Oracle, Salesforce). This will take the form of liberating data via APIs and extending these systems rather than ripping them out. Modernizing core platforms means replacing – not rewriting – many bespoke applications, which will lead to a smaller mesh of agents and SaaS platforms. |
2. | Low-quality data and lack of governance will slow the adoption of agentic systems.Agentic systems connect traditional and generative AI with agents that operate across tools and data, make decisions and carry them out. The shift lifts AI capabilities from single functions to a system-of-systems. Enterprises are beginning to layer in generative and agentic enhancements on top of proven predictive capabilities – but not without its challenges. Many organizations have fragmented data management, inconsistent semantics and poor data quality—making it difficult for AI to deliver meaningful business outcomes. Agentic systems also require dynamic oversight to be effective (and trusted). This includes embedding validation, quality scoring and write-back mechanisms directly into systems of record. Very few organizations have reached this level of maturity in their data and governance, which will significantly hamper the adoption of agentic systems. |
3. | AI use cases will continue to proliferate but scaling business benefits will prove challenging.Over 30% of the most well-funded AI use cases are in production today, which is up from 15% a year ago. The good news is that all metrics – save headcount reduction – are delivering business results above expectations. The number of use cases that make it into production will continue to grow in 2026, but the pace of growth will likely slow. Costs (and timelines) are increasing and, as this happens, the lack of impact on the P&L will force organizations to improve their ability to measure non-P&L impacting metrics where AI is having a strong impact – in areas like R&D, quality and customer experience. Lack of organizational readiness will also be a significant hindrance. AI forces companies to rethink workflows, decision rights and even job roles beyond IT. This takes time, and most organizations are just now starting to recognize this constraint. |
4. | Industry-specific business functions will be disrupted by AI faster than most leaders expect.In their efforts to modernize, forward-thinking firms will train models on domain-specific data and embed industry rules that sit directly inside workflows. For Life Sciences, that means systems that accelerate candidate triage, trial matching and pharmacovigilance. In Manufacturing, firms will use process telemetry data to improve planning, predictive maintenance and quality control. In Banking, firms will implement real-time risk scoring, KYC automation and transaction surveillance with models trained against financial ontologies and regulatory context. The initial focus of these AI-led modernization efforts will be on cost reduction, but as firms gain more experience, they will lean on industry specificity as a multiplier that creates operational advantage. |
5. | AI pricing chaos will continue, but clarity is on the horizon.IT and business process outsourcing contracts are typically priced based on underlying cost drivers. Most often, these cost drivers are a combination of labor and tools. However, agentic AI is rapidly reshaping how technology services are delivered, which means the share of work done by AI is increasing, while the share of work done by humans is decreasing. This sudden shift is creating a “black box” for enterprise buyers and service providers; neither side knows exactly how or when the benefits of AI will be realized. In 2026, enterprises and service providers will pilot new pricing mechanisms to bridge the gap between labor-centric service delivery and autonomous service delivery. This is what we’re calling autonomy-level pricing. |
6. | The convergence of generative AI today and quantum computing tomorrow will require greater cybersecurity investment.Generative AI has dramatically increased the speed and scale at which hackers can perform social engineering. Autonomous malware, AI-powered social engineering and deepfake and synthetic identity attacks are here today. This means that what was once a human-scale threat is now a machine-scale threat. At the same time, public key cryptographic algorithms, which secure sensitive data, are vulnerable to advances in quantum computing, necessitating a transition to quantum-resistant algorithms. These two forces are merging as attackers increasingly use a “harvest-now-decrypt-later” strategy to collect data today in anticipation of quantum advances tomorrow. In 2026, enterprises, especially those in regulated industries, will get serious about funding the technology, talent, infrastructure and governance needed to protect against this rapidly emerging threat. |
7. | AI will push more – not fewer – workloads to the cloud.After several years of portfolio rebalancing and tightened FinOps discipline, enterprises are ramping up cloud consumption again with a focus on model training, streaming pipelines and inferencing on the edge. These workloads require model-aware compute, hybrid operating patterns and intelligent edge tooling – capabilities that favor platforms that can guarantee GPU capacity, low latency and integrated MLOps/observability. In 2026, firms will increase cloud consumption to scale frontier models and accelerate time-to-value while leaning on tighter FinOps, capacity commitments and deeper platform partnerships to keep growth predictable and performance. |
8. | Enterprises will accelerate their SAP transformation plans as the 2027 deadline looms.In 2026, the SAP landscape will be dominated by the final push to migrate from ECC to S/4HANA ahead of the 2027 support deadline. ISG Research shows that most enterprises are still mid-journey, with hybrid migration strategies – combining greenfield innovation and brownfield risk mitigation – as the preferred approach. Programs like RISE with SAP and GROW with SAP will accelerate adoption, but execution complexity will keep timelines tight and pressure high. The biggest hurdles will not be technology alone. Integration complexity, data migration risks and organizational change management will continue to challenge even well-funded programs. ISG data indicates that over one-third of enterprises expect providers to lead the change management, signaling a growing dependence on advisory and managed services. Talent shortages for SAP and AI skills will exacerbate these issues. |
9. | Digital sovereignty will accelerate the adoption of U.S.-based compliance-ready platforms.Geopolitical uncertainty, rising AI regulation and data residency concerns are making digital sovereignty – and specifically sovereign cloud – a top priority for enterprises across Europe. For a firm to have sovereign cloud, it needs cloud infrastructure and services that comply with local data laws, ensure operational independence from non-E.U. jurisdictions and delivered typically by European providers or partnerships. European firms are focusing on adopting three-tier hybrid architectures, balancing sovereignty, scalability and innovation. Evolving E.U. regulations are pushing CIOs to prioritize compliance-ready platforms, and consortiums are emerging to address these needs. Today, these platforms are still in their infancy. This means that, in 2026, much of these services will come directly from U.S.-based hyperscalers as E.U. digital platforms grapple with bureaucracy, fragmented standards and a massive funding gap compared to U.S. digital platforms. |
10. | Global capability centers will continue to proliferate but struggle with a growing disconnect between headquarters and local leaders.Global capability center (GCC) activity continues to be extremely strong, with both large and mid-size firms establishing and scaling their centers in locations like India, Eastern Europe and Latin America. Still, we see a growing disconnect between what corporate executives want from their GCC investments and what their local leaders want. Corporate executives continue to focus on using GCCs as a lever to reduce costs. However, GCCs, unlike shared services centers, are often designed around an innovation agenda. Local GCC leaders understand this, which creates an “innovation disconnect” between what headquarters wants versus what local leaders believe they can deliver. This erodes the value of the GCC over time. This disconnect will drive a new wave of GCC “resets,” in which enterprise leaders look to the IT services sector to help drive down costs and improve productivity, scaling down or even exiting their GCC in some cases. For firms that have established a strong linkage between HQ and the GCC, especially those in India, we will see an acceleration of engineering and AI leadership. |
