Why AI Is Becoming Core Business Infrastructure in 2026: Key Insights

Conclusion

AI is transitioning from an optional innovation project to a core operating infrastructure for enterprises.

AI Entering a Mature Adoption Phase: Pilots Decline, Scale‑up Grows

Overall, 64% of respondents say their organization is actively using AI in operations, 28% are still evaluating, and only 8% have no AI plans, indicating mainstream adoption.

Regional adoption rates: North America 70%, EMEA 65%, APAC 63% (APAC shows a higher share of non‑users).

Large enterprises (>1,000 employees) lead the charge: 76% are actively using AI, with almost no firms completely abstaining.

Trend: AI competition is shifting from "who can demo first" to "who can embed AI into real business processes first".

Enterprises Prioritize Efficiency Over Showmanship

The top three AI goals are operational efficiency (34%), employee productivity (33%), and new business/revenue opportunities (23%).

Impact: 53% say AI boosts employee productivity, 42% report efficiency gains, and 34% see new revenue possibilities.

In telecom, 99% of respondents say AI improves productivity, with a quarter rating the improvement as "significant".

Shift in mindset: from focusing on model capability to asking whether AI reduces manual work, shortens business cycles, and raises output per unit time.

AI Delivers Concrete Financial Results: Revenue Growth and Cost Reduction

88% of respondents report AI has positively impacted annual revenue growth.

Revenue Growth

30% see revenue increases >10%, 33% see 5‑10% growth, and 25% see <5% growth.

30% report >10% revenue uplift

33% report 5‑10% uplift

25% report <5% uplift

Over 40% of C‑suite executives observe >10% revenue growth.

Cost Reduction

87% say AI helped lower annual costs; 25% report cost cuts >10%.

Retail and consumer goods show the strongest impact, with 37% reporting >10% cost reduction.

Result: AI is simultaneously expanding revenue and compressing costs.

Typical Cases Go Beyond Chatbots

Nasdaq: AI Platform Unifies Business and Data

Nasdaq built an AI platform to streamline internal operations and enhance external product capabilities, connecting data across business units to improve services.

PepsiCo + Siemens: Digital Twins Accelerate Plant Changes

Using high‑fidelity 3D digital twins, PepsiCo achieved a 20% throughput increase, 100% design‑verification success, 10‑15% capex reduction, and identified up to 90% of potential issues before physical modifications.

Throughput +20%

Design verification 100%

Capex down 10‑15%

Potential issues detected 90%

Lowe’s: Turning 2D Product Images into 3D Models

Lowe’s deployed AI for over 1,750 stores, creating precise 3D models from 2D images at a cost of less than $1 per model.

These cases illustrate that AI value lies in reshaping data, processes, and asset production rather than merely providing a chat interface.

Agentic AI Starts Scaling in 2026

44% of enterprises have deployed or are evaluating AI agents; telecom leads with a 48% adoption rate, followed by retail/consumer goods at 47%.

In healthcare, Clinomic’s Mona AI assistant for ICU settings reduces documentation errors by 68% and perceived workload by 33%.

68% reduction in documentation errors

33% reduction in perceived workload

Agentic AI is moving from concept to production in high‑skill, real‑time scenarios.

Generative AI Is Strong, Yet Core Workloads Remain Business‑Focused

Data analysis and predictive AI account for 62% of enterprise AI workloads, slightly ahead of generative AI at 61%.

Traditional, predictive, generative, and agentic AI together form the new intelligent systems landscape.

Open Source Becomes a Key AI Strategy Foundation

85% of respondents rate open source as moderately to extremely important for their AI strategy; 48% consider it "very important".

SMBs are especially enthusiastic—58% rate open source as very important—while 51% of executives assign high importance.

Enterprises recognize that ROI comes from specialized AI applications built on open models, weights, and software, enabling tailored solutions and fine‑tuning on proprietary data.

AI Budgets Keep Growing in 2026

86% say AI budgets will increase in 2026; 12% stay flat; about 40% anticipate >10% growth, with North America at 48%.

Top spending areas for 2026: optimizing existing AI workflows (42%), seeking additional business use cases (31%), and building AI infrastructure access (31%).

Remaining Bottlenecks: Talent and Data

48% cite insufficient data as the primary hurdle, 38% lack AI experts/data scientists, and 30% find ROI difficult to quantify.

Enterprises need to strengthen data governance, talent organization, and ROI measurement together.

Five Key Trends Revealed by the Report

AI has moved past the "whether to do it" stage.

AI value now appears in concrete business metrics (revenue, cost, productivity).

Vertical, specialized AI systems outweigh generic models for ROI.

AI agents are transitioning from experimentation to production deployment.

Open source + proprietary data + infrastructure will form a lasting AI moat.

Final Takeaway

AI is evolving from an innovation tool to an operating system that helps enterprises run more efficiently, lower costs, discover new revenue streams, and systematically redesign business processes. Success now hinges on data capability, organizational readiness, engineering expertise, and deep industry understanding.