56% of companies are getting “nothing” out of their AI investments. Not disappointing returns. Not slower-than-expected adoption. Nothing.
Meanwhile, companies are doubling down. Corporate AI spending will hit approximately 1.7% of revenues in 2026, more than double last year’s allocation.
So what separates the 12% of organizations achieving both revenue growth and cost savings from AI from the majority spinning their wheels?
PwC’s global chairman, Mohamed Kande, put it bluntly:
People forgot the basics.
The companies seeing results focused on clean data, well-defined processes, and strong governance before deploying AI. Everyone else rushed to adopt the technology without the foundation to support it.
Key takeaways
- 56% of companies report no meaningful gains from AI investments, while only 12% have achieved both revenue growth and cost savings
- Unplanned downtime costs Fortune 500 manufacturers $1.4 trillion annually (11% of revenue), with predictive maintenance reducing these costs by 25-40%.
- 53% of bankers rank fraud detection as their top AI use case for 2026, ahead of back-office automation (39%) and customer service (39%)
- Gartner predicts 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. However, over 40% of agentic AI projects will be canceled by 2027 due to escalating costs or unclear business value
- High-performing organizations are three times more likely to successfully scale AI agents than their peers, with the key differentiator being workflow redesign rather than technology sophistication
This piece breaks down the current state of enterprise AI adoption, explores proven applications in predictive maintenance and fraud detection, and outlines practical strategies for achieving ROI from custom AI implementations.
Enterprise AI adoption in 2026: The gap between spending and results
Three years after generative AI tools entered mainstream business use, adoption rates have stabilized at a high level.
The BCG AI Radar report, which surveyed 2,360 executives, found that 72% of CEOs now serve as their organization’s primary decision-maker on AI, twice the share from the previous year.
The gap between “using AI” and “getting value from AI” keeps growing. And it explains why so many executives are frustrated. Half of them believe their job security depends on successfully implementing AI strategies.
The financial commitment reflects this urgency. Companies plan to spend approximately 1.7% of revenues on AI in 2026, more than double the 0.8% allocation in 2025. Technology and financial services firms lead this investment, with both sectors planning to allocate roughly 2% of revenues to AI initiatives.
The gap between AI experimentation and scaled production
The oft-cited statistic that “95% of AI projects fail” from MIT requires context. Most pilots stall due to organizational factors: unclear success metrics, weak executive sponsorship, skills gap, cultural resistance, rather than technical limitations.
Technology alone doesn’t separate AI winners from the rest. Only 6% of organizations qualify as “AI high performers,” meaning they attribute 5% or more of EBIT to AI initiatives.
The defining factor is workflow redesign. High performers are nearly three times more likely to have fundamentally restructured processes around AI capabilities (55% compared to 20% for everyone else).
They also put real money behind it: over 20% of digital spend goes to AI, versus just 7% at average organizations. Perhaps more telling, these companies are 3.6 times more likely to pursue enterprise-wide transformation, targeting growth and innovation rather than settling for isolated efficiency wins.
Agentic AI adoption: Enterprise projections and market realities
AI agents, autonomous systems capable of planning and executing multi-step tasks without continuous human prompting, represent the next frontier of enterprise automation.
40% of enterprise applications will incorporate task-specific AI agents by the end of 2026, up from less than 5% in 2025. In its best-case scenario, agentic AI could generate approximately 30% of enterprise application software revenue by 2035, exceeding $450 billion.
There is a warning worth heeding: over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.
About 130 of the thousands of vendors claiming “agentic AI” capabilities offer genuine agent technology, with many engaging in “agent washing” by rebranding existing products such as chatbots and RPA tools.
AI ROI benchmarks for custom AI solutions
AI investment keeps climbing, with Gartner projecting enterprise AI software spend to nearly triple to $270 billion this year. Only about one-third of enterprises have seen tangible cost reduction or revenue increase from AI in the past 12 months.
Matt Marze, Vice President at New York Life Insurance Company, told CIO magazine that his team approaches AI investments “the same way we think about all our investments,” evaluating each project against operating expense reduction, margin improvement, and revenue growth.
Banking use cases: AI-powered customer service and fraud detection
Bank of America’s Erica virtual assistant has surpassed 3 billion client interactions since its 2018 launch, now serving nearly 50 million users and averaging 58 million interactions per month.
The bank reports that 98% of users find the information they need through Erica, significantly reducing call center volume.
On the employee side, over 90% of Bank of America’s workforce uses Erica for Employees, which has reduced IT service desk calls by more than 50%.
According to Holly O’Neill, president of consumer, retail, and preferred lines of business, the two million daily consumer interactions with Erica save the bank the equivalent of 11,000 staffers’ daily work.
On the fraud side, the UK government’s Cabinet Office reported that AI-powered detection tools helped recover £480 million between April 2024 and April 2025, the highest amount ever recovered by government anti-fraud teams in a single year. The Fraud Risk Assessment Accelerator, developed internally, cross-references data across government departments to identify vulnerabilities before they are exploited.
Manufacturing use cases: Predictive maintenance and quality inspection
Shell’s predictive maintenance platform, built with C3 AI, now monitors over 10,000 pieces of critical equipment across its global operations, ingesting 20 billion rows of data weekly from more than 3 million sensors. The system identified two critical equipment failures in advance, allowing preventive maintenance that saved approximately $2 million and “substantially improved operational reliability.”
In automotive, Siemens and Audi deployed AI-powered visual inspection in Audi’s car body shops, where 5 million welds are made daily. According to NVIDIA, integrating the models with Siemens’ Industrial AI Suite helped Audi achieve up to 25x faster inference directly on the shop floor, where defects can be addressed in real time. A separate Siemens deployment documented in R&D World showed an automotive OEM reducing unplanned downtime by 12% within 12 weeks of connecting more than 10,000 assets across four continents using Senseye Predictive Maintenance.
The predictive maintenance market is projected to grow from $10.93 billion in 2024 to over $70 billion by 2032, reflecting a compound annual growth rate exceeding 26%.
Industrial AI implementations must account for the specific demands of manufacturing environments. Edge deployment capabilities become critical for operations in remote locations or facilities with limited connectivity. Systems must integrate with existing PLCs, SCADA infrastructure, and ERP platforms while meeting regulatory and safety requirements.
Custom solutions developed with industrial data integration expertise address these technical constraints while delivering production-ready analytics.
What defines a custom AI solution for enterprise automation
To be effective in enterprise automation, AI must be purpose-engineered, designed with the organization’s data, workflows, controls, and compliance frameworks embedded from the outset.
1. Domain-specific AI models
Custom solutions often extend or fine-tune large foundational models with proprietary data and business logic to ensure accuracy and relevance in domain tasks. This goes beyond generic training to include task-specific reasoning, industry taxonomies, and operational constraints.
2. Workflow orchestration
AI must do more than generate outputs. It must execute multi-step workflows:
- Automate decisions where business rules match data evidence
- Trigger human review loops when confidence is low
- Ensure audit trails and accountability by design
This orchestration layer serves as the navigator between AI predictions and enterprise systems.
3. Integration with core systems
Integrations with CRM, ERP, document repositories, compliance systems, and analytics platforms are central to delivering ROI and closing the loop between AI automation and existing enterprise processes.
4. Governance, security, and compliance
Custom solutions embed governance by default, including role-based access, explainability logs, policy controls, and anomaly reporting, to meet regulatory and risk standards.
5. Outcome-driven KPIs
The shift from experimentation to performance mandates operational KPIs rather than model metrics:
- cycle time reduction
- cost per transaction
- error rates and exception volume
- compliance pass rates
- real ROI dashboards monitored by business owner
Strategic recommendations for scaling enterprise AI
For manufacturing organizations:
- Prioritize predictive maintenance: Focus initial AI investments on reducing the $2.8 billion annual downtime costs
- Implement Edge Computing: Deploy AI systems capable of operating in remote manufacturing locations
- Develop visual inspection capabilities: Leverage computer vision for real-time quality control
- Create multi-agent systems: Design collaborative agent networks for complex production optimization
For financial services:
- Enhance fraud detection: Invest in real-time transaction monitoring and pattern recognition
- Deploy customer service agents: Implement virtual assistants to handle routine inquiries and reduce call center volume
- Automate compliance processes: Use AI for KYC verification, AML surveillance, and regulatory reporting
- Focus on identity management: Develop robust systems for managing both human and AI agent identities
Universal success factors:
- Adopt the 10-20-70 framework: Invest 70% of resources in people and process transformation
- Implement strong governance: Establish AI firewalls and security frameworks before scaling
- Measure outcome-driven KPIs: Focus on operational metrics rather than model performance alone
- Plan for multi-agent orchestration: Design systems that can evolve from single agents to collaborative networks
Conclusion: AI that drives enterprise value
The era of AI experimentation is giving way to performance-aligned custom solutions. CIOs and business leaders are moving beyond proof-of-concept to enterprise-grade deployment by engineering AI into business processes with governance, integration, and measurable outcomes at the core.
Custom AI solutions perform best when they address specific business problems with domain expertise, embed governance from the start, integrate with existing systems, and measure real operational outcomes. Whether the application is predictive maintenance, reducing million-dollar downtime incidents, or fraud detection protecting billions in transactions, the pattern is consistent: foundation first, technology second.
In 2026 and beyond, success will be determined not by how many AI tools you deploy, but by how your AI delivers measurable impact on business outcomes across the organization.
