Manufacturing executives are wagering nearly half their modernisation budgets on AI, betting these systems will boost profit within two years.
This aggressive capital allocation marks a definitive pivot. AI is now seen as the primary engine for financial performance. According to the Future-Ready Manufacturing Study 2025 by Tata Consultancy Services (TCS) and AWS, 88 percent of manufacturers anticipate AI will capture at least five percent of operating margin. One in four expect returns exceeding 10 percent.
The money is there. The ambition is there. The plumbing, unfortunately, is not.
A disparity exists between financial forecasts and the reality of the factory floor. While spending on intelligent systems accelerates, the underlying data infrastructure remains brittle, and risk management strategies still rely on expensive manual buffers.
The pressure to extract cash value from tech stacks has never been higher. 75 percent of respondents expect AI to rank as a top-three contributor to operating margins by 2026. Consequently, organisations are funneling 51 percent of their transformation spending toward AI and autonomous systems over the next two years.
This spending eclipses other vital areas. Allocations for AI outpace workforce reskilling (19%) and cloud infrastructure modernisation (16%) by a wide margin. For CIOs, this imbalance signals a looming crisis: attempting to deploy advanced algorithms on shaky legacy foundations.
Anupam Singhal, President of Manufacturing at TCS, said: “Manufacturing is an industry defined by precision, reliability, and the relentless pursuit of performance. Today, that strength of foundation becomes multifold with AI in orchestrating decisions—delivering transformational business outcomes through greater predictability, stability, and control.
“At TCS, we see this as a defining opportunity to help manufacturers build resilient, adaptive, and future-ready enterprise ecosystems that can thrive in an era of intelligent autonomy.”
Analogue hedges in a digital era
Despite the heavy investment in predictive capabilities, operational behaviour betrays a lack of trust. When disruption hits, manufacturers aren’t leaning on the agility of their digital systems; they are reverting to physical safeguards.
Following recent disruptions, 61 percent of organisations increased their safety stock. Half opted for multisourcing logistics. Only 26 percent utilised scenario planning via digital twins to navigate volatility.
This is the disconnect. While AI promises dynamic inventory optimisation, a benefit cited by 49 percent of respondents, the prevailing instinct is to hoard inventory. Supply chain leaders are buying Ferraris but driving them like tractors. Bridging this gap requires moving from reactive safety measures to proactive and system-led responses.
Ozgur Tohumcu, General Manager of Automotive and Manufacturing at AWS, commented: “Manufacturers today are facing unprecedented pressure—from tight margins to volatile supply chains and workforce gaps. At AWS, we are revolutionising manufacturing through AI-powered autonomous operations, shifting from manual, reactive processes to intelligent, self-optimising systems that operate at scale.
“By embedding artificial intelligence into every layer of the operation and leveraging cloud-native architecture, manufacturers can move beyond simple automation to true autonomous decision-making where systems predict, adapt, and act independently with minimal human intervention. This enables not just faster response times, but fundamentally transforms operations with AI-driven predictability, resilience, and agility.”
Infrastructure debt
The primary obstacle to these financial returns isn’t the AI models; it’s the data they feed on. Only 21 percent of manufacturers claim to be “fully AI-ready” with clean, contextual, and unified data.
The majority (61%) operate with partial readiness, struggling with inconsistent quality across different plants. This fragmentation creates data silos that prevent algorithms from accessing the enterprise-wide inputs necessary for accurate decision-making.
Integration with legacy systems stands as the primary hurdle, cited by 54 percent of respondents. This “technical debt,” accumulated over decades of digitisation, makes it difficult to overlay modern autonomous agents on older operational technology.
Security also bites. Security and governance concerns top the list of plant-level obstacles at 52 percent. In an environment where a cyber-physical breach can halt production or cause physical harm, the risk appetite for autonomous intervention remains low.
The shift towards agentic AI in manufacturing
Despite the headwinds, the industry is charging toward agentic AI (i.e. systems capable of making decisions with limited human oversight.)
Seventy-four percent of manufacturers expect AI agents to manage up to half of routine production decisions by 2028. More immediately, 66 percent of organisations already allow – or plan to allow within 12 months – AI agents to approve routine work orders without human sign-off.
This progression from “copilots” to independent agents capable of completing entire tasks fundamentally alters the workforce. While 89 percent of manufacturers expect AI-guided robotics to impact the workforce, the focus is on augmentation rather than displacement.
Productivity gains are currently concentrated in knowledge-intensive roles. Quality inspectors (49%) and IT support staff (44%) are seeing the fastest gains. Traditional production roles like maintenance technicians (29%) lag behind. Adoption is following a pattern of cognitive augmentation before addressing physical coordination.
As AI agents embed themselves across platforms, enterprise architects face a choice regarding orchestration. The market shows a strong aversion to vendor lock-in.
63 percent of manufacturers favour hybrid or multi-platform strategies over single-vendor solutions. Specifically, 33 percent plan to coordinate through multiple platform-native agents, while 30 percent prefer a hybrid model blending platform-native and custom orchestration. Only 13 percent are willing to anchor on a single foundational platform.
Converting the manufacturing industry’s AI investment to profit
To convert this massive capital outlay into actual profit, the C-suite needs to look past the hype.
First, fix the data. With only 21 percent of firms fully ready, the immediate priority must be modernisation rather than algorithm development. Without clean, unified data, high-value use cases in sustainability and predictive maintenance will fail to scale.
Second, leaders must bridge the AI trust gap. The reliance on safety stock indicates a lack of faith in digital signals. Staged autonomy is the answer—starting with administrative tasks like work orders, where 66 percent are already heading, before handing over complex supply chain decisions.
Finally, avoid the monolithic trap. The data supports a multi-platform approach to maintain leverage and agility. Manufacturers are betting their future on AI, but realising those returns requires less focus on the “intelligence” of the models and more on the mundane work of cleaning data, integrating legacy equipment, and building workforce trust.
See also: Frontier AI research lab tackles enterprise deployment challenges
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