Manufacturing executives are betting nearly half of their modernization budgets on AI, betting that these systems will boost profits within two years.
This aggressive capital allocation marks a decisive turning point. AI is now considered a key engine of financial performance. According to the Future-Ready Manufacturing Study 2025 by Tata Consultancy Services (TCS) and AWS, 88% of manufacturers expect AI to capture at least 5% of their operating margins. One in four people expect returns of more than 10%.
The money is there. The ambition is there. Unfortunately, this is not the case with plumbing.
There is a disconnect between financial forecasts and factory floor reality. While spending on intelligent systems accelerates, the underlying data infrastructure remains weak and risk management strategies still rely on expensive manual buffers.
The pressure to extract cash value from your technology stack has never been greater. 75% of respondents expect AI to be a top-three contributor to operating profits by 2026. As a result, organizations will focus 51% of their transformation spend on AI and autonomous systems over the next two years.
This spending dwarfs other important areas. Allocation to AI significantly exceeds workforce reskilling (19%) and cloud infrastructure modernization (16%). For CIOs, this imbalance points to the impending danger of deploying advanced algorithms on unstable legacy foundations.
Anupam Singhal, President, Manufacturing, TCS “Manufacturing is an industry defined by the relentless pursuit of precision, reliability, and performance. Today, that foundational strength is multiplied many times over with AI that coordinates decision-making and delivers transformative business outcomes through greater predictability, stability, and control.”
“At TCS, we believe this is a critical opportunity for manufacturers to build resilient, adaptable, and future-ready enterprise ecosystems that can thrive in the era of intelligent autonomy.”
Analog hedging in the digital age
Despite heavy investments in predictive capabilities, operational behavior shows a lack of trust. When disruptions occur, manufacturers cannot rely on the agility of digital systems. They are returning to physical safety measures.
61% of organizations increased their safety stock following recent disruptions. Half chose multi-sourcing logistics. Only 26% used digital twin scenario planning to avoid volatility.
This is a disconnection. While AI promises dynamic inventory optimization, cited as a benefit by 49% of respondents, the common instinct is to hoard inventory. Supply chain leaders are buying Ferraris, but driving them like tractors. Closing this gap requires a shift from reactive safety measures to proactive, system-driven responses.
“Manufacturers today face unprecedented pressures, from tight profit margins to unstable supply chains and labor shortages,” said Ozgur Tumuk, General Manager of Automotive and Manufacturing at AWS. “At AWS, we are revolutionizing manufacturing through autonomous, AI-powered operations, moving from manual, reactive processes to intelligent, self-optimizing systems operated at scale.”
“By incorporating artificial intelligence into every layer of their operations and leveraging cloud-native architectures, manufacturers can move beyond simple automation to truly autonomous decision-making, where systems predict, adapt, and act on their own with minimal human intervention. This not only improves response times, but also enables them to fundamentally transform their operations with AI-driven predictability, resiliency, and agility.”
infrastructure debt
The main obstacle to these economic gains is not the AI model. It’s the data they feed on. Only 21% of manufacturers claim to be “fully AI-enabled” with clean, contextual, and integrated data.
The majority (61%) operate with partial readiness and suffer from inconsistent quality between different plants. This fragmentation creates data silos that prevent algorithms from accessing enterprise-wide inputs needed to make accurate decisions.
54% of respondents cited integration with legacy systems as a major hurdle. This “technical debt” accumulated over decades of digitalization makes it difficult to layer modern autonomous agents on top of older operational technologies.
Security is also strict. Security and governance concerns top the list of plant-level obstacles at 52%. In an environment where a cyber-physical breach can disrupt production or cause physical damage, the risk appetite for autonomous intervention remains low.
Transitioning to agent AI in manufacturing
Despite the headwinds, the industry is moving toward agent AI (i.e., systems that can make decisions with limited human oversight).
74% of manufacturers expect up to half of day-to-day production decisions to be managed by AI agents by 2028. More directly, 66% of organizations already allow or plan to allow AI agents to approve routine work orders without human approval within 12 months.
This evolution from a “co-pilot” to an independent agent capable of completing an entire task fundamentally changes the workforce. 89% of manufacturers expect AI-guided robotics to impact their workforce, but the focus is on augmentation rather than replacement.
Currently, productivity gains are focused on knowledge-intensive roles. Quality inspectors (49%) and IT support staff (44%) are growing the fastest. Traditional production roles such as maintenance technicians (29%) are lagging behind. The introduction follows a pattern of strengthening cognitive functions before addressing physical conditioning.
As AI agents are embedded throughout platforms, enterprise architects will have to make orchestration choices. The market has shown a strong aversion to vendor lock-in.
63% of manufacturers favor a hybrid or multi-platform strategy over a single vendor solution. Specifically, 33% plan to coordinate via multiple platform-native agents, and 30% prefer a hybrid model that combines platform-native and custom orchestration. Only 13% are willing to stick with a single underlying platform.
Turn manufacturing industry AI investments into profits
To turn this huge capital expenditure into real profits, executives must ignore the hype.
First, fix the data. Only 21% of companies are fully prepared, so the immediate priority must be modernization, not algorithm development. Without clean, integrated data, high-value use cases in sustainability and predictive maintenance cannot scale.
Second, leaders must close the AI trust gap. Reliance on safety stock shows a lack of trust in digital signals. Gradual autonomy is the answer. Before handing over complex supply chain decisions, 66 percent start with administrative tasks like work orders, which they are already working on.
Finally, avoid the monolithic trap. Data supports a multi-platform approach to staying leveraged and agile. Manufacturers are betting their future on AI, but to realize the benefits they need to focus less on model “intelligence” and more on mundane tasks like cleaning data, integrating legacy equipment, and building employee trust.
See also: Frontier AI Institute tackles enterprise implementation challenges
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