Manufacturing leaders are allocating almost half of their modernization budgets to artificial intelligence, betting that AI‑driven solutions will lift profitability within the next two years.
This aggressive capital deployment marks a decisive shift: AI is now viewed as the primary engine of financial performance. The Future‑Ready Manufacturing Study 2025, conducted by Tata Consultancy Services (TCS) and Amazon Web Services (AWS), found that 88 % of manufacturers expect AI to contribute at least 5 % to operating margin, while one in four projects margins above 10 %.
Capital is abundant and ambition is high, but the underlying infrastructure is lagging.
A stark gap exists between lofty financial forecasts and the realities on the shop floor. Spending on intelligent systems is accelerating, yet the data foundations remain fragile, and risk‑management practices still rely on costly manual buffers.
The pressure to monetize technology stacks has never been stronger. Seventy‑five percent of respondents say AI will rank among the top three contributors to operating margins by 2026. As a result, organizations plan to channel 51 % of their transformation spend into AI and autonomous systems over the next two years.
This allocation dwarfs other critical investments. Funding for AI surpasses workforce reskilling (19 %) and cloud‑infrastructure modernization (16 %) by a wide margin. For CIOs, the imbalance signals a looming crisis: deploying sophisticated algorithms on shaky legacy foundations.
“Manufacturing is defined by precision, reliability and relentless performance improvement,” said Anupam Singhal, President of Manufacturing at TCS. “When AI augments those core strengths, it creates a resilient, adaptive enterprise ecosystem capable of delivering transformational outcomes through greater predictability, stability and control.”
Analogue hedges in a digital era
Despite heavy investment in predictive capabilities, operational behavior reveals a lingering lack of trust. When disruptions occur, manufacturers revert to physical safeguards rather than leveraging digital agility.
Following recent supply‑chain shocks, 61 % of firms increased safety stock, 50 % diversified logistics sources, and only 26 % employed scenario‑planning tools such as digital twins to navigate volatility.
This disconnect underscores the paradox: AI promises dynamic inventory optimization—a benefit cited by 49 % of respondents—yet the prevailing instinct is to hoard inventory. Supply‑chain leaders are buying Ferraris and driving them like tractors. Closing the gap requires a shift from reactive safety buffers to proactive, system‑led responses.
Ozgur Tohumcu, General Manager of Automotive and Manufacturing at AWS, noted, “Manufacturers face unprecedented pressure—from thin margins to volatile supply chains and talent gaps. By embedding AI across every layer of the operation and leveraging cloud‑native architecture, we can move beyond simple automation to true autonomous decision‑making. Systems that predict, adapt and act independently deliver faster response times and fundamentally transform operations with AI‑driven predictability, resilience and agility.”
Infrastructure debt
The primary barrier to realizing these financial returns is not the AI models themselves but the data that fuels them. Only 21 % of manufacturers consider themselves “fully AI‑ready,” meaning they have clean, contextual and unified data.
The majority—61 %—operate with partial readiness, struggling with inconsistent data quality across plants. This fragmentation creates silos that prevent algorithms from accessing enterprise‑wide inputs needed for accurate decision‑making.
Integration with legacy systems is the top hurdle, cited by 54 % of respondents. Decades of incremental digitization have generated “technical debt,” making it difficult to overlay modern autonomous agents on older operational technology.
Security concerns also loom large. Plant‑level obstacles related to security and governance rank highest at 52 %. In environments where a cyber‑physical breach can halt production or cause physical harm, risk appetite for autonomous intervention remains low.
The shift towards agentic AI in manufacturing
Despite headwinds, the industry is pressing forward with agentic AI—systems capable of making decisions with minimal human oversight.
Seventy‑four percent of manufacturers expect AI agents to handle up to half of routine production decisions by 2028. Already, 66 % of firms either allow or plan within 12 months to let AI agents approve routine work orders without human sign‑off.
This evolution from “copilots” to independent agents fundamentally reshapes the workforce. While 89 % of manufacturers anticipate AI‑guided robotics will impact labor, 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 improvements, whereas traditional production roles such as maintenance technicians (29 %) lag behind. Adoption follows a pattern of cognitive augmentation before addressing physical coordination.
AI agents proliferate across platforms, enterprise architects face a strategic choice about orchestration. The market shows a strong aversion to vendor lock‑in.
Sixty‑three percent of manufacturers favor hybrid or multi‑platform strategies over single‑vendor solutions. Specifically, 33 % plan to coordinate through multiple platform‑native agents, while 30 % prefer a hybrid model that blends platform‑native and custom orchestration. Only 13 % are willing to anchor on a single foundational platform.
Converting the manufacturing industry’s AI investment to profit
To translate this massive capital outlay into tangible profit, C‑suite leaders must look beyond hype.
First, fix the data. With just 21 % of firms fully ready, immediate priority should be data modernization rather than algorithm development. Without clean, unified data, high‑value use cases—such as sustainability analytics and predictive maintenance—cannot scale.
Second, bridge the AI trust gap. The continued reliance on safety stock signals a lack of confidence in digital signals. Staged autonomy offers a pathway—starting with low‑risk administrative tasks like work‑order approval, where 66 % of firms are already moving, before handing over more complex supply‑chain decisions.
Finally, avoid the monolithic trap. Survey data supports a multi‑platform approach to preserve leverage and agility. Manufacturers are betting their future on AI; realizing those returns will depend less on the sophistication of the models and more on the mundane but essential work of cleaning data, integrating legacy equipment, and building workforce confidence.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/14015.html
