AI propels transformative shift in Indian lifesciences industry with scalable, data-driven solutions

The use of AI in this manner also democratizes engineering expertise. A key policy goal of President Donald Trump’s administration is bringing jobs back to American factories, though the effort has been mired in a years-long slog despite support from both Democratic and Republican administrations. These closed-loop feedback systems are where process parameters adjust automatically in response to detected variations, contributing to higher product consistency and lower defect rates.

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It offers efficiency in many industries but can be especially advantageous for asset-intensive industries such as energy and utilities and manufacturing. Before you rush to add agentic AI to your technology stack, consider your company’s overall technical maturity. The launch of successful agents depends heavily on system integration. Agentic AI can provide insights, but it cannot take meaningful workflow actions until it integrates with core systems.

Managing these assets and ensuring their functionality or industry compliance is still, in many cases, tracked using manual processes – relying on spreadsheets, which are time-consuming and prone to error. A recent survey revealed that while many companies acknowledge the need for AI, only a few are leveraging it strategically. Deloitte says 55 percent of industrial product managers are using Generative AI (GenAI) tools, and 40 percent plan to increase their investment in AI and machine learning over the next three years. But the original remit, from a year ago, talked about upskilling or reskilling around 100,000 Danone employees “to the jobs of the future”, as well as attracting new AI talent.

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• Two recent examples highlight the consequences of rushing to implement and publish positive results from AI adoption.
• Instead of manually reviewing process logs or inspection reports, AI systems can comb through vast datasets to pinpoint causal relationships between inputs and defects.
• However, despite the apparent benefits, many companies still resist adopting these “no-brainer” solutions.
• In a utility environment, outage resolution is one of the more complex workflows.
• These problems and concerns, therefore, don’t allow organisations to take the strategic next decisive steps necessary to harness AI’s full potential.

Information security and infrastructure teams should also reassess vendors and review their internal infrastructure to support agentic capabilities, especially those requiring access to more sensitive information. Technology leaders should also review their architecture and assess their technical debt and readiness for integrating AI capabilities. Recent research shows that 92% of manufacturers say outdated infrastructure critically hinders their generative AI initiatives, and fewer than half have conducted a full-scale infrastructure readiness assessment. That’s not yet completely resolved and most surveys of CTOs and chief information officers show that the return on investment for generative AI projects isn’t as clear as they’d like it to be.

As development tools improved, organizations adopted a “mobile first” mindset and designed phone and tablet apps for specific user personas and job contexts. In a world where 80% to 90% of all AI proof of concepts fail to scale, now is the time to develop a framework that is based on caution. Then find opportunities to compare successful AI-based automation efforts at peer companies through peer discussions.

How agentic AI will transform mobile apps and field operations

However, with the advent of IoT (Internet of Things) wireless sensors, this barrier has been eliminated. Sensors can now be easily retrofitted and function independently of a company’s IT network, removing security concerns. Or think of a manufacturing firm with equipment scattered across multiple sites.

” When asked to “do something with AI,” technical leadership and their organizations promptly responded — sometimes begrudgingly and sometimes excitedly — for work-sanctioned opportunities to get their hands on a new technology. At that point, there was no time to sort between actual business returns from applying AI and “AI novelty” use cases that were more Rube Goldberg machines than tangible breakthroughs. As AI integrates into machines and control panels, workers will learn to operate and troubleshoot systems using new interfaces like augmented reality, natural learning commands, and AI-driven human-machine interfaces.

Today’s opportunity: Significant automation gains

Even when SaaS platforms announce agentic experiences, data teams should evaluate whether data volume and quality on the platform are sufficient to support the AI models. Mobile apps for the field usually consist of forms, checklists, access to information, dashboards, and reports. They can inform field operations about work that needs to be done, answer implementation questions, and provide information to planning and scheduling teams working at the office. The new facility is located at the company’s Nutricia factory in Opole, in southern Poland. (The Nutricia brand specialises in therapeutic food and infant formula.) The site in Opole is listed as one of the World Economic Forum’s ‘global digital lighthouse’ venues. Training will cover advanced automation, AI prompt writing, and data-enabled decision-making; modules will be delivered in person and online.

It said it wanted to double its number of partnerships over the next two years, as part of this Partner for Growth (P4G) scheme. It’s a key pillar of our Renew Danone strategy and will create long-term value for all our stakeholders. To thrive in an AI-enabled landscape, the workforce needs to evolve alongside the technology. Operators, technicians, and managers must understand how to interpret and act on AI data, requiring a basic statistical understanding, familiarity with dashboards, and the ability to question model outputs. AI can also account for multivariate influences, such as the combined effects of humidity, machine wear, and operator behavior. Cumulatively, this information gives stakeholders a more holistic view of the process and the root causes of defects.

Too many companies build AI models and try to apply governance later, which runs the risk of project delays and problems. Follow a replicable pattern for the project, beginning with a clear, written proof of concept, a pilot project and then the production of the agentic AI. AI operations – new Danone AI academy in Poland aims to upskill 20,000 factory staff in AI by 2026.

• When new data deviates from expected behavior, whether from equipment sensors, visual inspection systems, or material characteristics, the system flags it immediately.
• These advancements go beyond basic automation, enabling smarter, data-informed operations that adapt quickly and optimize for both performance and cost.
• It calls for deliberate strategy, scalable infrastructure, and thoughtful implementation.
• Koerte has had a long career at Siemens, joining the company in 2007 as a corporate strategist and taking on several leadership roles before ascending to the CTO and CSO titles in 2020.
• However, with the advent of IoT (Internet of Things) wireless sensors, this barrier has been eliminated.

The most promising opportunities should simplify work for field engineers while allowing them to deliver more value to customers. Instead of menus and structured workflows, mobile AI apps will include prompt interfaces and personalized data visualizations. AI will forecast what the end-user needs to know based on their current job, and prompt interfaces will simplify both querying for information and providing job updates. Software development ranks as a high priority use case for generative AI. The company’s software developer workforce of about 27,000 employees have been using AI coding assistants like GitHub Copilot and the productivity lift from those tools ranges between 10% to 30%, says Koerte.

Consider a high-throughput line where abrasive blasting is one of several value-added steps. AI can optimize upstream and downstream processes to synchronize with blasting throughput, ensuring balanced workloads and minimal idle time. It can also correlate variables such as abrasive type, pressure, and humidity to final finish quality, recommending adjustments to maximize yield. Instead, it extends their abilities through autonomous, guided actions.