Max ROI: AI Cost Efficiency Reshapes Enterprise Strategies

How do you measure cost efficiency? Generative AI has made headlines for its ability to generate cost efficiency by automating administrative and creative tasks, such as graphic design, email writing, and customer service. However, AI is also helping leaders in the industrial manufacturing industry dramatically improve efficiency and achieve cost savings across their entire organization, leaving many organizations asking, "How does AI help with cost efficiency?"

Gartner recently estimated that "AI has the potential to deliver…approximately $1 trillion in values remains to be captured from the industrial sector." In particular, the research found that integrating AI into industrial manufacturing firms can result in a 10 to 15% increase in production and a 5% increase in earnings before interest, taxes, and amortization (EBITA).

Compared to other industries, manufacturing is particularly well-suited to embrace AI-driven data analytics and cost efficiency because of the mass amounts of data that companies generate. Deloitte discovered that "Manufacturing is estimated to generate about 1,812 petabytes (PB) of data every year, more than communications, finance, retail, and several other industries."

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Industrial companies are integrating AI into every aspect of their businesses, helping the most innovative companies streamline their back office cost efficiency, maintain equipment, prevent shutdowns, and design the efficient manufacturing plants of the future.

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The Potential of AI in Finance: Immediate Cost Efficiency

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While many industry leaders look to AI for cost efficiency and reduce downtime, accounting and supply chain divisions are often low-hanging fruit with the potential to produce immediate cost savings with minimal investments.

Gartner identifies the benefits of AI in finance as numerous, saying,

“The value of artificial intelligence comes from improving the finance function’s ability to predict, analyze and uncover important patterns from unstructured data and thereby automate work, make informed decisions, compute large quantities of information (including unstructured data) and avoid risk.”

These incredible benefits are why Deloitte found that over 20% of manufacturers have already integrated AI into financial management, with over 30% of companies planning to do the same within the next two years.

AI experts believe companies should embrace this emerging technology because it “enables finance professionals to focus on higher-value activities, such as strategic planning and analysis, instead of manual and transactional activities.”

Organizations interested in integrating AI into their accounting workflows should start small and look to repetitive tasks that involve a lot of human labor. Narrowly-focused off-the-shelf options are a solid place to start because they allow you to quickly and cheaply assess the return on investment (ROI) before committing to pricey custom solutions or enterprise packages. Potential AI use cases in finance include:

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• Auditing
• Bookkeeping
• Tax Law Research
• Invoice Generation & Tracking
• Writing Procedure Guides
• Identifying Suspicious Activities

Contemporary AI can effectively handle these tasks, and companies can begin reaping the benefits of this emerging technology with minimal investment or disruption in their business.

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Jaguar Land Rover's AI-Enabled Cost Efficiency

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Jaguar Land Rover is one industrial manufacturer that uses AI to increase cost efficiency in its financial groups and de-silo essential data that could transform the company's operations. The company recently partnered with Blue Prism to build AI-based process automation into their supply chain management and accounting divisions, and de-silo essential data that could transform its operations.

As a result of this partnership, Jaguar Land Rover was able to discover and implement back-office improvements, unlock AI cost efficiency, and discover new business opportunities. The automotive maker began by automating accounts payable and invoice reconciliation processes, increasing the accuracy of these tasks and reducing risks associated with regulatory compliance before setting future sights on the production line itself.

By automating accounts payable and invoice reconciliation processes, Jaguar Land Rover has generated over $1,200,000 in added value through back-office efficiency improvements so far. The company is investing this newfound money in future digital transformation and automation initiatives—and they’re an incredible example of how AI can revolutionize financial management in the manufacturing industry.

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Predictive Maintenance as the Key to AI-Enabled Cost Efficiency

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Predictive maintenance is a prime target for AI disruption because unplanned downtime costs individual manufacturers millions of dollars annually. Research “suggests that unplanned downtime now costs Fortune Global 500 companies 11% of their yearly turnover—almost $1.5tn.” The price is even higher in high-tech manufacturing facilities like automotive, where a single hour of downtime can cost upwards of $2 million.

That’s why, for many manufacturers, using data and AI-driven insights to predict machinery breakdowns and prevent disruption of assembly lines is the next logical application for AI. In fact, seven out of ten manufacturing businesses now “see Predictive Maintenance as a strategic priority.”

The recent focus on predictive maintenance is backed by data—it can lead to a remarkable reduction in the amount of downtime. The Deloitte Analytics Institute found that "Predicting failures via advanced analytics can increase equipment uptime by up to 20%." Predictive maintenance can also reduce the number of breakdowns by up to 70% and result in a 25% increase in productivity.

Manufacturing companies can use this technology to add value by:

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• Installing sensors on the production floor
• Collecting and structuring data correctly
• Analyzing data and producing actionable insights
• Conducting predictive maintenance to reduce downtime
• Integrating these insights into future production floor planning
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Before getting started, it’s important to accurately assess the state of your data collection and analysis capabilities. If you have limited experience with this type of machine learning, turn to a trusted AI partner to learn to set up your production floor to maximize data collection and turn that massive amount of data into high-level insights that boost productivity and savings.

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Siemens and Colgate-Palmolive's Embrace AI to Boost cost efficiency

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Siemens AG recently embraced AI to boost cost efficiency across its global manufacturing divisions and improve consumer product quality. The company reduced downtime and increased revenue through AI predictive maintenance.

"Predictive maintenance increases the lifetime of our machines by monitoring operations to assess and identify potential failures," said Raithel. "A planned maintenance shutdown predicted by AI could prevent a breakdown of the production line at 3 am. That's a 15-minute loss of production compared to hours."

Siemens is also using AI to identify weak links in large electronics. The medical device manufacturer found that "these early fault assessments have allowed the company to perform 30% fewer X-ray tests, have a 100% quality rate, and reduce capital investment by $555,000."

Colgate-Palmolive witnessed equally impressive cost efficiency from its embrace of artificial intelligence. Early implementation of AI at a toothpaste manufacturing plant saved "192 hours of downtime and an output of 2.8M tubs of toothpaste, plus $12,000 for a new motor and $27,000 in variable conversion costs" by identifying a problem with a single motor's water cooling system.

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Warren Pruitt, Vice President of Global Engineering Services at Colgate-Palmolive, told Automation World, "Although early diagnosis of problems is a key advantage here, there are additional savings from extending use of equipment past what would be typical preventative maintenance schedules."

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Unlocking the Potential of AI in Construction: Efficiency, Safety, and Sustainability

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The World Economic Forum (WEF) recently noted that AI may be a game-changing technology for construction, helping companies realize better cost efficiency when building commercial properties.

By integrating AI into the planning process, “It’s now possible to optimize construction schedules without relying on rinse-and-repeat schedules for each project to determine how long things really take. This knowledge alone makes the process more efficient and more profitable.” AI also makes it possible to increase worker safety in new facilities by allowing planners to identify and fix hazards before site plans are finalized.

Finally, the WEF found that AI may be the key to improving sustainability by reducing change orders and design plan mistakes, both of which reduce waste. The organization also discovered that AI “can significantly reduce material waste, optimize the costs allocated for materials and positively impact the entire workflow of the construction site.”

Organizations can speak with their architectural partners to discover how they can integrate AI into the planning stages of their next facility. In particular, AI may help your company:

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• Increase cost efficiency
• Reduce Emissions
• Draft Building & Site Plans
• Increase Operational Efficiency
• Test New Layouts & Machinery Virtually
• Help Companies Meet Regulatory Standards
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Some of the world's most innovative companies are embracing the power of AI for industrial construction, leveraging AI insights to safely build new facilities that can handle an increased production rate and test cutting-edge technology using simulated real-world conditions.

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The Virtual Revolution: AI and BMW's Metaverse Manufacturing Transformation.

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BMW Group was one of the first large-scale companies to embrace AI to create the next generation of manufacturing facilities. The car manufacturer recently partnered with NVIDIA to use AI and the metaverse to create virtualized production plants, detailed layouts, and customized robotics to design high-efficiency facilities years before the company starts building a new facility.

The automaker officially opened its first virtual-only factory in March 2023, with plans to start physical operations at its real-world counterpart in 2025. NVIDIA projects that BMW will achieve "30% savings from optimized facilities planning and highly efficient processes" by using the AI-powered platform, in addition to significantly reducing capital investments and change orders during construction.

NVIDIA says, "This means they can get to production faster and operate more efficiently, improving time to market, digitalization, and sustainability."  Milan Nedeljković, a Member of the BMW Group Board of Management, says, "This is transformative—we can design, build and test completely in a virtual world."

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Starting Small, Thinking Big: The Path to AI-Driven Productivity Gains

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While many firms are eager to embrace AI as a game-changing technology, experts warn companies to start small before scaling their efforts. Carmen Taglienti, Chief Data Officer and Data/AI Portfolio Director at Insights, told the In Business podcast that increasing productivity is one of the most accessible areas to achieve immediate results.

He encourages leaders to find tasks that require a lot of manual labor. These areas are ripe for disruption by AI, which can accomplish these tasks more efficiently. He says, "Those are really good use cases, and the ROI [return on investment] is kind of easy in those cases because anytime that you have manual processes or manual labor involved, you can immediately see benefit from an ROI perspective by reducing" those manual labor tasks.

Dorothy Li, CTO of Convoy and former VP of Business Intelligence and Analytics Services at Amazon Web Services, told In Business the same thing: "In the logistics industry, we have a lot of manual labor. There's a lot of undifferentiated heavy lifting."

It's also critical to assess your data analytics capabilities and ensure you have the leadership to oversee these efforts. Li notes, "Data is the foundation for AI and the first step every organization needs to take. When you're thinking about taking on AI and machine learning, look at the data needed and look at where you are in the data platform, where you are in the data journey, and if you don't already have a chief data officer."

Despite the promising benefits associated with AI, starting small and ensuring AI automation provides meaningful ROI before launching large-scale projects is essential to achieving real cost efficiency. Regarding new AI initiatives, Taglienti says, "Ultimately, we want to test them, validate them, and demonstrate the ROI. Once we're able to do that, then we can scale it."

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