AI in manufacturing: bridging the implementation gap

An example is the manufacturing sector, where 80% of industry players have either implemented AI or are actively testing its potential, per Salesforce’s “Trends in Manufacturing” report.

Yet, despite this impressive adoption rate, few enterprises use AI to its full potential. Many are still struggling to move beyond pilot projects due to legacy systems, fragmented data, security issues, or skills shortages. For these manufacturers, it’s not about whether AI can help—it’s about how to make it work in the real world without blowing their budgets or derailing operations.

However, no one says you have to do everything at once. Depending on your budget and business goals, you can minimise downtime and keep costs low through a gradual rollout. With support from experts like Fluido, this transformation journey becomes more manageable and less risky.

Interested in finding out more? Here’s what you should know about the challenges of implementing AI in manufacturing—and the steps to take for a successful transition.

The state of AI in manufacturing: widespread but uneven

In 2024, Salesforce surveyed 800 manufacturing leaders to gauge their feelings about digital transformation, emerging technologies, and the industry as a whole. When it came to AI, their responses revealed a dramatic shift from just a few years ago, when the technology was still considered experimental or speculative.

36% of manufacturers reported having fully implemented AI across their organisations. Most enterprises (44%) were either in the process of rolling it out or actively experimenting with it at the time, and only 4% said they had no plans to make the switch.

However, the reality is far more nuanced than it appears on the surface. First, the AI models used vary, ranging from predictive AI to large language models (LLMs). Second, AI adoption is broad but not even, with some interesting variations across manufacturing subsectors and regions.

By manufacturing subsector:

• Agricultural products lead the way, with 43% of manufacturers reporting full implementation of AI.

• Chemical producers show strong momentum, with 35% having fully adopted the technology.

• Complex and industrial equipment manufacturers are moving more cautiously, with 31% reporting full deployment.

By geographic region:

• Canada stands out globally, with an impressive 60% of manufacturers fully implementing AI.

• Brazil (30%), South Korea (29%), and the Nordics (30%) are demonstrating steady progress.

• Japan, on the other hand, is taking a more reserved approach, with just 20% of respondents reporting full adoption.

Take agriculture, for example. AI enables products to move from reactive to proactive by identifying patterns across climate data, supply chain inputs, and commodity pricing. Based on these insights, they can anticipate disruptions (e.g., droughts or delivery delays) and adjust accordingly.

Without AI, forecasting crop yields, assessing soil health, and planning for demand would rely heavily on guesswork or outdated data. So, it’s no surprise that adoption in this sector is surging.

By contrast, complex and industrial equipment manufacturers often rely on decades-old machinery and enterprise systems that were not built with AI in mind. These infrastructures make integration difficult and expensive, slowing down progress. Industry players recognise the value of AI, but the practical hurdles require a cautious approach—which explains the low adoption rate.

Perhaps most striking is how emerging AI technologies are gaining traction. Despite generative AI—like ChatGPT and similar tools—being much newer to the market than predictive AI, 74% of sales, marketing, operations, service, and strategy teams have already implemented it, compared to just 47% for predictive AI.

The implementation challenge: what’s holding manufacturers back?

Despite the enthusiasm for AI, manufacturers face several significant barriers to successful implementation. These range from data locked away in legacy systems to the complex regulatory framework and a shortage of talent capable of bridging the gap between strategy and execution. Let’s see a few examples:

• Data security and privacy concerns (39%) top the list of challenges. Manufacturing data often contains sensitive intellectual property, customer information, and operational details that require robust protection. Without ironclad security and privacy frameworks, businesses remain hesitant to feed this data into AI systems.

• Implementation and maintenance costs (38%) present another major hurdle. While AI promises significant ROI over time, the initial investment and ongoing maintenance costs remain substantial. This is particularly true for manufacturers operating on thin margins.
• A midsize plastics manufacturer, for instance, might recognise the value of predictive maintenance but hesitate when faced with the cost of retrofitting sensors across dozens of legacy machines.

• Explainability and transparency of AI outputs (36%) rank third among implementation challenges. In manufacturing environments where decisions can impact product quality, safety, and regulatory compliance, “black box” AI solutions that can’t explain their reasoning present unacceptable risks.

• Integration difficulties with existing systems (33%) create practical barriers to implementation. Many manufacturers operate with a complex patchwork of legacy systems, making AI integration technically challenging. It’s like trying to plug a USB-C cable into a socket from the 1970s.

• Lack of internal expertise (32%) completes the top five challenges. The specialised knowledge required to implement and manage AI systems effectively remains in short supply, creating a talent gap that manufacturers struggle to fill. Many enterprises are trying to reskill existing engineers and IT staff while leaning on external partners for the heavy lifting.

A 2024 survey by the Manufacturers Alliance echoes these concerns. Nearly half of respondents (46%) flagged data security as a major issue, with an equal share expressing hesitation around AI’s impact on data privacy. Industry leaders also cited regulatory compliance (42%), legal implications tied to AI-driven decision-making (37%), and risks to product quality and safety standards (36%) as key barriers to adoption.

The data foundation: AI’s critical prerequisite

One of the most revealing insights from Salesforce’s report is how deeply data challenges are intertwined with AI implementation hurdles. With manufacturing data volumes projected to grow by more than 22% annually, enterprises face a dual reality: greater potential for AI-driven innovation but also increased complexity in managing, securing, and leveraging data effectively.

Most manufacturers surveyed by Salesforce agree that much of their data is inconsistent, hard to access, or trapped in silos. Here’s what the numbers say:

• 48% of technical decision-makers admit they can’t fully trust the accuracy of their data.

• 78% report spending significant time searching for information across disconnected systems.

• Only 50% or fewer say the data they need is integrated from disparate sources and compiled in a single location.

These figures explain why manufacturers are prioritising data quality, AI development, and enterprise-wide data literacy as key strategic initiatives.

Not surprisingly, 85% of technical decision-makers agree that AI is only as good as the data it runs on. Without reliable, accessible, and integrated data, even the most advanced implementations risk falling short of their potential.

Bridging the gap: strategies for successful AI implementation

AI implementation may be complex, but you can position yourself for success through an iterative approach. Follow these best practices to build momentum, mitigate risk, and unlock long-term value:

1. Start with a strong data foundation

First, make sure you have a strong data foundation in place. If the information feeding your AI is outdated, incomplete, or inaccurate, the insights will be just as unreliable.

• Standardise how you collect and validate data to improve its accuracy and reliability.

• Integrate your data across systems and departments to achieve a unified view that supports smarter decision-making.

• Consider using a platform like Salesforce Manufacturing Cloud to unify customer, operations, and sales data into a single source of truth.

• Foster data literacy enterprise-wide so your teams can effectively use and interpret data.

For example, start by aligning data formats across production systems to eliminate inconsistencies. Use IoT sensors and automation to capture real-time data from manufacturing equipment and processes, then store it in the cloud to make it easily accessible for AI applications.

2. Prioritise high-value, low-complexity use cases

Large-scale AI implementations add a whole new layer of complexity, increasing the risks involved. If something goes wrong, it can disrupt production lines, compromise data integrity, or erode employee trust in the technology.

The real wins often come from small but impactful projects with fewer integration hurdles. That’s why manufacturers should:

• Identify specific use cases with clear ROI potential, such as predictive maintenance, quality control, or demand forecasting.

• Start with focused implementations that require minimal integration with critical systems.

• Build on successes to gradually expand AI applications.

An example of a high-value, low-complexity use case would be predictive maintenance or automated quality checks. For instance, a packaging plant may use sensors and AI models to predict when conveyor belts need servicing.

3. Address security and transparency proactively

Enterprises cannot achieve AI success without first addressing fundamental data challenges—like those related to security. That’s why it’s essential to:

• Develop comprehensive data governance and security frameworks before scaling AI initiatives.

• Select AI solutions that prioritise explainability and transparency.

• Establish clear protocols for validating AI outputs, particularly in quality-critical applications.

• Cater for the need to tackle biases and toxicity in AI outputs.

This proactive approach can reduce risk while building trust among employees and stakeholders. At the same time, it lays the groundwork for responsible AI adoption and streamlines regulatory compliance.

For example, a chemical producer rolling out AI to optimise formulas may enforce strict role-based access controls. Engineers can view performance metrics, but only compliance officers have visibility into sensitive inputs. This practice would help prevent data leaks and ensure transparency for regulatory teams or other authorised parties.

4. Build internal expertise while leveraging external resources

According to the Manufacturers Alliance survey, 41% of manufacturing companies collaborate with educational institutions and industry partners to build AI-focused workforce development pathways. More than one-third regularly assess their teams to identify skill gaps and training needs, while 40% offer tailored programs to upskill employees in AI technologies.

Respondents also said they support their staff through rotational programmes (26%), cross-functional knowledge sharing (29%), or mentorship and coaching (17%). However, such initiatives take time to deliver results—and you may need to lean on external experts until your team members develop the skills to tap into AI’s full potential.

With that in mind, partner with specialised AI solution providers for initial implementations. Fluido’s experts can guide you through this journey and maximise user adoption through custom training courses.

As AI adoption expands, build internal centres of excellence to accelerate learning and encourage best practices. These can serve as internal hubs for AI expertise, paving the way to a culture of innovation.

5. Integrate AI into broader digital transformation initiatives

Successful manufacturers view AI not as a standalone technology, but as an integral component of their overall digital transformation, including:

• Customer relationship management (CRM): 47% of the manufacturers surveyed by Salesforce have fully implemented CRM systems to streamline sales and service operations.

• Supply chain management solutions: With an adoption rate of 46%, these tools help improve visibility, efficiency, and collaboration across the value chain.

• Enterprise resource planning (ERP): 45% of manufacturing companies have integrated ERP systems into their operations to unify business functions and support data-driven decision-making.

These technologies amplify AI’s potential, and the other way around. They complement each other, tapping into the power of data to unlock opportunities for growth and innovation.

The path forward: from experimentation to value creation

Most manufacturers acknowledge the power of AI and what it means to business. At this point, they are no longer debating whether to adopt this technology, but how to implement it effectively.

To reap the benefits, industry players must bridge the gap between experimentation and value creation, which requires the following:

• A strategic approach that aligns AI initiatives with core business objectives

• Patience and persistence through the inevitable challenges of implementation

• A foundation-first mindset that prioritises data quality, integration, and governance

• Cross-functional collaboration between IT, operations, and business leadership

By addressing these requirements methodically, manufacturers can move past implementation hurdles and tap into AI’s transformative potential. Those who succeed won’t just boost operational efficiency but also unlock new business models, enhance customer experiences, and drive innovation in ways that were previously impossible.

With deep experience in Salesforce and AI, Fluido can help you connect people, processes, and technologies as part of your transformation journey. Reach out to a consultant to find out more or explore our Quick Start Packages for the manufacturing industry to see what we can do for your business.