Small Manufacturing Businesses Can Beat Slow Adoption of AI Technologies

The global AI market is forecast to hit US $1.3 trillion by 2032, Bloomberg research reveals. AI solutions have been transforming data management and application processes for companies worldwide. According to a Deloitte survey, the manufacturing industry, in particular, is generating the most data, creating over 1,812 petabytes of data annually.

However, smaller manufacturing companies face challenges that prevent the widespread adoption of AI technologies, largely revolving around the fragmentation of data. Additional hurdles also arise from a lack of AI-related skills in the workforce.

As a result, there’s been a significant drop in the number of manufacturers planning to increase AI spending this year: A recent report from CIO shows that this has dropped from 93% in 2023 to 53% in 2024.

Let’s dive into the main challenges the industry is facing with AI adoption and how to curb these.

The major hurdle of fragmented data 

Manufacturers produce huge swathes of data in their day-to-day operations and these data outputs come from all over, including the plant floor, logistics, and wider departments such as HR and finance. A significant issue is that departments are often siloed, resulting in slow and inefficient data sharing across the organization. In fact, a Cisco survey shows that siloed or   fragmented data is a major reason why 81% of companies have been reluctant to adopt AI technology.

Related:AI Makes Slow but Sure Progress in Manufacturing

This data fragmentation also undermines manufacturers’ capabilities to collect good quality data that is reliable for wider use-cases in AI adoption. For instance, algorithms rely on high-quality, sound data inputs to develop over time to maximize their efficiency to produce predictive analytics that yield results for manufacturers looking to optimize their operations.

One solution is to overhaul existing data procedures within your organization. This means adopting a data management plan for business strategies and operations that aims to unify and streamline data sharing across the company. As part of this approach, it’s vital for manufacturers to prioritize the collection, management, and collation of company-wide data, whether that’s from the factory floor or wider departments. 

Yet fragmented data is not only a problem unique to siloed departments. Achieving data harmonization across machinery, particularly when introducing new technology alongside legacy equipment, is a significant challenge that manufacturers continue to contend with. Calibrating the multitude of data points that are involved with manufacturing operations can be a painstaking process—and if not done correctly, can cause harm to personnel and exacerbate issues around operational downtime.

Related:Manufacturers Grapple with AI and Robotics

High-quality data are needed for optimal insights

It’s also worth noting that an overhaul of existing data processing not only focuses on how data is collected but the quality of the data too. A crucial factor is that many AI models, including machine learning (ML) algorithms, natural language processing (NLP), computer vision, and digital twins—all widely used in many manufacturing processes—rely on high-quality data inputs to generate optimal insights.

Strong data, accrued through robust data collection and management models, is a non-negotiable way for enabling these solutions to help manufacturers automate repetitive tasks, thus freeing up more manpower to enhance logistical and production outputs. The added complexity of ensuring data quality alongside the difficulties surrounding its calibration is an underlying reason for the workforce’s ongoing reluctance to accelerate AI adoption. 

The role of generative AI

Some industry leaders have turned to generative AI (GenAI) or synthetic data to address ongoing data pains. This helps organizations circumvent the question of acquiring high-quality data necessary for deep learning models.

As the name suggests, GenAI technology can generate data, such as text or even images, usually in response to prompts—a renowned example is ChatGPT. GenAI has proven to be a pivotal solution for many in the industry in producing the data needed to inform AI models and algorithms.

Another key use case of GenAI is in computer vision, an increasingly popular technology across the manufacturing industry. Computer vision aids the technology in refining automation processes in addition to data collection and management. Computer vision is emerging as a transformative tool enabling manufacturers to capture, process, and analyze real-world visual data. This is also a great solution for augmenting safety protocols by spotting potential risks on the factory floor. Moreover, computer vision can help teams with repetitive tasks, freeing up their time and energy to focus on other responsibilities.

GenAI manufacturing benefits are also reaped in reducing machinery downtime energy consumption and helping operators optimally use machinery. However, it’s important to reiterate the importance of quality data input, as system errors can carry health and safety consequences on the factory floor. 

Strategies for stimulating adoption

It’s imperative for leaders in manufacturing companies to bridge the skills gap across their workforce. Importantly, this issue is not unique to those working on the factory floor alone—cross-organizational awareness needs to be nurtured around the power of AI technology and how embracing these solutions can transform business outcomes.

Smaller manufacturing companies are encountering an additional hurdle in their executive leadership. Some business leaders feel trepidation to utilizing AI which stems from an inherent lack of awareness on how it can be integrated into existing processes.

The data audit is the first step

Assessing current logistical and operational processes, including taking an audit of data across these, is the first step. This will help organizations spot gaps and pinpoint needs to better formulate an action plan for adopting and integrating AI technologies into the organization. 

Alongside this, team leaders can employ the Observe, Orient, Decide, Act technique as a useful methodology to familiarize associates with valuable steps. Additionally, setting three operational KPIs—yield, up-time, and change-over time—can help associates (and other team members) understand the ultimate operational objectives of using the system.

Another recommendation is to make associates aware of shortening time to notice (Observe), time to consult and decide (Orient and Decide), and time to process (Act) as direct impacts from the system. This will help streamline the adoption process, particularly among the executive leadership.

Sanitary data processing should be a priority

Furthermore, introducing in-depth and engaging skills development programs across the organization is a powerful way of closing the skills gap among teams. This also helps alleviate AI anxiety—many employees perceive AI as a threat rather than a benefit to their professional well-being. One potential game-changing training method for easing this anxiety is employing AI tools in workshops or activities. For instance, virtual reality or natural language processing solutions can be used to provide real-world, engaging AI learning experiences.

Finally, versing teams on the importance of sanitary data processing and collection methods should be a top priority; this includes educating employees on the consequences of poor data in terms of health and safety, business gains, and logistical outcomes. Training teams on good data housekeeping ties back to the aforementioned overhaul of existing data management in your organization. This will help manufacturing companies adopt in-depth skills-building opportunities that correlate with teams and their degree of data and operational responsibility.

These methodologies won’t be adopted overnight but by following these recommendations, organizational leaders can more seamlessly and effectively implement AI technology into their current operations. Ensuring immersive and engaging training is imperative to bolstering widespread adoption of emerging tools, as well as augmenting skills and employee confidence to avoid risks on the factory floor.