The manufacturing industry is in an unenviable position. Facing a constant onslaught of cost pressures, supply chain volatility and disruptive technologies like 3D printing and IoT. The industry must continually optimize process, improve efficiency, and improve overall equipment effectiveness.

At the same time, there is this huge sustainability and energy transition wave. Manufacturers are being called to reduce their carbon footprint, adopt circular economy practices and become more eco-friendly in general.

And manufacturers face pressure to constantly innovate while ensuring stability and safety. An inaccurate AI prediction in a marketing campaign is a minor nuisance, but an inaccurate AI prediction on a manufacturing shopfloor can be fatal.

Technology and disruption are not new to manufacturers, but the primary problem is that what works well in theory often fails in practice. For example, as manufacturers, we create a knowledge base, but no one can find anything without spending hours searching and browsing through the contents. Or we create a data lake, which quickly degenerates to a data swamp. Or we keep adding applications, so our technical debt continues to increase. But we are unable to modernize our applications, as logic that is developed over the years is hidden there.

The solution lies in generative AI  

Let’s explore some of the capabilities or use cases where we see the most traction:

1. Summarization

Summarization remains the top use case for generative AI (gen AI) technology. Coupled with search and multi-modal interaction, gen AI makes a great assistant.  Manufacturers use summarization in different ways.

They may use it to design a better way for operators to retrieve the correct information quickly and effectively from the vast repository of operating manuals, SOPs, logbooks, past incidents and more. This allows employees to focus more on their tasks and make progress without unnecessary delays.

IBM® has gen AI accelerators focused on manufacturing to do this. Additionally, these accelerators are pre-integrated with various cloud AI services and recommend the best LLM (large language model) for their domain.

Summarization also helps in n harsh operating environments. If the machine or equipment fails, the maintenance engineers can use gen AI to quickly diagnose problems based on the maintenance manual and an analysis of the process parameters.

2. Contextual data understanding

Data systems often cause major problems in manufacturing firms. They are often disparate, siloed, and multi-modal. Various initiatives to create a knowledge graph of these systems have been only partially successful due to the depth of legacy knowledge, incomplete documentation and technical debt incurred over decades.

IBM developed an AI-powered Knowledge Discovery system that use generative AI to unlock new insights and accelerate data-driven decisions with contextualized industrial data. IBM also developed an accelerator for context-aware feature engineering in the industrial domain. This enables real-time visibility into process states (normal/abnormal), alleviates frequent process obstructions, and detects and predicts golden batch.

IBM built a workforce advisor that uses summarization and contextual data understanding with intent detection and multi-modal interaction. Operators and plant engineers can use this to quickly zero in on a problem area. Users can ask questions by speech, text, and pointing, and the gen AI advisor will process it and provide a response, while having awareness of the context. This reduces the cognitive burden on the users by helping them do a root cause analysis faster, thus reducing their time and effort.

3. Coding Assistance

Gen AI also helps with coding, including code documentation, code modernization, and code development. As an example of how gen AI helps with IT modernization, consider the Water Corporation use case. Water Corporation adopted Watson Code Assistant, which is powered by IBM’s gen AI capabilities, to help their transition into a cloud-based SAP infrastructure.

This tool accelerated code development by using AI-generated recommendations based on natural language inputs, significantly reducing deployment times and manual labor. With Watson Code Assistant, Water Corporation achieved a 30% reduction in development efforts and associated costs while maintaining code quality and transparency.

4. Asset Management

Gen AI has the power to transform asset management.

Generative AI can create foundation models for assets. When we must predict multiple KPIs on the same process or there is a fleet of similar assets. It is better to develop one foundation model of the asset and fine-tune it multiple times.

Gen AI can also train for predictive maintenance. Foundation models are very handy if failure data is scarce. Traditional AI models need lots of labels to provide reasonable accuracy. However, in foundation models, we can pretrain models without any labels and fine-tune with the limited labels.

Also, generative AI can provide technician support and training. Manufacturers can use gen AI technologies to create a training simulator for the operators and the technicians. Further, during the repair process, gen AI technologies can provide guidance and generate the best repair procedure.

Build new digital capabilities with generative AI

IBM believes that the agility, flexibility, and scalability that is afforded by generative AI technologies will significantly accelerate digitalization initiatives in the manufacturing industry.

Generative AI empowers enterprises at the strategic core of their business. Within two years, foundation models will power about a third of AI within enterprise environments.

In IBM’s early work applying foundation models, time to value is up to 70% faster than a traditional AI approach. Generative AI makes other AI and analytics technologies more consumable, which helps manufacturing enterprises realize the value of their investments.

Build new digital capabilities with generative AI

Stone X Group’s chief strategist, Kathryn Vera, gave a presentation at the Miami conference, stating that bitcoin won’t be a reserve currency – economics jargon for a currency like the dollar, euro or yuan held by central banks to support global trade and finance – “in her lifetime.” A key reason why the largest conventional currencies are cornerstones of finance is this reserve currency status.

The latest findings of the IBM X-Force® Threat Intelligence Index report highlight a shift in the tactics of attackers. Rather than using traditional hacking methods, there has been a significant 71% surge in attacks where criminals are exploiting valid credentials to infiltrate systems. Info stealers have seen a staggering 266% increase in their utilization, emphasizing their role in acquiring these credentials. Their objective is straightforward: exploit the path of least resistance, often through unsuspecting employees, to obtain valid credentials.

Organizations have spent millions developing and implementing cutting-edge technologies to bolster their defenses against such threats, and many already have security awareness campaigns, so why are we failing to stop these attacks?

Challenges of traditional security awareness programs

Most security awareness programs today provide employees with information they need about handling data, GDPR rules and common threats, such as phishing.

However, there is one major weakness with this approach: the programs don’t consider human behavior. They typically follow a one-size-fits-all approach, with employees completing annual generic computer-based training with some slick animation and a short quiz.

While this provides necessary information, the rushed nature of the training and lack of personal relevance often results in employees forgetting the information within just 4-6 months. This can be explained by Daniel Kahneman’s theory on human cognition. According to the theory, every individual has a fast, automatic, and intuitive thought process, called System 1. People also have a slow, deliberate and analytical thought process, called System 2.

Traditional security awareness programs primarily target System 2, as the information needs to be rationally processed. However, without sufficient motivation, repetition and personal significance, the information usually goes in one ear and out the other.

It is crucial to understand employees’ behaviors

Nearly 95% of human thinking and decision making is controlled by System 1, which is our habitual way of thinking. Humans are faced with thousands of tasks and stimuli per day, and a lot of our processing is done automatically and unconsciously through biases and heuristics. The average employee works on autopilot, and to ensure that cybersecurity issues and risks are ingrained in their day-to-day decisions, we need to design and build programs that truly understand their intuitive way of working.

To understand human behavior and how to change it, there are a few factors we must assess and measure, supported by the COM-B Behavior Change Wheel.

• First, we need to know employees’ capabilities. This refers to their knowledge and skills to engage in safe online practices, such as creating strong passwords and recognizing phishing attempts.
• Then, we need to identify whether there are sufficient opportunities for them to learn, including the availability of resources such as training programs, policies and procedures.
• Lastly, and most importantly, we need to understand the level of employee motivation and their willingness and drive to prioritize and adopt secure behaviors.

Once we understand and evaluate these three areas, we can pinpoint areas for behavioral change and design interventions that target employees’ intuitive behaviors. Ultimately, this approach aids organizations in fostering a first line of defense through the development of a more cyber aware workforce. 

We need to foster a positive cybersecurity culture

Once the root causes of behavioral issues are identified, attention naturally shifts toward building a security culture. The prevailing challenge in cybersecurity culture today is its foundation in fear of error and wrongdoing. This mindset often fosters a negative perception of cybersecurity, resulting in low completion rates for training and minimal accountability. This approach requires a shift, but how do we accomplish it?

First and foremost, we must reconsider our approach to initiatives, moving away from a solely awareness-focused, compliance-driven model. While security awareness training remains vital and should not be overlooked, we must diversify our educational methods to foster a more positive culture. Alongside broad organizational training, we should embrace role-specific programs that incorporate experiential learning and gamification, such as the engaging cyber ranges facilitated by IBM X-Force. Furthermore, organization-wide campaigns can reinforce the notion of a positive culture, involving activities like establishing a network of cybersecurity champions or hosting awareness months with diverse events.

Once these initiatives are selected and implemented to cultivate a positive and robust cybersecurity culture, it’s imperative that they receive support from all levels of the organization, from senior leadership to entry-level professionals. Only when there is a unified, affirmative message, can we truly transform the culture within organizations.

If we don’t measure human risk reduction, we don’t know what works

Now that we’ve identified the behavioral challenges and implemented a program aimed at fostering a positive culture, the next step is to establish metrics and parameters for success. To gauge the effectiveness of our program, we must address a fundamental question: to what extent have we mitigated the risk of a cybersecurity incident stemming from human error? It’s crucial to establish a comprehensive set of metrics capable of measuring risk reduction and overall program success. Traditionally, organizations have relied on methods such as phishing campaigns and proficiency tests, with mixed results. One modern approach is risk quantification, a method that assigns a financial value to the human risk associated with a specific scenario. Integrating such metrics into our security culture program enables us to assess its success and continuously enhance it over time.

Collaborate with IBM and build the human firewall

The shifting landscape of cybersecurity demands a comprehensive approach that addresses the critical human factor. Organizations need to cultivate a positive cybersecurity culture supported by leadership engagement and innovative initiatives. This needs to be coupled with effective metrics to measure progress and demonstrate the value.

IBM offers a range of services to help our clients pivot their programs from awareness to focus on human behavior. We can help you assess and tailor your organization’s interventions to your employees’ motivations and habits, and help you foster a resilient first line of defense against emerging threats by empowering every individual to be a proactive guardian of cybersecurity.

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