
Without a doubt AI technologies are taking the business world by storm. The path to an intelligent manufacturing supply chain is now within reach. The allure of this shiny new technology stack and its promise to supercharge a company's business is irresistible. A significant 55% of executives anticipate consequential and imminent supply-chain disruptions to their sectors according to the latest Economist Impact Survey, but the prize of driving operational efficiency, visibility and enhanced supply chain collaboration makes the challenges worth it to more than 68%.
Whisper it though, in spite of billions being poured into AI consultancy and tech — into nascent AI supply chain solutions and miraculous manufacturing productivity tools — we are facing a counterintuitive reality. Instead of easing workload and making manufacturing more automated, employees are burning out and AI projects are languishing as many puzzle how to turn algorithmic code into supply chain gold.
Nearly half of employees are clueless as to how to make the productivity gains leadership are expecting of them to conjure up with generative AI (GenAI) tools and assistants. Even worse, 77% report increased workload from normal when trying to utilise AI in task completion. For many it is a pain point and obstacle rather than a net positive magical solution.
For supply chain managers it is even more head-scratching. 88% confess that company IT infrastructure and systems need significant upgrades to fully leverage generative AI. For an additional 10% it is even worse: they say company IT would need a complete overhaul. Bridging the gap from strategic planning to practical implementation requires effort, expertise, expenditure and innovation.
We know generative and predictive AI will revolutionise industrial output. It is already having an impact. We also know how resilient and resourceful UK manufacturing and supply chain can be. We have strong foundations in industrial production and logistics, a high skills workforce and a longstanding innovative engineering culture. So let's examine this productivity paradox, and look at ways we can utilise these assets to solve it and bring technological and human ingenuity together.
We need to square AI integration challenges with legacy systems, ongoing operations, workforce, skilling and other human elements.
AI dreams and supply chain realities
AI promises to optimise up to 50% of supply chain operations within three years, according to 65% of chief supply chain officers. However, the reality is far more complex. Poor integration with legacy systems and a lack of workforce training have left many employees overwhelmed, without the necessary skills for the GenAI transition.
AI can improve operational efficiency no doubt — but while it is developing and some way from an all-powerful AGI (artificial general intelligence) — it is paramount that humans have oversight to assess quality of output, and weed out confabulations of false but seemingly plausible data. These could easily cause more harm than good in mission critical operations.
Critical thinking and expertise in using these new tools is paramount, yet only 26% of organisations have so far implemented AI training programmes (according to studies by the Upwork Research Institute). Much of the workforce is flying blind in attempting to use it. It's akin to asking people to miraculously speak a language without knowing the basic grammar.
We can take heart from acknowledging the current situation has historical precedent. The productivity paradox — where the potential of new technology and the productivity gains it can unlock lag behind innovation and access — is a common theme that has been experienced in the past. Adoption requires familiarity and expertise, not simply executive edicts from on high. While 96% of C-suite leaders believe AI will boost productivity they need to be careful not to mismatch the state of AI readiness and capability with what is available, efficient and reliable in the current versions of AI tools. We've solved these issues and transitioned to new ways of working before, we shall do it again. Integration of AI into human workflows will take time, and with the right training programmes, clear policies and usage boundaries, along with phased implementation, progress toward this can be accelerated.
We have big problem areas this new tech can tackle: unconnected inventory systems along with piecemeal and incompatible automation technologies creates inefficiencies and breakdowns in operational effectiveness. That is why integration of systems must also correspond with human integration for workflow. Marrying these two streams is the primary success factor for optimal AI implementation.
Business motivations for applying GenAI in supply chain processes

Chaos without compatibility: integration is key
OEM manufacturing is marked by complexity. Requirements of high technology product and complicated assembly often result in a long and complex supply chain with hundreds if not thousands of components that must be quality assured and meet exacting specifications.
These all present AI adoption challenges in manufacturing supply chain. And conversely, they also offer real opportunities for AI to aid in managing these complex operations, the demand planning of inventory, and the coordination of delivery and enhancement of supply partner interoperability. The Economist study identifies the top priority in using GenAI in procurement and supply chain is to actually increase resilience and security (according to 63% of executives). Scenario planning and rapid response to disruptions play to the strengths of AI in crunching data for analysis, and pattern recognition for predictive insights.
We've already mentioned the supply chain IT problem regarding AI system integration. GenAI is being held back by legacy systems, as only 12% of organisations describe their infrastructure as being fully capable of integrating this technology stack. And disconnected, faultily integrated systems don't just slow operations but amplify risks. Poor or corrupted data synchronisation can cause inventory chaos, mismatched stock levels, delayed shipments and unoptimised buffering of componentry that erodes profitability and manufacturing efficiency.
Incompatible systems greatly increase the risks of data leakage, privacy issues, and securing sensitive supply chain data. 53% cite data security as a critical concern in deploying GenAI. We've seen instances of AI models leaking data and their guardrails and safeguards being cracked before. Siloed systems stop AI from accessing the full spectrum of data needed to achieve its full potential in being a proactive rather than reactive tool, but connecting silos concurrently increases the need for data security and elimination of 'backdoor' access. Vulnerability to the whole system is increased. Balancing these two issues while also fulfilling data compliance legalities is yet another challenge for GenAI's organisational implementation.
The strategic solution to these dilemmas: well thought out planning, phased introduction, thorough security testing, targeted IT investment to modernise IT infrastructure and ensure interoperability. This also extends to human workflows, with clear protocols and robust training. AI then becomes a bridge to efficiency rather than a bottleneck to productivity.
AI adoption, from stock-outs to burn out
AI can help automatise tasks and provide more operational efficiency, but a company runs on people. Alarmingly 1 in 3 people are considering leaving their jobs within 6 months, overburdened by the expectations of utilising and unlocking big productivity gains with AI yet without the training and guidance to do so. 71% report burn-out as GenAI in its current state has actually increased their workload, with routine tasks sometimes taking longer with AI. Result? Unintended reduction in productivity. AI tools still require an eagle eye and overwatch to check results and critical assessment.
74% of employees think their organisation needs an overhaul of how their productivity is measured, with 54% saying the company doesn't have an accurate picture of how productive they are. Working to co-create workflows and evaluation metrics that emphasise innovation, strategy and creativity rather than raw speed and efficiency offers a solution. While the latter more obvious metrics are easier to measure, the former ones empower employees and allow the building of better customer relationships and organisational adaptability, agility and innovation that contribute more strategically to the company's bottom line in the long run.
Full utilisation of AI's potential within a business means fundamentally shifting the way talent and work is organised, undergone and assessed. Time, effort and training to thoughtfully integrate AI workflows and appreciate the limits and strengths of its capabilities to make the best work are necessary. AI unlocks talent from repetitive tasks to work on more value-added, richer and more complex activity. Data analysis and interpretation, problem solving and critical thinking, collaboration and communication are the most critical personal skills for an AI-enhanced workforce in procurement and supply chain.
Companies must urgently create strategic plans and implement training if they don't want to lose their assets of talent, and workforce investments. Supply chain officers expect the integration of GenAI to create more new job opportunities (49%), outweighing threats to job displacement due to automation (14%).
Effectively integrate AI into your supply chain
To save your company and people drowning in ill-implemented GenAI technology we need to follow a simple pathway to success:
Step 1: Conduct a System Audit
What are the capabilities of legacy systems? What needs upgrading, eliminating and replacing? Assess for compatibility and identify resource/data gaps. Identify strategic IT infrastructure investments.
Step 2: Invest in Workforce Training
AI proficiency amongst full-time employees is 35% (according to the Upwork study). Identify what up-skilling and dedicated training is needed. Allow time to experiment and implement without fairytale expectations. Expect to iterate and improve incrementally rather than as a big bang. Improve AI literacy with in-house training, monitor and reduce employee frustration.
Step 3: Co-Create AI Adoption Strategies
Involve employees and managers in designing AI workflows to ensure alignment with daily operations. Make sure systems are actually easy to implement and use rather than overly technical and time-consuming to understand.
Step 4: Develop Resilient Data Systems
Educate everyone on security risks, data privacy and best practices. Implement robust data infrastructure to enable smooth AI operations with diligent cybersecurity measures. Security software needs regular updating, with systems in place for data backup, lockdown, with data quarantining as needed.
Building the AI-Ready OEM supply chain of the future
OEMs can build resilient, AI-enhanced supply chains with the right proactive partners. AI-to-AI supply chain communication for seamless operations may soon be possible using the newer agentic capabilities of AI. The potential for coordinating and translating human decisions into automated actions offers clear operational innovation.
The capability of GenAI to drive more responsible and sustainable operations is exciting and much needed. It can be charged with identifying operational inefficiencies and optimising resource usage to comply with sustainability targets and Scope 3 emissions regulations. These new legal requirements need complex monitoring, analysis and collaboration with suppliers. AI can optimise routes and delivery schedules, cut carbon emissions, and address and monitor emissions for greener supply chains.
Good people makes great AI
Technology alone isn't the answer. Technology is always about people. That's why at Acorn we are prudently implementing GenAI, using AI critically for inventory and planning tasks, but still offering expert human service and customer experience. Together these things allow us to create the bespoke service our clients love.
AI holds immense promise for OEM manufacturers and their logistics, but its success lies in thoughtful integration, robust systems, capable partners, and empowered workers. An intelligent supply chain of the future won't stand out through simply automation, but with innovation and the human touch.
Sources — Next-Gen Supply Chains, Economist Impact research study. — From Burn-out to Balance: AI-Enhanced Work Models, Upwork Research Institute.
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