Technical debt, like financial debt, is a concept in information technology where shortcuts, quick fixes or immature solutions used to meet immediate needs burden enterprises with future costs. This debt can significantly impact supply chain efficiency, especially as businesses face the pressures of staying competitive and agile in a post-pandemic world.
Madhumita Banerjee, Associate manager, Supply chain and manufacturing at Tredence, sheds light on how technical debt accumulates for Enterprises, and how generative AI can play a pivotal role in addressing these challenges.
Banerjee explained that in the context of supply chains, technical debt accumulates when outdated systems, fragmented processes, and manual, siloed workflows are used. Over time, these inefficiencies lead to increased operational costs, reduced responsiveness, and heightened exposure to risks, making it harder for companies to remain competitive.
One of the primary contributors to technical debt, according to Banerjee, is the reliance on legacy systems. “Many supply chains rely on outdated systems that are difficult to integrate with modern technologies, leading to increased maintenance costs and inefficiencies,” she noted.
These legacy systems, coupled with data silos where information is stored in disparate systems, create significant barriers to seamless information flow, which is critical for supply chain efficiency.
Manual processes also play a role in accumulating technical debt. Tasks requiring human intervention are prone to errors and delays, contributing to inefficiencies and higher operational costs.
As companies transitioned to digitalization, the rushed adoption of custom solutions and cloud migrations—often driven by the need to keep pace with technological advancements—introduced added complexity and heightened system maintenance burdens. Generative AI emerges as a pivotal new factor in this scenario. Although early adopters face new risks and the possibility of future debt with each generative AI deployment, the technology shows significant promise in addressing these challenges
The Role of Generative AI in Addressing Technical Debt
Banerjee emphasised that while analytics has historically helped connect data and enhance visibility, the emergence of generative AI, especially LLMs, marked a significant shift.
“Conversational AI and LLM-powered agents make it easier for functional partners—both technical and non-technical—to understand and act on complex data,” she explained. This is especially crucial in supply chains, where not all stakeholders, such as warehouse partners and freight workers, are tech-savvy.
One of the most significant advantages of generative AI in supply chain management is its ability to enhance data integration and visibility. For instance, in order processing, which traditionally involves many manual steps prone to errors, generative AI can automate the entire workflow—from order intake and validation to order confirmation—ensuring seamless communication across departments and reducing the need for manual intervention.
Generative AI also holds promise in optimising decision-making processes within supply chain platforms. However, Banerjee noted that the effectiveness of generative AI in this area depends on the maturity of the supply chain itself.
“For instance, if we have an LLM-powered event listener that detects market sentiments and links this information to the forecast engine, it can significantly narrow down the information demand planners need,” she said.
This level of optimisation requires a robust and connected data model where all data parts communicate effectively, enabling real-time insights and more accurate demand forecasts.
Predictive Analytics, Real-time Data Processing, and Compliance
Banerjee said that predictive analytics is another area where generative AI can revolutionise supply chain management. She recalled the evolution from traditional “what-if” analyses to more advanced machine learning algorithms that predict outcomes over time.
However, she pointed out that decision-making has now evolved to require not just predictions but also a deeper understanding of cause-and-effect relationships. “With GenAI, we can weave in causal discovery algorithms that translate complex data into actionable insights presented in simple English for all stakeholders to understand,” she added.
This capability is particularly valuable in areas like inventory forecasting, where understanding the root causes of forecast errors and deviations can lead to more accurate and reliable predictions. By translating these insights into easily digestible information, generative AI empowers supply chain managers to make more informed decisions, ultimately improving efficiency and reducing costs.
Speaking about real-time data processing being critical for the effectiveness of generative AI, Banerjee clarified that it’s not the AI that contributes to real-time processing but the other way around.
“We need to have real-time data to make sure we can analyse scenarios and use generative AI to its maximum potential,” she explained. For instance, ensuring that data entered into legacy systems is immediately available on the cloud allows LLMs to process and convert this data into actionable insights without delay.
In terms of compliance and risk management, generative AI can bolster efforts by removing manual interventions. Banerjee highlighted procurement and transportation as a key area where GenAI can enhance compliance. In transportation, where contracts are reviewed annually, GenAI-powered systems can query specific contracts, compare terms, and ensure adherence to key metrics like freight utilisation and carrier compliance.
Challenges and Future Outlook
Although generative AI offers numerous benefits, challenges still persist. Banerjee stressed the importance of properly vetting the fitment and maturity of Gen AI strategy. “Embarking on the GenAI journey may appear simple, but without a thorough assessment of need and fitment, along with strong investments in data quality, integration, and governance, companies are likely to deepen their technical debt.”, she added
One of the most significant concerns is the issue of “hallucination”, where AI models generate incorrect or misleading information. and validating the data on which AI models are trained to avoid garbage in-garbage out scenarios.
In summary, Banerjee ties the discussion back to the central theme of technical debt. By addressing the key contributors to technical debt—legacy systems, data silos, and manual processes—generative AI can help reduce future costs and risks, enabling companies to pursue digital initiatives with greater confidence.
“If we can successfully integrate GenAI into our systems, we can revolutionise the entire supply chain platform, making it more efficient, responsive, and competitive”, she concluded.
