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While AI has taken the centerpiece across business strategy in organizations worldwide, what’s striking from one of the top 10 key takeaways as per the Artificial Intelligence Index Report 2023 published by Stanford University, is that for the first time in the last decade, year-over-year private investment in AI decreased.
As per the report, global AI private investment was $91.9 billion in 2022, which represented a 26.7% decrease since 2021. There has been a decrease in the total number of AI-related funding events as well as the number of newly funded AI companies.
At the same time, the report further highlights that organizations that have adopted AI have realized meaningful cost decreases and revenue increases.
The Artificial Intelligence Index Report 2023 further summarizes that the three most adopted AI use cases in 2022 were service operations optimization (24%), new AI-based products (20%), and customer segmentation (19%).
Despite the challenges in terms of investments, regulations, and potential misuse of AI, today, it is essential for organizations to include AI strategy as a core part of their organizational business strategy.
Moreover, it is also imperative to link the AI strategy to business outcomes and deliver these outcomes through successful implementations, which would fuel further investments in AI.
Based on McKinsey’s Global Survey on AI 2022, the function that most respondents saw decreases in cost because of AI adoption was Supply Chain Management (SCM) (52%).
Identifying SCM use cases for prototyping, and consequently scaling up to meet the needs of the enterprise has shown momentum in recent years. One such growth area happens to be Supply Chain Risk Management (SCRM).
SCRM is a growing field of study aimed at ensuring business continuity and contributing to business performance. SCRM is still evolving, and researchers in the past have proposed multiple definitions of SCRM.
One frequently cited definition of SCRM covers four constructs, namely, (a) supply chain risk sources, (b) risk consequences, (c) risk drivers, and (d) risk mitigation.
Due to the after-effects of COVID-19, increasing complexity in supply chain processes, trade barriers, and growth of just-in-time processes, SCRM has become essential to ensure business continuity and operational performance of organizations.
However, many organizations do not invest or focus on SCRM. SCRM has the potential to enable early and proactive identification of supply chain risks along with predicting the causes of such risks, leading to reduced disruptions and financial losses.
Proactive supply chain strategies help to build resilient supply chains. AI can play an important role in SCRM by proactively identifying, assessing, mitigating, and responding to supply chain disruptions. AI techniques like computer vision, deep learning, natural language processing, and other machine learning techniques including the capabilities of Generative AI, have fuelled AI adoption.
AI has the potential to proactively identify risk in supply chains and provide insights for effective risk response. Some of the applications of AI are autonomous drones, factory automation, cybersecurity, finance, and hazardous environments.
Furthermore, past research involving AI in SCRM has forecasted the level of integration required with the supply chain to minimize risks and AI techniques to analyze risk exposure in supply chains using what-if analysis and stress testing.
At the same time, there is a need to study architectural paradigms for the implementation of AI at an enterprise scale. To ensure that AI in the supply chain risk can be used in practice at an organization, a suitable information systems architecture is a necessity. Another critical aspect is to study the deployment of the AI models for prediction either in a batch or online mode enabling decision-making.
New use cases involving audio and video streaming, human gestures, and integration of AI with other fields like robots, drones, edge computing, augmented reality, and others need to be studied in detail and new architectural paradigms evolved.
However, business leaders need to be aware of the factors influencing AI adoption. Recent research studies have used the Technology–Organization–Environment (TOE) framework to study AI adoption processes.
In a study conducted across select industries in India, namely, manufacturing, wholesale trade, retail trade, and transportation, the research identified that AI adoption in SCRM is influenced by integrated data management, complexity, talent, organizational agility, and disruption impact.
Moreover, AI routinization or usage in SCRM was influenced by complexity, cost of ownership, top management support, enterprise risk management alignment, disruption impact, regulatory uncertainty, and AI implementation in SCRM.
For business leaders to justify and secure funding opportunities for AI, an integrated approach is the need of the hour. Driving business outcomes, identifying relevant business use cases, building for scale, addressing data quality challenges, and adopting sustainable practices are key elements of focus AI leaders should bring forth that benefit organizations and humankind in general.