Artificial Intelligence (AI) has found its way to increase sales and margins while causing conspiracies in almost every solution in the supply chain. To gain a competitive advantage or to maintain a relevance, many organizations are rushing to adopt AI in a way that can be superficial, ineffective and ultimately real value.
For example, in the field of supply chain applications, only a small fraction of AI applications provide real value through a solid return on investment. AI is a reality, but many solutions are unable to address the needs of the company. The key is to look past the noise and allow implementations to fulfill their promises.
AI value gap
Despite the heavy investment in AI-powered supply chain solutions, most organizations struggle to realize meaningful benefits. Cutting is often driven by the hype surrounding AI, where excitement can overshadow real value. Gartner's research showed that Generated AI is deployed by 72% of supply chain organizationsthere are only modest results of productivity and ROI so far.
Challenges exist on both the vendor and the buyer side. First, do vendors properly express expectations for desired AI capabilities, clearly define issues to address, and detail the expected targets? Do supply chain leaders have enough insight to distinguish between genuine innovation and sophisticated marketing, or do they understand how to derive the most value from the tool?
Decision makers sometimes fall prey to buzzwords and overlook demonstrable performance improvements. Supply chain planning requires accurate and context-specific calculations within complex, volatile environments where current AI models cannot be delivered reliably without special design or without serious risks. Inconsistent with supply chain complexity allows users to reduce value rather than add value.
AI doesn't sum
The mathematical complexity of supply chain operations is a critical challenge for common AI applications. Supply chain management requires sophisticated algorithms that can simultaneously handle multiple signals, such as demand patterns, industry trends, seasonality, and broader market power.
Generation AI cannot manage only these issues (yet). Big data can provide guidance for creating better demand models that explain multiple dimensions and probabilities. However, effective decision-making also requires modeling of management goals and supply factors. Both are very unstable and unknown. Generated AI systems that are not properly processed and trained with calibrated data in these areas tend to hallucinate, producing unreliable outputs, leading to user mistrust and system failures. Without trust, these systems will ultimately fail.
AI as a decision support system
AI can act as a support system for decision making and help businesses to price and dynamically manage inventory level values by predicting, planning and implementing more accurate product decisions.
The most successful AI implementations are collaborative in nature and are designed to enhance decision-making rather than exchange decisions. However, many interconnected decisions cannot all be optimized at once. Managers should start with short-term or real-time decisions. This is easy to observe and improve.
Through this process, they are able to be confident in the strengths and weaknesses of the system and understand reasoning rather than blindly accepting it. Only then can they progress gradually to medium-term and long-term decisions, such as longer learning journeys, given the inherent delays in the feedback loop.
User friction and transparency
Even if an AI solution can show improvements in critical areas, its value can be ignored if users don't understand or trust it. Consider this mathematical formula: value = Impact / Friction.
Many implementations fail because they do not provide demonstrable value. Even those who do so risk being abandoned due to workflow friction: adoption complexity, inappropriate training, lack of transparency. Without transparency, users tend to override the system, ignore recommendations, or return to manual processes to remove potential benefits. The focus should remain measurable results, but it should ensure that the interface builds trust through a clear explanation of recommendations.
More importantly, users need to focus on expanding their capabilities with translation. This means developing clear, action-defined instructions that the machine can properly interpret and understand.
Take, for example, Pitta Rosso's Harvard Business School Case.Artificial Intelligence-Driven Pricing and Promotions. ” Here we learned how important and difficult it is to define objective features that machines can implement efficiently.
Identifies the value
Decision makers can separate valuable AI implementations from superficial implementations.
● performance: Solutions need to improve specific metrics and make them faster. Leaders should request evidence of improved performance in environments similar to their own.
● User Experience: Solutions clearly explain language recommendations that make sense to supply chain professionals, creating transparency and trust.
● Use Case Value: Rather than broad capabilities across many functions, it focuses on clear and narrow challenges, especially at first.
A valuable solution must demonstrate deep integration with supply chain-specific solutions rather than standard AI capabilities. This means more than just a promise of empty, it means decades of proving empirable research and development efforts. With these evaluation criteria in mind, decision makers can focus on solutions that deliver real results.
Beyond the Buzz
As AI continues to evolve, leaders need to evaluate solutions based on their ability to address specific challenges. The most valuable implementations demonstrate value through clarity and performance. The influence is divided by friction, user-friendly design and mathematical refinement.
By focusing on features, value and return on investment, organizations can avoid expensive solutions they don't offer.
Success belongs to those who implement AI the most, not those who identify and deploy the right AI application with certain measurable goals in mind.
