Artificial intelligence has long been touted as an effective and efficient response in overcoming business and societal challenges that are complex in nature. In recent years, many organizations incorporated artificial intelligence solutions in their operations since the technology seemed very much futuristic, cutting edge, and competitive. In the boardroom, artificial intelligence had long been touted as a tool capable of automating work, lowering costs, and enhancing decision-making.
However, the on-the-ground reality was far from what was expected. Although it seemed quite promising in theory, there were instances where the applications of artificial intelligence were not creating any value for the people they were intended for. Instead, it was more about being noticeable, and it is here that the gap emerged.
Problem Statement
It wasn’t the absence of capability that mattered but the ability to align with what humans truly needed. Solutions were frequently built before the problem that could accommodate the technology. Thus, the main effects of some AI systems may have been feelings of confusion, unreliability, and lack of trust. They may even add to the complexity. This was because it was difficult for organizations to scale their artificial intelligence projects beyond pilots.
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Real Human Problems That AI Was Supposed to Solve
The need for AI solutions arose out of operational hurdles like dealing with information overflow, poorly managed customer service, supply chain management issues, fraudulent activities, and predictive maintenance. These hurdles and concerns led to lagging decisions, loss of funds, and reduced productivity in the retail, finance, manufacturing, and logistics sectors
Specifically, retailers wanted better inventory management in order to reduce the chances of overstocked or stocked-out situations. Manufacturers wished to forecast equipment failure before a failure occurred. The banking industry also hoped for better approaches toward revealing hidden fraud in very large transaction amounts. These were legitimate goals in theory.
Without intervention, these problems tend to compound rather than correct themselves.
Stakeholders and People Affected
The group most impacted by this technology was the employees, management, and the front-line team. The front-line team members took many long hours conducting manual jobs like inventory verification, transactions, and customer queries.The managers experienced disrupted workflows because of supply chain interruptions and unclear data. The key decision-makers would not get insights on time because data was spread across various systems. The customers would be impacted indirectly because of service delivery, delayed assistance, product availability, and missed fraud events.
Why Traditional Solutions Fell Short
In the past, before the advent of AI technology, organizations processed their information manually. This involved using spreadsheets and databases. The manual approach was unable to cope with the sheer speed with which information is being generated. In the retail and logistics domains, the absence of real-time visibility resulted in stock imbalances. In finance, the lack of pattern detection sophistication in rule-based fraud systems made them ineffective. The customer service teams relied on scripted answers, making them unscalable. This made the adoption of AI a necessity as a viable alternative
Conclusion
This particular case study is significant in the aspect that the reliance and benefit of artificial intelligence lie less within the technological and more within the relevance of the innovation to the human need. Though artificial intelligence is expected to benefit the organizational aspect through increased effectiveness and efficient decision-making, the difference between the two, along with the actual results, reveals the inefficiency of the technological innovation to handle the human challenge. To get from promise to reality, however, organizations have to look at people first, understanding their work flows, instilling trust through transparency, and augmenting human judgment rather than merely replacing it. As organizations consider the context, responsibility, and utility that go with AI, the latter starts to become more than just an innovation, transforming instead into something that actually helps people
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