Do you think AI Projects Fail? Because I do? [REASONING IS HERE]
Introduction
There is no surprise that AI and ML have become the key ingredients of modern technology and cyberspace. From wearables to robotics, AI is almost everywhere and in every sector. Most companies extend their hands to AI vendors to adopt AI into their workflow. They spent lots of time, money, and effort to ensure a successful project. However, Gartner estimated that more than 85 percent of AI projects fail and render errors. Another report says that around 70 percent of companies say that implementing AI has minimal or zero impact on overall workflow efficiency. This article will analyze the common reasons why AI projects fail and how to overcome them.
Reason for failure behind all AI projects –
Many of AI projects fail because integrating AI into a legacy system often becomes difficult. For making this merger possible, the AI system has to be of top-notch quality and work. Most enterprise constantly thinks about how productive the AI project will become. But the organization never asks how well they will respond while merging with the existing system. Let us discuss some other miss beliefs that ultimately drag an AI-based project to failure.
· Preparing for a dependable system: AI-based projects face a lot of difficulties when it encounters production circumstances. During its development and learning stage, many situations arise where the project stops working or does not cater to desired results. For example, the AI project will stop working when it encounters missing or irregular data. That impacts the speed also. Such a situation ultimately reduces the performance of the AI project by a substantial margin and thus the entire business. So, the AI team should create a dependable system to avoid bottlenecks in processing covering latency issues. Also, the team should keep in mind the contingency plans in case the project crashes.
· Not enough expertise: One primary reason why AI projects fail is that the technology is still in its infancy. A lot of engineers & a large audience do not find it relevant or comprehensible. AI projects require heavy datasets, instruction models, data structure, security algorithms, and a vast set of other domains. Aligning an AI project as per business requirements needs a tremendous team effort. If one of the project members fails to understand the goals behind the project or lacks expertise in his/her domain, the entire project might fail.
· Every big is not big enough: Yes, we are talking about Big data and its need in AI. The scarcity of artificial dataset generation often reduces the potential of AI. Healthcare and accidental sectors often lack proper (artificial) datasets. As we all know, the prediction becomes better with larger datasets. But there is the hindrance of large datasets. With the increase in the size of data sets, challenges arise; the AI systems need time to render these large-sized data. Again merging data from multiple sources does not sync every time. That becomes challenging for the AI system to digest and comprehend the objective behind feeding multi-tenant data. So, it all depends on the type of business and providing an adequate set of data to the ML model. Thus, organizations should hire data scientists & experienced AI advisors to discuss the expectations and current state datasets carefully.
· Security breach and cyber risks: Another major problem that arises while extending business workflow through AI-driven systems is security. Scaling AI for production can cause security entanglements. Businesses do not take security issues seriously while deploying AI projects. AI introduces a new dimension of vulnerabilities and threats. Any form of mishandling or malfunctioning of AI can leak data or pose threats to cyber risks like data leakage, system failure, or security breach.
· Lack of Collaboration: Lack of collaboration among different individuals of the development members or its internal teams can be the reason for AI project failure. All the various project members like data scientists, data analysts, data engineers, DevOps, BI specialists, and programmers should reside on the same page. For this, the product manager should responsibly take control of the project and make sure that everyone aligns with the project goals.
· Data governance and maintaining standards: Many business leaders and corporate executives pay close attention to user privacy and data security amidst the unprecedented possibilities of AI. Even customers and users are also aware of data privacy these days. Even though data is the blood and soul ingredient of AI systems, organizations often leverage customer data without following normative data handling standards. Not maintaining adequate data standards, compliances, and transparency in the algorithm’s workflow can ruin the AI project in the long run. That turns out to be another notable reason why AI projects fail so frequently.
· Unexpected behaviour: AI systems often exhibit unusual behaviour during actual implementation or collaboration with the existing business system. An AI project that runs well during the testing phase might malfunction when scaling the product to a tangible workflow. That is why project managers in collaboration with data scientists should closely analyze the bottleneck and inclinations of the system. Organizations can also leverage machine learning algorithms to improve themselves over time and derive the accuracy of the AI algorithm.
· Unclear business objectives: AI systems are too robust and can cater to large and powerful implementations. So, leveraging the power of AI without having a clear business strategy will not make the AI project a success. Rather than initiating from an uncertain business problem, enterprises should determine the business problem first and then decide if the AI project can help solve the problem.
List of some well-known AI project failures –
· One well-known AI project failure is IBM’s cooperation with The University of Texas (M.D. Anderson Cancer Center) in developing IBM Watson for Oncology. The project was unsafe and was performing incorrect cancer treatment.
· Another AI project failure was by the Canadian tech startup Element AI. It faced challenges in preparing its products for the market due to unusual operational costs.
Conclusion –
Companies planning to develop their AI-driven solutions can outsource their work to those companies that know the actual essence of where AI projects fail. They comprehend the client’s specifications and compile a system that works smoothly with the current business scenario.
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