There is a range of reasons for the failures of AI projects. It includes a lack of emphasis within an enterprise on cultural change and preparation to adapt to new working practices. But inadequate data is the most significant factor. This involves everything from insufficient information architecture and exploration to modeling, performance, and governance. If you use the analogy that the “icing on the cake” is artificial intelligence, then the cake itself is data. There are many reasons why AI projects fail, in this article, we will discuss about the same.
Managing and communicating expectations to leadership is a critical challenge for AI projects in the process. It is primarily related to the time and expense it will take to implement the technology. AI projects are not linear, so it is necessary to convey to managers that testing is required for the process. They will iterate and refine the process along the way, as development teams train machine learning models and collect new insights.
AI projects have shown that their mere existence reveals innovation and a digital transformation at the cutting edge of technology. This would not be the case with increased financial and operational scrutiny. So, CIOs/CDOs would either have to “get it right” or reduce the possibility of financing AI ventures.
Below are a few reasons why AI projects fail
1. Data Architecture
In every company right now, there is no more important job than a data architect. And the best architects would realize that unlocking value from data is the final performance of their role. Data Architects understand the strategy and business challenges of the company to be solved. But have the technological insight to get the data itself dirty in their hands. They know what information is required and how different systems can incorporate the information. They then set out straightforward instructions from governance to protection for all. This purpose is not the same as IT architecture. The companies sometimes make the mistake of leaving this to the CTO or a conventional IT transformation provider. Rather than having the requisite specialist support to achieve success. This is one of the critical reasons why AI projects fail.
2. Data Access
There are various data sources to feed the system once the architecture is sound from a business perspective. And these will require careful management. The greater the wealth of data from different sources, the better the performance. But an enterprise becomes overwhelmed with messy, unreliable, or incomplete data of different kinds and quality without management.
These data sources include enterprise data silos open sources data like social media, government data, or sensor data from the IoT. However, the real obstacle is that these are all separate data silos for data scientists to extract, convert, and load. It is not possible to transfer it into one convenient data warehouse. The growth of Data Catalogs shows that CIOs/CDOs in any enterprise are increasingly becoming “must-have” to address the issue of multiple data silos. It has more advanced data catalogs providing a discovery feature to find data that they didn’t even know existed.
3. Data Modeling
The modeling of data is also seen as boring and therefore ignored. But this is a crucial practice if your company wants to gain value from its data. Why wouldn’t this be the case where information in every company is now such a valuable asset? Time spent in data modeling ensures continuity in the organization of structure, terminology, and standards. Even the transitioning process from a conceptual model to a physical data model allows all data stakeholders to collaborate and agree.
The method of data modeling puts together business processes and is compatible with data and IT communities. It will recognize critical components and relationships between different data sources and business workflows. It will save the company time and cost, as well as boost performance. Simply put, it will encourage everyone to understand how data can in use within the organization. More importantly, how the information is translated into information that provides an end-user with insight. Data modeling should be in place to avoid the reasons why AI projects fail.
4. Data Quality
Like any asset, its worth depends on how reliable it is for the organization. The same applies to information; companies frequently do not or do not want to quantify the cost to their company. It can be directly related to data of low quality or incomplete data. Data quality is the benchmark for the precision, timeliness, and completeness of the information of an entity and compliance with business laws. Data itself cannot be relied on for analytics or AI applications without reasonable data quality. As the amount of data increases, this becomes much more important, as are the types of data from various data sources. So this is an ongoing process that needs to be proactively handled and embraced by the whole enterprise by business rules.
5. Data Governance
Data governance consists of the rules, implementation, and data management of an entity, concentrating on the above four elements. This means that once done, and the above four areas are not one-off tasks that fall by the wayside. For it to be useful, data has only a particular lifetime. The change in culture through the training needed to complement AI projects is followed by good data governance. Data governance must be a proactive practice of the entire enterprise to ensure data access, accuracy, protection, and management. It is required to deliver information to the organization at the right time and good quality, to translate it into information. And so, to provide information-driven insight into what business choices can be in development.
A strategy that discusses how an organization approaches the above five data management areas is a good start. It is necessary to prove that an organization thinks of its data as an asset and should know why AI projects fail. Projects of artificial intelligence will then be even more effective in releasing the data’s value.
