Potentially, the absolute majority of businesses can integrate AI into their operations. Despite the enthusiasm, though, many still fail to receive sufficient ROI from their AI implementations.
Most commonly, the problem isn’t in AI itself but the strategy behind its integration. For example, even the most outstanding AI model won’t ever realize its full potential if it can’t properly communicate with existing systems. Now, let’s define the most common reasons that make AI projects fail.
Some companies struggle to fathom the idea of AI implementation being a never-ending project that often requires all parts of their organizations to change and adapt. This includes rethinking the team composition and making sure all tracks of AI development are interconnected and in sync.
This calls for thoroughly modified methods for building reliable AI. The two most important questions that need to be answered at this stage are: ‘What type of talent do we need?’ and ‘How will they communicate with each other?’
You’ll need to cover all the areas of the AI lifecycle, from AI design to deployment to monitoring, with the right talent. The employees will range from very narrowly-specialized data scientists to AI ethicists, who all need to work closely with each other in order to achieve success.
You’ve probably heard it: data is one of the most important factors of AI success. Feed AI with unqualified data and it will come up with inaccurate decisions. Here at Iflexion we consider data cleaning as a top priority on any AI development project.
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Companies often assume that data cleaning implies that their historical data needs to be thoroughly checked and reorganized yet only once. Given that in most cases AI needs both historical and real-time data to make accurate decisions, companies need to completely reimagine their data governance to make data preprocessing continuous.
As organizations have a multitude of data sources, creating a framework that could properly clean, sort, and ingest every necessary bit of information is overwhelming. However, in most cases, not all incoming data is useful. This is why it’s critical to find out exactly what type of data you need in order to achieve your specific AI goal, and then collect, clean, and process only relevant datasets.
Executives often mention the unexpected need for additional investment in the midst of AI development as a significant roadblock. Conveniently, establishing smart data governance frameworks ensures that no resources will be wasted on cleaning datasets that will never be used.
While today we have an avalanche of specialized tools and well-defined approaches to data governance as a whole, companies still struggle to overcome data bias. The consequences of biased AI algorithms include significant economic losses and damaged brand reputation.
Even with perfectly balanced datasets that take into account all possible parameters, AI algorithms can still find unwanted links between those attributes and make biased decisions. Currently, there is no other way of dealing with this problem rather than reassessing training data continuously, with regular human evaluation of the decisions taken by AI.
In this sense, treat this technology like a child: it grows, learns, and makes decisions on its own, but when you stop paying attention to its development, it goes astray easily.
Flexibility and Scalability
At this point, it should be clear that AI is an all-permeating system that requires continuous improvement and monitoring. In a nutshell, any new variable in a company’s business environment can significantly lower the reliability of AI decision-making, while this company’s production environment might simply be not agile enough to reconfigure the AI system accordingly and in time to avoid poor outputs.
Predicting these changes is never easy, but many of these scenarios can be modeled. When companies start simulating the behavior of their AI system under certain conditions, many scalability issues become apparent. For example, a sudden bump in computing demand can significantly hinder the decision-making accuracy of your AI model. Again, considering the pervasive impact of AI on companies’ infrastructure, it’s often other IT systems that are struggling to scale, rather than an AI model itself.
At this point, the opportunities offered by AI are too significant to ignore. However, rushing into adopting AI rarely brings positive results.
Just finding an experienced technology partner and developing an outstanding proof of concept might be solid first steps for conventional software development projects, but not those dealing with AI. Too many companies are getting caught in the loop of creating perfect AI algorithms, but few pay enough attention to their successful integration into business operations.