AI is a new buzzword in the corporate environment, and although 98% of companies are aiming to become data-driven, less than one third succeeded in 2018.
There are multiple reasons behind this inability to adopt AI and data-driven approaches. For most organizations, there are significant obstacles to using AI daily, which have to do with strategy, data organization, storage, and even managers’ egos.
The scarcity of data mining specialists is an example of a significant obstacles, and an industry-specific shortcomings. Here is a list of the most common barriers to overcome on your way to becoming a data-driven business.
Lack of Strategic Thinking
AI is just another tool which can help to solve age-old business problems. Companies need to identify their issues first, think about their priorities, and decide what to focus on first. AI works best by detecting patterns in heaps of data, which would be otherwise a considerable struggle to go through manually.
Also, it should be remembered that there is no AI without data. Even if you have defined a problem that could be solved by applying machine learning, it is not going to work out if you don’t have the raw material to train the system.
The Black Box Effect
Since AI is not deterministic and works through hidden neural layers which go through a hardly traceable decision-making process, AI-based systems can be biased and hard to correct. This lack of transparency is not compatible with most corporate policies.
Because AI learns from the examples it gets, it can be heavily influenced. The harshest case is that of Tay, the Microsoft chatbot that turned homophobic and used Nazi references in less than a day. Until AI has simple mechanisms to be fine-tuned so that defects can be eliminated on the spot, AI will not remain an easily accessible and transparent option.
The outcome of this black box effect is that, in fact, AI implies blind trust in its output. An AI system is only as good as the information used to train it. If the data has not been updated or goes inaccurate over time, the AI will not perform efficiently. For example, imagine a chatbot that was trained using data from senior managers’ conversations in a large corporation, and then made to answer inquiries from teenagers. Even if the system worked perfectly in the initial setup, it could fail miserably even due to a slight deviation in a user’s language.
Data Management Systems
As data is the central point of any AI system, any problems with managing, sharing, or accessing data can hurdle the adoption of such tools. Some companies are used to keep separate storage facilities for historical data such as sales records, call center logs and more as opposed to real-time data which come in through web portals. With such different silos, though, using AI systems might be counter-efficient due to the lack of a single picture.
This obstacle can be addressed by setting up a unified data lake to be processed by a big data management technology. For companies looking to incorporate AI into their DNA, data silos need to be eliminated and replaced by more flexible storage methods which are also centralized.
In any organization, data security is a significant concern, and usually IT departments take extra care to prevent any breaches. Until now, most companies have heavily invested in state-of-the-art protection systems and have set many user permissions. Although this help to keep data integrity, they also pose a significant challenge to timely data accessing and processing. Project managers need to mediate between users and the IT department to find the best combination of restrictions to allow decision-makers the opportunity to look at data.
To ensure that the machine learning algorithm works appropriately, it needs to have properly labeled data. Most of the times this is a tedious manual process, especially if it is a sector with specific terminology. Once the data is labelled and fed into the AI system, the learning can be automated where the system only improves with time.
As the demand for AI specialists explodes, it becomes clear that there is a definite shortage of such experts. Furthermore, few academic programs prepare them; most graduates in the related fields are engineers, statisticians, or mathematicians. Most data scientists are self-trained, yet it takes years of training and experience to accumulate the necessary know-how. Nevertheless, as more companies want to jump on the AI bandwagon, there will likely be more AI software development specialists to meet the market demand.
AI adoption might also be hindered by the initial setup expenses. These costs include the discovery phase to identify the problems to solve and the data to be used, building data storages, recruiting data analysis experts, and on-boarding other staff members. All these requirements may overcomplicate the process of adopting AI and leave companies thinking that it is better to stick to their legacy systems instead.
These are just a few of the obstacles which need to be considered when discussing the possibility of adopting AI as a working tool. If you don’t find proper solutions to these, there may be risks of running behind the schedule, incurring additional costs or even abandoning the project altogether.