AI projects: Adhering to a technology – whatever it is – has associated benefits and challenges. With AI, it would be no different.
In fact, due to its characteristics, it is a paradigmatic case in terms of potential benefits and challenges – both of considerable scope for all sectors. But what are the most common challenges in AI projects? In this article, we have gathered the main ones.
Organizational Culture Still Doesn’t See Value In Artificial Intelligence
The most common challenge in artificial intelligence projects is the lack of understanding of the technology, especially among professionals not connected to technical areas.
The problem is nearly twice as common in companies that do not yet have AI projects than in companies that have already started adoption. This indicates that the longer the organization adopts, its resistance to AI is more excellent.
Gartner refers to this point as fear of the unknown, which translates into a 42% rate of respondents to its survey who say they don’t fully understand the benefits of AI.
But the challenge isn’t just familiar to organizations that haven’t yet adopted AI. For early adopters, in contrast, this challenge, while much smaller, still exists. According to a McKinsey study, these organizations need an AI champion recognized and well engaged in the C-suite, who will sponsor the initiatives and, more than that, pull the alignment of the AI strategy with the organization’s strategy, which will ensure that she feels more comfortable taking risks associated with her decisions, understands how often her models are updated, etc.
Difficulty Identifying Appropriate Business Use Cases
Perhaps a result of or even a cause of the first challenge is the difficulty of identifying AI use cases within the business.
This challenge is quite common, both in organizations that have already passed the mark of their first project and in organizations that are still considering adopting the technology.
Having an AI vision and strategy that aligns with the organization’s plan and then a road map of linked AI initiatives will define the company’s ability to finance and generate performance and prevent the work from happening. Aim at impossible goals.
This does not mean that the business areas need to become technical, but that, to map use cases and understand what technology can do for the business, it will be necessary that, together with the technical areas, they target their segment, follow the major players in the market, form partnerships with companies with expertise in the field and learn about the possibilities of use.
Lack Of Qualified Professionals And Difficulty In Hiring Specialists
Successful AI projects bring together both technical knowledge and business understanding. However, finding these two different skills on the market will not be uncommon.
This challenge is felt, above all, by organizations that already have AI projects, which need to go to the market in search of professionals.
Skills gaps mainly involve professional machine learning modelers and data scientists. As for these, there is a lack that is difficult to fill, as professionals such as data scientists, in addition to technical and theoretical expertise, need practical expertise linked to the specific domain of the business. It is this knowledge that allows them, for example, to identify business-appropriate AI use cases. Another professional that the market misses is data engineering.
In addition to being few and new, data science degrees prioritize – with good reason – a varied curriculum. However, one of the significant challenges of the courses is the specificity of the areas, which require a particular mastery of data, models, and treatment. It is not uncommon, therefore, that most professionals learn in practice within the company.
Missing Data Or Data Quality Issue
This has to do with another of the most common difficulties in AI projects: data quality. Problems with this, such as lack or inconsistency, are widespread in early adopters of AI, even though the volumes of relevant data are essential to AI success.
This happens due to data collected in different organization activities, in other formats, and stored in various databases; lack of a unified repository from which data can be sanitized and accessed; unstructured, incomplete data, etc.
In this case, it is not uncommon for AI projects to tend to surface data quality issues that were previously hidden or latent. Organizations that have not yet started projects with AI do not experience this type of problem. But they need to do their homework before starting their projects.
This is because the problem is severe and can lead to errors and bias that produce distorted results and predictions.
Challenges In Technical Infrastructure
It sounds basic, but if the technical infrastructure cannot support the amount of data needed or the type of processing required, the project will struggle. Many organizations face this challenge, which is the most common in AI projects.
This lack of adequate infrastructure occurs because not all organizations can integrate AI into their processes, either because their infrastructure is old or unprepared.
Data and infrastructure teams must work together to arrive at a stable data architecture that supports needs but is agile enough to scale as projects mature.
Legal Or Compliance Concerns
Although regulations on the use and privacy of data are there, organizations remain in an environment of fear and doubt about what can and cannot be done and in what way.
Furthermore, the field of AI is one of those typical cases where regulation does not keep pace with the evolution of technology – and perhaps it could not. But this also raises questions, especially those that come in the wake of the risks of AI: what if, because of an AI’s defect, something or someone is harmed? Whose responsibility is it?
At this point, we can also mention the ethical debates raised mainly from the cases of systems biased toward minority groups.
Also Read: Artificial Intelligence In The Digital World