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Get Past These Roadblocks to AI Implementation Featured

Get Past These Roadblocks to AI Implementation "Construction site barricades"

There is a major disconnect between what people would want to do with AI and their potential to actually do it. With the rising number of companies now adopting artificial intelligence in their operations or planning to invest in it to enhance their products, operations, and services, this technology must be understood with regard to its potential and challenges. Get past these roadblocks to AI implementation and succeed in your implementation plan.

  1. Data issues

This is one of the leading roadblocks in the journey to implementing AI. It is largely caused by a lack of relevant and insightful data free from bias and does not compromise privacy. However, the availability of massive volumes of data does not guarantee the availability of useful data. A McKinsey survey identified that out of 100 companies that have piloted AI, 24 of them stated that the biggest impediment to proper AI implementation is the unavailability of useful and relevant data. While organizations continuously collect data that should be sufficient when fed into AI systems, most of it goes to waste. From this, it is evident that collecting data is not the end of the process. Rather, there is a need to collect useful data.

  1. High cost of tools and development

When planning to develop and implement AI systems in an organization from scratch, the cost of acquiring human resources, technology and tools can be high. This cost is high mostly when a company is starting and has no staff with the right technical skills or tools. For organizations with small budgets and lack of experience that cause the development of AI to be outsourced, the challenge is similar. However, an alternative to this will be to leverage tools developed to work with systems that companies in a given industry are already using.

Lack of a clear leadership

When it comes to big organizations investing in AI implementation, leadership is a critical component without which things will not work as expected. However, larger organizations do not have a problem when it comes to leadership. In fact, these organizations often have many leaders with diverse opinions. While opinions are good for progress, too much of it can turn into a problem, even with a lack of leadership. While most leaders in organizations think about investing in emerging AI technologies, they often fail in their duties because of a lack of the same vision across the organization. For example, the IT department might have enthusiasm about the implementation of AI and how it will turn the organization's fortunes. On the other hand, the other department might be too slow to cope with this pace of change. This unevenness might result in poor adoption of technology, which can increase costs over time and may lead to conflicts in the overall AI adoption strategy.

Shortage of skills

Even after the organization decides to implement AI, most of them lack adequate skilled personnel to develop AI systems. A survey by Deloitte indicates that 31% of companies see the skill gap as a key challenge in the adoption of AI. The key challenge here is that most companies cannot conduct the adoption on their own due to a lack of required skills. There is also a mismatch of skills because some skills are more available than others. It is important to note that the implementation of AI requires a broad set of skills. This problem can be addressed by outsourcing the development of AI systems to other organizations that already have existing tools. This will enable the organization to fix the labour problems that might hinder the implementation of AI.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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