Challenges and Solutions in Implementing AI Automation
AI is undoubtedly among the breakthrough innovations of the 21st century! It’s everywhere and progressing at an unimaginable pace in terms of acceptance, numbers, and trust. From managing the highly sophisticated manufacturing ecosystem to streamlining education, there’s hardly a domain that hasn’t embraced AI automation.
However, as with every technology, businesses may confront a range of challenges while implementing AI. While most are technical, a few are commercial and behavioral. So, let’s look at some common challenges and solutions in implementing AI automation.
10 Common AI Automation Implementation Challenges and Solutions
Implementing AI automation is a strategically advantageous move. However, businesses may confront diverse challenges impeding the process. Here are ten.
1. Data Availability and Quality
Amongst the most significant challenges encountered while dealing with AI implementation is the availability of data and its quality. AI systems are hugely dependent on high-quality data to learn and make accurate projections. However, often, businesses do not have the adequate amount and the necessary quality of data that will help the AI system deliver useful outcomes. Even if businesses use such data, the results are likely to be half-baked and practically useless, putting all the resources down the drain!
However, data availability and quality aren’t challenges businesses cannot deal with. They can overcome it by investing in data quality and management. Hiring professionals who help them ensure data accuracy, completeness, and relevance can make a considerable difference. They can accomplish these goals through data cleansing, normalization, and enrichment. Alongside this, investing in external sources to supplement the internal data can contribute to overcoming data-related challenges.
2. Integration with the Current Systems
Organizations implement AI automation to enhance the functionality of the existing systems. However, it is these systems themselves that hinder the AI implementation process. Moving to AI is a crucial technical breakthrough for every business. Hence, it goes beyond adding plugins or integrating a new functionality into the system.
Accordingly, a comprehensive assessment of the infrastructure, including factors like data storage and data input is imperative. Additionally, businesses should consider the compatibility of the existing systems to facilitate seamless operations.
Doing all of it on your own may prove challenging. Hence, businesses should partner with AI experts with the necessary experience in implementing AI systems for businesses of varying IT infrastructures. Furthermore, companies must invest in training employees on the newly induced AI systems to achieve the desired levels of success.
Of course, all of it calls for a considerable investment. But looking at the advantages of implementing AI automation in the long run, the investment is worth it.
3. Redundant Infrastructure
Businesses expect a sizable value from AI automation after implementing it. But for AI to deliver it, the technology should be able to process massive datasets in the blink of an eye. Organizations can achieve such efficiencies only when they have a competent infrastructure with the required processing capabilities.
Nevertheless, the problem with many companies is that they are still using redundant infrastructure. Legacy or outdated systems are comfortable to work with. But if a company wants to implement AI automation, it cannot stick to age-old infrastructure for very long. A change in the approach and upgrades to the existing infrastructure can help drive the change and overcome challenges for AI automation implementation.
4. AI Bias
AI systems and their accuracy depend on the data on which businesses train them. Accordingly, having superior-quality data on hand is necessary from the viewpoint of delivering the desired value. The absence of such data can lead to biasedor prejudiced results that could harm the company’s course of action.
Accordingly, organizations must work toward eliminating such biases by ensuring that the data is unbiased and of good quality. Another way of eradicating biases is developing straightforward algorithms that can be easily read and understood. Additionally, developing frameworks driving increased transparency and quicker identification bias in AI algorithms is equally important to achieve valuable results.
5. Ever-Evolving Nature of AI
AI continues to evolve at an unmatched pace. While that’s beneficial, it could prove challenging in AI implementation. Building the computing power required to process gigantic datasets for building AI systems proves a significant challenge for startups and SMBs with weaker financial muscle. Given the situation, partnering with an AI consulting company help. Such companies help their clients navigate AI’s vast and ever-growing landscape and do what’s necessary to keep up with AI’s pace of evolution.
6. Implementation Cost
Implementing AI automation is a strategic decision. But it comes with a significant cost attached to it. Companies, especially the smaller ones, should factor in before they proceed. Businesses should plan to implement AI carefully. They should carefully assess the returns before investing in AI with the help of an AI expert. The high costs may appear discouraging to many. Nonetheless, businesses should consider benefits like informed decision-making, cost-savings, increased efficiency, strategic edge, etc., and look at the larger picture during AI implementation.
7. Legal Dimension
AI is popular but it isn’t devoid of legal concerns. The data algorithms gathered from users are sensitive. Errors in the algorithms and data governance systems in AI applications can lead to potentially incorrect outcomes and loss-making decisions. Not to discount the violation of laws that can land a company in legal trouble. Although it is one of the most critical challenges for AI automation implementation, companies can deal with them through AI consultants who know about these aspects of AI.
8. A Dearth of AI Expertise
AI has been around for a while. Yet, it is a new and constantly evolving technology. For many businesses, AI’s dynamic nature makes finding the best-suited talent for their AI automation implementation endeavors. Here, companies can adopt two approaches – either outsource AI or build in-house strength. Outsourcing incurs costs but it is a quick solution. On the other hand, developing an in-house team requires time and effort. Companies should decide based on their short- and long-term goals. A blend of both could be another approach companies can adopt to deal with the scarcity of AI talent.
9. Explainability
What does explainability refer to? At the outset, we must factor in the fact that human beings trust things they easily understand. One of AI’s critical implementation challenges includes the unfathomable nature of the way deep learning models and inputs can predict an outcome and devise a solution. Explainability matters while ensuring transparency in AI-driven decisions and the algorithms that drive them. It refers to evaluating AI’s impact on decision-making, conducting periodic audits, and ensuring continuous training to simplify things and strengthen AI’s position in the organization.
10. Employee Resistance
As humans, we tend to resist changes. Implementation of AI automation isn’t an exception to it. In fact, with AI, the resistance may be even stronger, given the job insecurities the technology brings with it. Besides, the older the system gets, the more comfortable its users get with it. Thus, employees may have numerous reasons to oppose AI.
Reducing resistance to AI automation requires companies to develop an agile culture that accepts changes. It can involve focusing on the tangible benefits of AI from the viewpoint of company and career growth. It is impractical to imagine an organization with zero resistance. However, one can surely work towards reducing the friction while implementing new technologies, especially those like AI with a far-reaching impact.
The above are generic challenges for AI automation implementation. In addition to these, companies may have specific challenges. Dealing with both and formulating solutions in implementing AI automation would require companies to partner with AI consulting and implementation experts. FidelTech can serve as one.
With extensive expertise in AI and experience implementing AI automation across Japan and other parts of the world, we help businesses address common and unique challenges about AI automation implementation. Please email us at infofidel@fideltech.com to explore more about our AI implementation solutions.
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