Top 10 challenges to AI adoption and ways to overcome it

Even though AI is growing in popularity, many organizations are still unsure how to use this “new” technology. By 2024, half of all AI investments will have been quantified and tied to particular key performance metrics to track ROI. The fact that their business culture does not realize the need for AI is one of the reasons why firms have not progressed in their AI adoption. Other factors are a lack of data and competent personnel and difficulty identifying relevant business cases to democratize AI. Below are the challenges that are a hurdle to AI adoption and how that can be overcome.

1. Lack of skills

The first hurdle is a lack of knowledge. AI will redefine the abilities required to do AI jobs, according to business and IT professionals. For instance, AI is now capable of evaluating X-rays in the same way that human radiologists do. As this technology progresses outside research settings, radiologists’ focus will change to collaborating with other physicians on diagnosis and therapy, treating diseases, performing image-guided medical interventions, and discussing procedures and outcomes with patients with augmented analytics tools.

2. Fear of Unknown 

People aren’t fully aware of AI’s advantages and applications in the job. For business and IT leaders, quantifying the advantages of AI projects is a considerable challenge. While specific benefits, such as increased revenue or saved time, have well-defined values, others, such as customer experience, are difficult to describe precisely or evaluate effectively for AI Democratization.

3. Data Quality from AI 

The whole data scope or data quality obtained from AI is the third challenge. A large number of data from which enterprises extract knowledge about the optimal response to a scenario is required for successful AI initiatives. Organizations are well aware that AI will fail because of insufficient data or if the circumstance faced differs from previous data. Others understand that the more complicated the situation, the more likely it will not match the AI’s previous data, resulting in AI failures.

4. Unclear goals 

Without a clear AI strategy and goals, firms frequently overlook the most important places to begin adopting AI, resulting in lost value and faith that investing in AI is the right decision. While the push for automation and AI is frequently a grassroots effort, the enthusiasm may be misdirected without leadership guidance. When executives pay attention to data and analytics teams, they open up communication channels and learn where AI may add real value. However, it is the job of leadership to ensure that AI adoption is in line with the organization’s overall business goals. A more comprehensive vision will arise from ensuring that AI strategy has roots from the top-down and bottom-up.

5. Silos and Segmentation 

When data has an end-to-end path from collection to analysis, insights, and feedback loops, AI adds the most value. Organizational silos obstruct these routes and make it difficult to respond quickly to the information AI provides. Instead of focusing on ways to assist teams in collaborating to operationalize data projects, focus on ways to help them collaborate. The utility and accuracy of AI projects can be improved by building on these foundations using augmented analytics software.

6. Data Labeling

Most of our data were organized or textual just a few years ago. With the Internet of Things (IoT), photos and videos make up a significant portion of the data. There’s nothing wrong with it, and it may appear that there isn’t a problem here, but many machine learning and deep learning systems are taught in a supervised manner, requiring the data to be labeled. The fact that we generate massive volumes of data daily doesn’t help; we’ve reached a point where there aren’t enough people to identify all of the generated data.

7. Explainability

Many “black box” models generate a result, such as a prediction, but do not explain. If the system’s conclusion agrees with what you already know and think is true, you’re unlikely to question it. But what happens if you disagree? You’re curious about how the decision was made. In many cases, simply making a decision is insufficient. For instance, doctors cannot rely solely on the system’s suggestions regarding their patients’ health. It’s much easier to figure out how much we can trust the model when we understand decisions.

8. Bias and Case-Specific decisions

Bias can be caused by a variety of variables, beginning with the method of data collection. If the information is gathered by a survey published in a magazine, we must keep in mind that the responses (data) are confined to those who read the magazine, which is a small social group. We can’t say that the dataset is representative of the entire population in this circumstance. Our intelligence helps us to apply what we’ve learned in one sector to another. Humans can transfer learning from one context to another, similar situation, which is known as transfer of learning. Artificial intelligence is still having trouble transferring its knowledge from one set of conditions to the next.

9. Poor business alignment

Top challenges to AI deployment include a company culture that does not recognize the need for AI and difficulties establishing commercial use cases. To identify AI business cases, managers must have a thorough awareness of AI technology, including its capabilities and limits. A lack of AI expertise may hamper many firms. However, there is another issue here. Some businesses jump on the AI bandwagon with overconfidence and no clear strategy. AI installation necessitates a strategic approach, which includes goals, KPIs, and ROI tracking.

10. Integration challenges

Integrating AI into your existing systems is a more involved process than installing a browser plugin. It’s time to build up the interface and aspects that will address your company’s needs. Some regulations are hard-coded. We must think about data infrastructure, data storage, labeling, and data feeding into the system. Then there’s model training and assessing the usefulness of the produced AI and developing a feedback loop to improve models based on people’s activities continuously and data sampling to limit the quantity of data saved while still generating correct results.

Is your organization facing the challenges of AI adoption? If yes, what are the challenges, and what are you doing to overcome these challenges? Please let us know your thoughts in the comments section.

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