We’ve seen how COVID-19 has put pressure on businesses to accelerate their digital transformation journeys by months, and in some cases even years. The arrival of the pandemic has made them rethink the technologies at their fingertips—particularly artificial intelligence (AI)—and leverage them to increase productivity, solve supply chain issues, and seamlessly deliver products and services. Organizations have realized the need to integrate AI into their digital strategies, and this article will focus on solving common AI adoption challenges.
Artificial intelligence is a revolutionary technology that can save time, energy and money. It's no longer limited to science textbooks or science fiction fantasies; it has countless real-world applications. Businesses now acknowledge the importance of implementing this future technology. In fact, high-level penetration of machine intelligence can solve fundamental problems.
A McKinsey survey shows that artificial intelligence adoption is on the rise in 2021 and will continue to do so. It noted that "56% of respondents reported using artificial intelligence in at least one function, up from 50% in 2020." The adoption of artificial intelligence is the way forward, but it's not always easy. So what are the key barriers preventing companies from realizing the vast potential of this next-generation technology? Let’s discuss these AI adoption challenges one by one.
Ethical Considerations
The first challenge in adopting artificial intelligence is how ethics becomes a pressing issue as organizations integrate artificial intelligence with more processes. Artificial intelligence gives seemingly scientific credence to human biases and tends to amplify them, calling their decision-making potential into question. Fortunately, we have a solution.
One promising sign is the growing awareness of the problem, and acknowledging the potential for bias in AI is the first step. When enterprises train their AI/ML models, they must actively combat biased data and specifically program their AI to be unbiased. Additionally, annotators must carefully analyze the training data before feeding it into the algorithm. This way, it does not lead to biased conclusions.
Poor Data Quality
One of the most critical barriers to monetizing AI is the poor quality of data being used. Any AI application is only as smart as the information it has access to. Irrelevant or inaccurately labeled data sets can prevent applications from working properly.
Many organizations collect too much data. It can be riddled with inconsistencies and redundancies, leading to data decay. Data quality can be improved by streamlining the collection process. Stakeholders must pay more attention to data cleaning, labeling and warehousing. These workflow changes can provide businesses with high-quality data.
Data Governance
In the face of rising cybercrime, responsible data governance is more important than ever. There are concerns about how companies access and use their confidential information, so it's important that organizations leveraging customer-facing AI hold themselves accountable when deploying applications.
The key here is segmentation and visibility. Organizations must ensure they can monitor and limit how their AI algorithms use data at all stages. Segmentation mitigates the impact of a breach and keeps user information as secure as possible. Likewise, transparent data collection policies can help alleviate concerns related to AI.
Process Flaws
Companies often use internal tools and pipelines for AI deployment and monitoring. Building an efficient AI model from scratch requires a lot of time and money. So, if you’re just starting out, AI adoption may cost you dearly. Additionally, your tools may contain inappropriate algorithms and biased data. In this case, adopting third-party tools for AI integration or using market-proven tools is a wiser choice.
CYBERSECURITY
Artificial intelligence implementation introduces cybersecurity risks. Numerous data breaches have occurred in an effort to collect data for artificial intelligence initiatives. Therefore, protecting stored data from malware and hackers should be a company’s top priority. A strong cybersecurity defense approach can help prevent such attacks. Additionally, AI adoption leaders need to acknowledge the growing threat of sophisticated threats and shift from a reactive to a proactive strategy.
Storage Limitations
Training AI/ML models requires a constant number of high-quality labeled datasets. Therefore, organizations need to feed large amounts of data into machine learning algorithms so that they can perform the required activities and provide reliable results.
This has become challenging because traditional storage technologies are expensive and space-constrained. However, recent technological breakthroughs such as flash memory appear to offer a solution. Unlike expensive traditional hard drives, flash storage is more reliable and affordable.
Compliance
Artificial intelligence and other data-centric operations are receiving increasing attention from laws and regulations. Organizations must comply with these restrictions, especially if they operate in highly regulated industries such as finance and healthcare.
Taking a flexible approach to maintaining high privacy and governance standards can help these companies become more compliant. Third-party auditors are more likely to be in demand due to increased regulations.
The Road Forward
Artificial Intelligence is emerging as a game-changer, and its potential is worth exploring. A study by PricewaterhouseCoopers states that “AI could contribute up to $15.7 trillion to the global economy by 2030, more than the current output of China and India combined. Of this, $6.6 trillion could come from increased productivity, 9.1 Trillions of dollars could come from consumer side effects.”
But what can make AI work for companies? Anticipating barriers to AI adoption and taking a strategic approach to implementation can help organizations achieve transformational growth and maximize returns.
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