Five Common Obstacles in AI Adoption

3 min read


Artificial intelligence (AI) is making inroads into growing number of industries at every size and scale. Enterprise are reaping the benefits of AI implementation, whether moderately or significantly, in various business functions. International Data Corporation (IDC) predicts that the global spending on AI is going to double from $50.1 billion in 2020 to $110 billion in 2024.

In a 2020 survey conducted by an analyst firm Cognilytica, 40% of respondents intended to implement AI in one or more identified patterns, and almost 90% indicated to have some sort of in-progress AI implementation within the next two years.

Although businesses are increasingly realizing its irrefutable potential, many organizations are still holding back from AI adoption, owing to their share of uncertainty around its business impact and advantages.

Let’s look at the five common barriers that companies face while considering AI implementation.

Lack of clear vision

Many a time, tempted by the vast capabilities of AI and following the trails of big corporates, business owners join the AI bandwagon, and they do so without a strategy in place.

A successful AI implementation requires a clear vision of the problem it is supposed to solve, the goals it’s expected to achieve and a roadmap to lead there. Many AI projects start without a proper goal, failing to derive the desired value and a weaker trust in AI capabilities. Without knowing what to want from the implementation, even all the right data and talent cannot help.

Business leaders should have clarity on AI use cases and well-defined success metrics. This will give them insights into the potential of AI implementation and also what resources the project will require.

Unrealistic Expectations from AI implementation

The internet is rife with the hype around AI. Over the years, there have been countless case studies of AI’s phenomenal success, which has made the hype even stronger. Several AI projects fall victim to hyper-optimism, leading to unrealistic expectations and, thus disappointment.

While AI opens countless possibilities, it’s critical to understand that it has certain limitations. It cannot achieve the desired success overnight and only get better over time with more data and continuous improvements. Decision-makers should realize that it’s not error-free and have a risk plan to be prepared for the consequences of errors.

What a business needs AI for is more important than what AI can do for it. Every business is different and so are its AI needs and risk appetite. To prevent excessive optimism, companies can consult an external specialist with a firm grip on AI advancements and experience in implementations in varied environments.

Talent gap

AI-enabled transformation is a considerable undertaking and it requires a team of skilled developers, researchers, engineers and data scientists to drive it. The right talent is required not only for the new implementations but also for ensuring their continuity and for new AI projects.

While AI promises novel opportunities, various studies suggest that most companies do not have the necessary skills in-house to leverage them. As AI adoption rises, it’s leaving an even wider gap of AI skills. According to a Deloitte article published last year, the demand for AI talent continued to be high despite the disruptions and payoffs caused by the COVID-19 pandemic.

According to Deloitte’s State of AI in the Enterprise, 2nd Edition survey, even most early AI adopters face an AI skills gap and as many as 68% of executives surveyed report a moderate-to-extreme skills gap and 27% rate their skills gap as “major” or “extreme.”

To address this, enterprises should expand and diversify their AI talent by hiring freshers and experienced professionals and exploring how they can leverage their vendors. They should invent upskilling their in-house talent and build an AI team with a long-term vision.

Lack of Relevant and Usable Data

Data is the fuel of AI engines. The dearth of high-quality training data is one of the potential hindrances in AI adoption. There are four major challenges involved.

The first is availability. While many enterprises have massive data volume, it’s not consumption-ready. Most of it is scattered across various business units. Identifying these data siloes and consolidating data from them is often a big hurdle.

Secondly, data in these siloes is in diverse format, such as text, image, speech, videos and maps. Refining it for use is not only time and resource-intensive but also requires specialized skills.

Another challenge that follows is that the raw data is often unstructured without proper tagging and labeling. Machines learn the same way as humans do. However, they need a lot of labelled data to detect and learn patterns.

Finally, depending on the purpose of data collection and social groups involved, the data is prone to human judgments and biases. When AI algorithms are trained using such datasets, the results are likely to be skewed or incorrect, impacting the success of the AI project.

Therefore, enterprises looking to implement AI must have a data strategy in place. There should be a single repository of data to train AI and ML models. Models should be continuously fed the latest, accurate and labelled data. Additionally, proper modeling techniques and a common understanding of ethical considerations can address this challenge.

Integration with Legacy Architecture

For many organizations, especially small-scale companies, infrastructure compatibility is a vital concern about their new AI initiatives. It can be excessively challenging to integrate new AI systems into their legacy enterprise architecture, which they may have designed over a long time with significant investment. The integration can prove complicated, requiring radical changes in the legacy systems, which is costly in terms of time and capital.

Although the transformation can be instrumental in driving efficiency-led growth, it can be too big an undertaking. Furthermore, some advanced AI systems may require replacing the legacy infrastructure with new hardware, which can multiply the cost involved.

Enterprises facing this challenge can consider leveraging plug-and-play solutions, where they do not need to invest in new technologies since vendors or service providers take care of the infrastructure. Cloud-based applications can be a big advantage since they eliminate the need for physical infrastructure and in-house support.

While these are the most common barriers that have slowed down the adoption of AI across the world, the opportunities that it promises outweigh these challenges. It facilitates information accessibility, boosts efficiency, improves customer experience and promises scalability by making the best use of organization data. Sooner or later, AI is going to be an integral part of the strategy for every business. The key to success lies in a well-defined roadmap and establishing a proof of concept while taking small steps.

Raviteja Sidda Raviteja is a Senior Product Marketing Executive at VOZIQ. VOZIQ is the only cloud-based predictive retention solution that leverages sophisticated AI that is powered by 10+ targeted machine learning models to enable recurring revenue businesses to predict customers who are at-risk of cancellation and prevent the cancellation by driving large-scale actions through contact center customer care, marketing and field channels. VOZIQ’s solution significantly cuts time-to-value in industries such as home security and automation, pest control, home warranty, energy providers, telecom, and insurance.

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