New Study Identifies Major Roadblocks to AI Adoption for Businesses — Are You Aware of Them?

A study done by automation vendor Hyperscience and the Harris Poll found that four out of five organizations are currently using AI, and almost all plan to increase usage in areas such as data analysis, cybersecurity, and predictive analysis. However, it also found significant AI adoption challenges.

Lack of Use of Proprietary Data

The success of ChatGPT and other generative AI (GenAI) engines has led many to neglect the most obvious usage of AI in the enterprise: gleaning further insight from existing data. The study Unlocking GenAI: Navigating the Path from Promise to ROI found that three out of five organizations are doing a poor job of leveraging their existing data estates. This particularly applies to proprietary data.

For example, most organizations use large language models (LLMs) like those owned by OpenAI, Google, Microsoft, and others. Relatively few have developed their own small language models (SLMs), yet the report found that three out of four organizations using GenAI have noticed that SLMs outperform LLMs in speed, cost, ROI, and accuracy. SLMs can be finely tuned to organizational needs, enabling them to understand context, jargon, and nuances relevant to particular industries. This specialization leads to faster processing times and improved outcomes, allowing businesses to automate repetitive tasks such as data extraction, categorization, and summarization.

“Data is the lifeblood of any AI initiative, and the success of these projects hinges on the quality of the data that feeds the models,” said Hyperscience CEO Andrew Joiner. “Three out of five decision-makers report their lack of understanding of their own data inhibits their ability to utilize GenAI to its maximum potential.”

Many organizations store a wide range of documents, including PDFs, blogs, customer files, database entries, web forms, application forms, orders, invoices, and more. This seems like an area ripe for AI, yet half of organizations don’t use GenAI to help with document processing or streamlining workflows.

This is one of the most straightforward implementations of AI in the enterprise, as the data is right there and is typically contained within the firewall. “GenAI can transform document processing and enhance operational efficiency,” Joiner said.

Other Barriers to Adoption

Whether LLMs or SLMs are used, the key is how the model is trained. This remains a big AI adoption roadblock. Around 77 percent of survey respondents admitted to underusing available data for training AI. As a result, model accuracy suffers, hallucinations become more frequent, and trust in AI diminishes.

Data privacy can also inhibit GenAI adoption. The Harris Poll noted that 83 percent of organizations express ethical and data privacy concerns about AI. Interestingly, according to the survey, small businesses are more likely to recognize and act on such concerns than large enterprises.

Integration is a major barrier to realizing AI’s potential benefits. It is one thing to ask a model for answers. It is quite another to integrate the business’s data into the models and tie the answers and responses into other business applications and workflows.

Overcoming AI Adoption Challenges

There is no simple solution to overcome the many barriers to AI adoption. Many traditional vendors and AI companies offer a wide range of ways to capitalize on AI. Hyperscience advocates hyper-automation to empower organizations to navigate challenges along their GenAI adoption journeys, streamline document processing, and automate complex workflows.

Cisco recommends a more technological infrastructure approach. Its AI Readiness Index noted that most networks are ill-equipped to meet AI workloads, with only 21 percent of companies having the necessary GPUs to meet current and future AI demands. Further, as few as 13 percent feel ready to capture AI’s potential, yet 98 percent express urgency to deliver on AI and 85% believe they have less than 18 months to act.

“AI is making us rethink power requirements, compute needs, high-performance connectivity inside and between data centers, data requirements, security and more,” said Cisco’s Chief Product Officer Jeetu Patel. “Regardless of where they are on their AI journey, organizations need to be preparing existing data centers and cloud strategies for changing requirements, and have a plan for how to adopt AI, with agility and resilience, as strategies evolve.”
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