Teradata report sees pressure to implement generative AI

A survey by IDC and Teradata shows only 30% of companies are ready for generative AI, with concerns about bias and industry skill gaps influencing business

A survey by IDC, sponsored by Teradata, highlights top-down pressure on companies to adopt generative AI, but just 30% of enterprises are ready to do so.

The survey, which spoke to 900 C-Suite executives from around the world, has revealed that, whilst 9 out of 10 executives understand the potential of generative AI (GenAI), two out of three are worried about the consequences of GenAI, particularly concerning bias and disinformation.

With the rapid development of GenAI, including ChatGPT, issues of bias are becoming increasingly prevalent and could lead to serious real-world consequences. 85% respondents confirmed that they are seeing little change - fearing greater challenges in the near future.

Large enterprises face global pressure to adopt GenAI

The survey showed that companies are becoming digitally mature, with 54% saying the information flow within their organisations is either very unconstrained or highly unconstrained and free flowing. 

However, the survey highlights executives at large enterprises worldwide are facing pressures to adopt GenAI, pointing to ever-increasing data complexity and managing a growing skills gap within the industry.

Although nearly 80% of the 900 global executives surveyed had high or significant trust that GenAI could be leveraged for their company’s future offerings and operations, the survey suggests that more work needs to be done.

The survey also revealed the extent of which executives say their organisations have been impacted by major corporate changes over the last two years. Participants noted that their companies have seen a new or significantly increased focus on economic challenges and geopolitical changes, as well as a growing influx in work-from-home and hybrid work patterns and an increased focus on ESG.

Similar to this, a large skills gap exists within GenAI, with just 30% of survey respondents saying that they are prepared or ready to leverage GenAI today, with only 42% completely agreeing they will have the skills in place to implement GenAI in 6 to 12 months.

89% of those surveyed also said they understand GenAI’s merits and potential, but 57% believe interest in GenAI products will fade with time.

Helping businesses to follow the curve of digital transformation

Hillary Ashton, Chief Product Officer at Teradata, spoke exclusively with AI Magazine to provide further insight into if corporations believe that AI will live up to its hype in the near future. 

She told us: “Generative AI and large language models (LLMs) are poised to transform every industry. There’s a lot of excitement about the potential for Gen AI and LLMs, but at the same time, I’d also caution businesses to not get caught up in the novelty.”

“Without a clear business value, companies can end up wasting a lot of money. I recommend starting with the end goal in mind, then regularly evaluating ROI and price performance.”

For businesses to ensure that they are staying ahead of the curve when it comes to digital transformation, Ashton stated that they should be identifying use cases to explore and what benefits that these can offer, including customer experience.

“Good data is essential when it comes to digital transformation and implementing AI. That’s why businesses must invest in technologies that improve data quality, governance, and reliability. 

“The key is having a cloud analytics platform that processes and connects data from every source and architecture across the business. Giving all parts of the business access to harmonised data and self-service solutions will help you get the most value from your data and deploy AI/ML projects faster and easier,” she continued. 

“AI can do super smart and disruptive things – and humans STILL have the wheel. AI has a lot of potential to help address the cyber skills gap. But only when you can trust the AI to deliver accurate and ethical outcomes,” she said.

“The data reflects there is still a significant trust gap when implementing AI tools. The first step companies can take to build higher trust and confidence when using AI, is to start off with good, clean, and reliable data.”

Ashton also spoke about how hybrid working models have affected the way that AI is viewed in the workplace. According to her, when used effectively, AI will dramatically amplify what both people and businesses can achieve. 

She said: “As a technology, trusted AI is only as trusted as the people using it and the processes you have in place for implementing it. And now there are more and more people working from home, on the road, or in the office.” 

“That’s why it’s so important for all teams within the company to use a platform that harmonises data from across the enterprise and ensures data governance, integrity, and model management.”


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