NEW YORK--(BUSINESS WIRE)--Hyperscience, a market leader in hyperautomation and a provider of enterprise AI infrastructure software today released a new report exploring the state of GenAI adoption in 2024 – titled, “Unlocking GenAI: Navigating the Path from Promise to ROI.” The report, based on an online survey conducted by The Harris Poll of more than 500 IT decision-makers between August-September, 2024 reveals high GenAI adoption, with 79% of organizations currently utilizing the technology and nearly all (97%) planning to increase usage in the next 2-3 years.
Among those with GenAI technologies in place, the top uses are data analysis, cybersecurity, and predictive analysis. Yet, among organizations using it, only 52% are applying GenAI to document processing, highlighting a substantial opportunity for improving efficiency.
Moreover, three out of five organizations are not leveraging their entire data estate, including proprietary data. This lack of utilization is essential for producing accurate outputs that meet specific business needs and drive effective decision-making, which leads to frustration among IT leaders. In fact, when it comes to focusing on the critical area of AI training, 77% specifically recognize they are underutilizing available data for training AI, a process vital for staying ahead in the market.
The GenAI adoption journey for organizations is fraught with other obstacles, chief among these challenges is data privacy. This concern is compounded by the complexities of integrating new technologies with existing systems, the burden of high initial investments, and high ongoing costs.
In light of the difficulties organizations face during their GenAI implementation, exploring alternative solutions becomes essential. The survey examined the adoption of Small Language Models (SLMs), which are not as widely embraced as their larger counterparts. While skepticism about SLMs persists among IT professionals, at least 3 in 4 organizations using GenAI report SLMs outperform LLMs in speed, cost, ROI and accuracy, suggesting a significant opportunity for those willing to embrace this technology.
Unlike larger models, SLMs are a safer, lower cost alternative to alleviate data privacy concerns as well as other challenges in the adoption of GenAI initiatives. They can also be finely tuned to specific 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. Alarmingly, three out of five decision-makers say a lack of understanding of their data is hindering their ability to fully leverage GenAI to its maximum potential,” said Andrew Joiner, CEO of Hyperscience. “The true potential of GenAI lies in adopting tailored SLMs, which can transform document processing and enhance operational efficiency. While skepticism about SLMs persists, performance data indicates a promising future for their adoption. Many enterprises are seeking to build their own 'sovereign' models using proprietary data, and SLMs are ideally suited for this approach.”
The report also explored the ethical concerns surrounding AI adoption, which are widespread, yet they do not appear to deter organizations from implementing GenAI. While 83% of organizations express ethical concerns, 4 in 5 organizations still utilize GenAI technologies. Small enterprises are more likely to express concern around ethical implications (91% compared to 79% of large enterprises).
As the landscape of AI continues to evolve, this report serves as a vital resource for IT decision-makers looking to harness the transformative power of GenAI while addressing the inherent challenges and responsibilities.
Hyperscience stands at the forefront of hyperautomation, empowering organizations to navigate challenges along their GenAI adoption journeys. By leveraging Hypercell for GenAI, businesses can effectively harness the capabilities of both SLMs and LLMs to streamline document processing and automate complex workflows. This not only enhances operational efficiency but also positions companies to gain a strategic advantage in their back-office operations. Partnerships with industry leaders like Google Cloud and HPE enhance these capabilities, providing an end-to-end development platform for enterprises.
About Hyperscience
Hyperscience is a market leader in hyperautomation and a provider of enterprise AI infrastructure software. The Hyperscience Hypercell platform unlocks the value of an organization’s back office data through the automation of end-to-end processes, and transforms complex documents into LLM and RAG-ready data to power new enterprise GenAI experiences. This enables organizations to transform manual, siloed processes into a strategic advantage, resulting in a faster path to decisions, actions, and revenue; positive and engaging customer, public, and patient experiences; and dramatic increases in productivity.
Leading organizations across the globe rely on Hyperscience to drive their hyperautomation initiatives, including American Express, Charles Schwab, HM Revenue and Customs, Mars, Stryker, The United States Social Security Administration, and The United States Department of Veterans Affairs. The company is funded by top tier investors including Bessemer Venture Partners, Battery, FirstMark, Stripes, and Tiger Global.
About The Survey
The research was conducted online in the U.S. by The Harris Poll on behalf of Hyperscience among 503 adults aged 21+ who live in the U.S. and are employed full-time in a manager or higher role, at an IT company with 1,500+ employees, and are a decision-maker for IT Tech. The survey was conducted between August 28 and September 13, 2024. Data are weighted where necessary by the employee size categories to bring them in line with their actual proportions in the population.
Respondents for this survey were selected from among those who have agreed to participate in our surveys. The sampling precision of Harris online polls is measured by using a Bayesian credible interval. For this study, the sample data is accurate to within ± 4.4 percentage points using a 95% confidence level. This credible interval will be wider among subsets of the surveyed population of interest.
All sample surveys and polls, whether or not they use probability sampling, are subject to other multiple sources of error which are most often not possible to quantify or estimate, including, but not limited to coverage error, error associated with nonresponse, error associated with question wording and response options, and post-survey weighting and adjustments.
