Generative AI

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Meta

Generative AI or Gen AI has gained much attention for the global corporates especially after the launch of ChatGPT about one and half years ago. A recent study conducted by Forrester Consulting highlights both the challenges and the persistent enthusiasm surrounding Gen AI technologies.&nbsp;
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Based on the<a href="https://aithority.com/machine-learning/global-study-finds-only-22-percent-effectively-using-gen-ai-across-all-business-functions/" rel="noopener noreferrer nofollow" target="_blank"> study</a> with the advent of generative AI and the hype surrounding it only 22% of companies said that they leverage Gen AI across the enterprise. This number is below the expectations of executives, which signifies the difference between the expected and actual value of Gen AI. However, the interest in Gen AI has not waned and enterprises are still increasing their spending on different applications.&nbsp;
<h2 class="wp-block-heading" id="companies-struggle-with-data-readiness-and-governance">Companies struggle with data readiness and governance</h2>
The research also reveals that over 50% of decision-makers have defined business objectives for Gen AI. However, 79% have identified concern over their organizations’ capacity to achieve these goals because of lack of internal or external skills. Additionally, 79% of the respondents have stated that a lack of current skills remains a problem. Despite these challenges, many organizations have deployed at least three Gen AI use cases and have plans to increase their investments in the next 12-18 months.&nbsp;
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A major challenge to the successful implementation of Gen AI is data readiness within organizations. Surprisingly, only 42% of the organizations have the competence to train Gen AI models, while a massive 89% fail at getting business data ready for Gen AI. Moreover, only 23% of the organizations have implemented governance plans, even though 90% of the organizations see the need for having such plans to facilitate proper use and management of the technology.&nbsp;
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
 “Despite a swift start to the Gen AI race, many initiatives get stuck in the piloting stages as more organizations realize their data infrastructure isn’t ready to adequately deploy Gen AI technologies beyond the proof-of-concept.” <cite>Alex Chubay, SoftServe’s CTO</cite>
</blockquote>
The study also reveals a significant gap in the technical knowledge. 84% of the respondents suggested that more profound technical knowledge is necessary for data integration, model optimization, and use case creation. Moreover, 80% of decision-makers stated that employees lack adequate use case knowledge and comprehension of Gen AI’s sophistication.&nbsp;
<h2 class="wp-block-heading" id="u-s-leads-in-gen-ai-adoption-as-industries-show-mixed-results">U.S. leads in Gen AI adoption as industries show mixed results</h2>
According to the study, data, governance, and skills are the three core elements that have helped organizations successfully implement Gen AI values. Of the four countries analyzed, the United States is the most advanced in realizing the potential of Gen AI, with the United Kingdom, Singapore, and Germany following behind.&nbsp;
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In regards to industry performance, the retail sector has the highest potential for using Gen AI, particularly in training models on owned data. Conversely, the financial services and insurance (FSI) sector faces more challenges before realizing Gen AI gains.
Other industries, including healthcare, life sciences, oil and gas, manufacturing, ISVs, and enterprise technology, show a balanced distribution in achieving Gen AI value. According to the study, businesses with revenues exceeding $5 billion have a more difficult time managing the necessary capabilities due to their large investment in hardware, software, and infrastructure.
Cryptopolitan Reporting by Brenda Kanana

Global businesses struggle to harness the full potential of generative AI, study reveals

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