IT companies facing challenges in developing Generative AI solutions?

Generative AI (artificial intelligence) carries the potential to unleash additional spending and revenue opportunities within the IT industry. However, insights from recent research reports by Infosys and LTIMindtree highlight the challenges encountered by IT companies in their pursuit of developing effective Generative AI solutions for enterprises.

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Generative AI (artificial intelligence) holds the promise of unlocking discretionary spends and revenue for the IT industry, whereas, recent research reports by Infosys and LTIMindtree shed light on the hurdles faced by IT companies striving to build successful Gen AI solutions for enterprises.

Data quality stands out as a fundamental concern, with enterprises expressing reservations about the collected and gathered data’s reliability, posing a significant challenge in constructing effective Generative AI solutions. According to an Infosys research report, nearly half of respondents identified data challenges, either related to privacy and security or usability, as their most significant obstacles to implementing generative AI.

The report also highlighted inherent risks such as hallucination and intellectual property infringement associated with generative AI relying on public data. To mitigate these risks, businesses are increasingly inclined to develop organization-specific tools trained on corporate data rather than relying on public sources.

Another report from LTIMindtree emphasized that 78% of respondents in the UK and Continental Europe pointed to data quality or availability issues as the primary barriers to Gen AI adoption. For enterprises embarking on their Gen AI journey, challenges include operational costs, data quality and availability, and the necessity for precisely identifying suitable use cases.

Ethical, security, and regulatory issues were cited by a staggering 79% of leaders as barriers to the successful adoption or scaling of Gen AI, with 78% identifying a lack of suitable skills and expertise as a major challenge. The healthcare industry, despite leveraging advanced technologies such as big data and machine learning, has shown limited interest in adopting Gen AI due to regulatory barriers, concerns over patient data security, and the complexity of health data.

Similarly, the banking and finance sector, highly regulated as well, exhibited only 17% adoption of Gen AI in the survey. Consumer-facing industries like retail and manufacturing lead in Gen AI adoption, with 40% of Gen AI leaders operating in these sectors, according to LTIMindtree’s survey.

Region-wise, challenges varied: the U.S. faced difficulties in identifying appropriate use cases, the UK, Continental Europe, and the Nordics grappled with data quality or availability issues, and the Nordics specifically cited technical infrastructure challenges as their top barrier.

As the IT industry explores the potential of Generative AI, overcoming challenges related to data quality, talent scarcity, privacy concerns, and regulatory barriers will be crucial for the successful implementation and scaling of Gen AI solutions in enterprises. Enterprises must navigate these obstacles strategically to fully harness the transformative power of Generative AI in their operations.