Implementing AI has become an instrumental move for enterprises to stay ahead. Especially with the recent advancements in GenAI, which is capable of interpreting and generating data such as text, images, and videos, the possibilities are endless. Leveraging this technology, enterprises have the potential to profoundly revolutionise their product development and operational efficiency, and deepen their customer engagement. 

However, implementing AI in enterprises is not without its challenges. According to an IBM commissioned report by Forrester, implementing AI is of foremost priority for many enterprises in transforming their business processes, and in driving their key business goals. Yet, their commitment to AI is hindered by challenges such as the lack of AI-literate talent, disorganised data, and data privacy and security concerns. Additionally, 90% of the respondents acknowledged that scaling AI presented significant challenges. 

With that being said, the successful implementation of AI requires strategic oversight, as well as thoughtful solutions to the following key challenges. 

Lack of Clarity on How and Why AI Would be Used

As reported in a recent survey by Deloitte, more than 40% of respondents remarked that their enterprises faced challenges in defining and measuring the value of their AI initiatives. Essentially, this can be attributed to a lack of clarity on how and why AI would be implemented. In turn, this ambiguity may lead to fragmented efforts, whereby AI initiatives are performed without a clear picture of how the technology will be integrated with existing systems, or contribute to specific business objectives. This may result in a misallocation of resources, while the potential advantages of AI remain unrealised. 

To effectively overcome this challenge, the enterprise must develop a clear vision in collaboration with C-level stakeholders, to define the importance of AI initiatives relative to their business goals, as well as other factors, including market conditions, the relevant market trends, and competitive pressures. With that, the enterprise must be able to recognise specific AI use cases that can produce tangible business value, and are also viable for implementation. It is also essential for the enterprise to anticipate the risks involved in implementing the use cases, and to develop concrete plans for mitigating these risks. 

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Lack of AI Skills

Notably with GenAI, the technology is increasingly used by non-technical business users, who are relatively less experienced with AI. Hence, they may not have the necessary skills to use AI effectively. In fact, the 2023 Gartner CIO and Technology Executive Survey revealed that leveraging AI for workforce productivity is one of the key areas of underperformance among enterprises, and it is largely due to the lack of formal assessments of AI skills. This highlights the importance of AI literacy, which is the ability to recognise relevant use cases, and to implement the suitable AI applications. This comprises an understanding of the fundamental concepts of AI, as well as the ethical considerations, risks, and the implications.

Therefore, enterprises can spur their AI adoption by partnering with the right technology providers who possess deep AI knowledge and expertise. These partners can closely guide enterprises through their AI journey, from defining their AI strategy, to implementing a comprehensive AI roadmap that is tailored to their needs. To complement these efforts, enterprises may consider investing in AI literacy. For instance, enterprises may partner with educational institutions to hold introductory training programs to equip the workforce with an understanding of fundamental AI concepts. 

Privacy and Ethical Concerns

It is no surprise that 77% of CEOs are concerned about trust in AI, according to PwC’s Annual Global CEO survey 2024. Implementing AI brings up significant privacy and ethical concerns, particularly due to the extensive collection and retention of personal data, which may result in risks such as unauthorised access and identity theft. It is also worth mentioning that the training data for large language models (LLMs) might include unauthorised information, the use of which could lead to potential legal action. Not only that, another major concern is that GenAI frequently “hallucinates”, or generates outputs that seem convincing, but are actually inaccurate, misleading, or even completely invented. 

Addressing these privacy and security concerns requires that enterprises adopt a comprehensive approach. Firstly, robust data protection measures, such as strong encryption, secure storage, and strict access controls are essential to safeguard personal data from unauthorised access and identity theft. Besides that, regular audits and compliance checks can help to ensure that the training data for LLMs is vetted for unauthorised information, thereby reducing the risk of legal complications. In addition, implementing rigorous validation processes and cross-referencing AI outputs with verified data can alleviate the risks associated with AI hallucinations. Overall, it is crucial to define clear lines of accountability and responsibility for the enterprise’s AI systems, to ensure that stakeholders are ultimately accountable for the ethical ramifications of AI-related decisions.

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Poor Data Quality

A common misunderstanding is that AI does not require high-quality data. In reality, data that is incomplete, biased, or inaccurate can severely undermine the effectiveness and accuracy of AI models. So, for enterprises to derive real value from implementing AI, they must have a data foundation that is AI-ready, in that it is curated, accurate, and well-managed. Alarmingly, a 2023 research from Gartner found that 96% of enterprises reported that their data were not AI-ready.

Enterprises should have strong data engineering practices in place, in order to secure their data’s AI-readiness. This involves continuously ensuring that the data accurately represents the specific use cases, including all patterns, errors, outliers, and anomalies needed for training or deploying the AI model for its intended function.

Conclusion

While it’s clear that implementing AI does come with significant challenges, these obstacles present valuable opportunities for transformative growth. As we embrace a new landscape where AI redefines every facet of the enterprise, the future belongs not to those who merely invest in AI, but to those who do it wisely. 

To learn about how JurisTech’s new framework, Juris.AI, is designed to address these challenges and seamlessly integrate GenAI into existing systems, read our new article. Juris.AI, which is JurisTech’s Enterprise Ready Architecture (ERA), is poised to revolutionise the loan life cycle process, allowing lenders to leverage the full potential of GenAI, while upholding the highest standards of data integrity and security.

About JurisTech

JurisTech is a global leading company, specialising in enterprise-class lending and recovery software solutions for banks, financial institutions, telecommunications, and automobile companies worldwide. JurisTech has been mentioned as a Representative Provider for Lending Ecosystems and as a Sample Vendor for Commercial Banking Onboarding across Gartner reports in 2024.

We power economies by reimagining financial services with cutting-edge software solutions, leveraging composable architecture and generative AI. Our offerings include artificial intelligence (AI), auto-decisioning, digital customer onboarding, loan origination, credit scoring, loan documentation, litigation, and debt collection.

Our solutions have enabled businesses across a broad array of industries to undergo digital transformation, providing enhanced customer experiences and, most importantly, achieving their business goals.