How Can Enterprises Hit The Generative AI Sweet Spot?

Tomur Ho

Director of Engineering for Asia, UiPath

The evolution of artificial intelligence (AI) has been nothing short of remarkable. In the past, businesses primarily harnessed analytical AI or symbolic AI capabilities to drive efficiency and decision-making. Today, generative AI advancements are unlocking possibilities and generating unprecedented use cases even in industries like fashion, design, media, and entertainment. 

According to IDC, 70% of organisations in Asia Pacific are already either exploring potential generative AI applications or investing in generative AI technology in 2023. Employees are also open to using technologies like generative AI at work. In a recent study, over half of Singaporean workers are receptive to leveraging AI-powered tools such as generative AI and automation to support them at work.

While generative AI has been generating a lot of buzz in recent months, it is important to move beyond the hype and focus on how we can elevate its business value across industries. One thing is clear:  AI alone is insufficient when it comes to achieving end-to-end digital transformation. It’s high time that we embrace integrating AI into the larger puzzle of interconnected business initiatives.  

Maximising the potential of generative AI

The output of AI is only as good as the quality of the input. Maintaining well-rounded data sets and ensuring that data is being aggregated from various sources into a unified reliable repository is a step in the right direction towards achieving data accuracy. In addition, we need to train AI models on the intricacies of business functions to mitigate the risk of biases in AI algorithms. This is where human and machine capabilities can help in a big way – creating a solid foundation for robust AI ecosystems.

On the other hand, while generative AI may be able to understand and create many types of content, it lacks the ability to take action. AI without automation is akin to having a brain without a body – it can think, but it cannot do anything with the insights. In the digital transformation ecosystem, both are needed to empower organisations with adaptive decision-making. 

For instance, financial institutions have already been using a combination of generative AI and automation to streamline the process of identifying suspicious transactions, leading to more efficient fraud detection and prevention.  The synergy of automation workflows and generative AI allows the automatic analysis of data such as transaction history and customer behavior in financial transactions. As anomalous transactions are automatically flagged, human analysts can already focus on verifying transactions and resolving fraudulent cases promptly. Bots can also automate the process of setting indicators in underlying applications to activate alerts in the future. 

Automation can also help clean training data, removing outliers and irrelevant information before feeding these into AI models. By facilitating feedback loops with the help of automation, businesses can refine and update AI models iteratively – reducing the risk of poor decision-making from AI algorithms. One way that automation can help is in identifying patterns in mispredictions. Given the capability of automation to analyse errors in AI models, organisations can better identify the root causes of mispredictions and finetune AI models. The synergy of automation and AI is creating new value, enhancing efficiency, and driving innovation in businesses across industries. 

Fitting generative AI into the digital transformation puzzle

While the synergy of generative AI and automation holds a lot of potential, it may be reckless to just rush into adopting new technologies without a thorough assessment of an organisation’s maturity level. Before anything else, data management processes and underlying systems that support the integration of generative AI and automation capabilities should be in place. 

It is also critical to move beyond a piecemeal approach and consider how individual AI components interact with other systems. When necessary pre-built connectors are lacking, it becomes difficult to integrate AI-generated insights into the relevant parts of an organisation’s infrastructure and take necessary action. As a result, the full potential of AI outputs remains untapped – with organisations unable to realise the broader benefits of AI-powered automation.

Over time, it becomes imperative to implement well-defined governance and change management procedures to ensure that automation and generative AI initiatives align with overall business objectives. Creating a framework that defines stakeholders’ roles and responsibilities provides clarity and prevents confusion and overlaps. Keeping all parties in the loop of changes and updates on automation and generative AI initiatives fosters transparency and trust in the change management process. But equally important is providing adequate employee training and support, as leveraging generative AI presents data privacy risks including misleading results or hallucinations and sensitive data appearing in content generated by other users. Given new processes and workflows, employees may be initially resistant to changes. To gain the support and commitment of employees, organisations must provide them with the required skills and knowledge to effectively use AI and automation at work. 

Transforming employees into AI and automation champions entails cultivating the right mindset and attitude. Forming a center of excellence with expertise and knowledge of new technologies is critical in driving widespread awareness and implementation. The dedicated team can help an organisation accelerate end-to-end digital transformation, enabling employees across all levels to optimise new technologies in their respective roles. VITAL, for instance, has made AI-powered automation the core of its digital roadmap, creating a “bot library” of automation best practices and scripts for more than 100 government agencies in Singapore. By adopting a citizen developer strategy, it continues to empower each officer to become a citizen developer capable of building bots with the aid of low- or no-code automation.

What’s next after generative AI?

All in all, the generative AI of today has become a force to be reckoned with. However, when it is combined with specialised AI, it can create many unique opportunities for enterprises. Specialised AI, which focuses on specific tasks and is trained on task-specific data, offers distinct advantages in enterprise AI, compared to large foundational models trained on general knowledge. Specialised AI can be securely trained with an organisation’s data and optimised for its specific needs, resulting in fast, accurate, and tailored solutions that are cost-effective to operate and that deliver high-value outcomes.

While AI and automation may very well be considered the brain and body behind today’s digital transformation ecosystem, they are not enough to drive long-term success. After all is said and done, humans remain to be the catalysts in the digital transformation equation, infusing the necessary creativity, intuition, emotional intelligence, and critical thinking to drive innovation.