If your answer is True, read on to find out why this misconception could derail corporate automation and digitalization efforts…

Businesses turn to automation to support their workforce and improve service to their customers. However, skills barriers and corporate culture challenges can hinder digital transformation efforts. Many leaders mistakenly believe that AI is the “secret sauce” to making bad business processes good. Too bad this not true.

According to Dan Ternes, Chief Technology Officer, SS&C Blue Prism (APAC), creating a seamless link between digital and human workers facilitates their collaboration in order to make the most of AI, automation and digital transformation.

DigiconAsia: How can leaders be convinced that AI cannot turn bad business processes good, and how should they adopt AI with realistic expectations in the current business climate of rising costs, inflation and cyber-talent shortages? 

Dan Ternes (DT): Not every process is ideal for AI. While AI (and automation) can enhance processes, it is not a magic wand to fix all flawed or inefficient processes. True — they can make a suboptimal process somewhat better, but the question is whether the investment of time and resources is justified.

It is essential to recognize that AI can even exacerbate issues in a poorly designed process. So:

    • Leaders should focus on process analysis and having a clear understanding of the desired business outcomes before AI is introduced. Process analysis, including process and task mining, sets realistic expectations on what AI can deliver across entire workflows, not just individual tasks.
    • It is also crucial to note that while AI can automate repetitive, manual business processes, there is a need to balance human elements in business processes, especially those that require nuanced decision-making, empathy and creativity.
    • Additionally, leaders should consider the long-term commitments required to ensure their AI systems are effective and accurate as their businesses continue to evolve. Given today’s increasingly volatile, uncertain, complex and ambiguous business climate, leaders need to leverage platforms that best complement their existing systems and begin with pilot projects to test the feasibility and impact of AI solutions before committing significant resources upfront.

To harness AI’s transformative benefits realistically, it is essential to prioritize process improvement, set clear objectives, and evaluate AI’s capabilities in the organization’s broader business strategy.

DigiconAsia: What should leaders know about the latest AI developments (such as GenAI) that could correct any misunderstandings or inoptimally-applied implementations of AI processes?

DT: On its own, AI is not a cure-all solution. Intelligent automation (IA), as well as a selection of other complementary technologies, provide the ideal vehicle for AI.

    • IA can mimic human behavior and intelligence to facilitate decision-making thanks to the cognitive ‘thinking’ aspects of AI alongside the ‘doing’ task functions of robotic process automation.
    • IA can now go beyond rule-based decisions, adapting its behavior based on evolving conditions, and analyzing large amounts of data.
    • While powerful, two pivotal facets should be considered: AI accuracy and operational AI.
      • Firstly, AI systems are not immune to issues such as biases and unreliable results. These challenges necessitate the intervention of human talent to ensure ethical and reliable AI applications. Without them, the degree of confidence we can place in AI-generated insights is questioned.
      • Achieving the full potential of AI goes beyond just accuracy. It hinges on the seamless upstream and downstream integration of AI into an organization’s processes. In some instances, AI can even unearth more complexities — like revealing problematic transactions — thereby creating additional workloads.

Effective AI implementation requires a holistic approach of diverse expertise, including domain knowledge, data science, ethics, and user experience. To harness AI’s transformative power, organizations should challenge the notion of AI as a “one-size-fits-all solution” and prioritize accurate AI and effective operational integration. 

DigiconAsia: How well have the latest developments in AI been applied in healthcare and financial services, given that these industries now face higher cyber threats?

DT: In addition to accelerating diagnosis and treatment, AI adoption in these industries has had a hugely positive effect by taking away mundane tasks from overworked staff in both patient-facing and back-office environments.

For instance, IA is accelerating cancer pathways for the United Kingdom’s Northern Cancer Alliance, propelling cancer care forward by streamlining processes and optimizing coordination. Within the financial services industry, AI is widely used for improving fraud detection, predicting credit risks, and evaluating market data for investment management.

However, as AI systems advance and become increasingly data-driven, they also become lucrative targets for cyberattacks. Moreover, AI has ushered in the era of ‘offensive AI’ where cyber threats are now more sophisticated and human-like. That said, there is some cause for optimism with robust cybersecurity measures that ‘defensive AI’ brings to the table.

Dan Ternes, Chief Technology Officer, SS&C Blue Prism

DigiconAsia: What are your views about the alarm bells that thousands of leading AI pioneers and technocrats are sounding about putting a short moratorium on further developments until all stakeholders have had time to work out all possible ethical and legal ramifications if advanced AI falls into the wrong hands and/or is abused to a hazardous, out-of-control manner?

DT: The concerns of these experts are inherently valid, and underscore the critical need for a balanced approach to the development of advanced AI. As a powerful technology, AI holds the potential to yield remarkable benefits that shape industries, economies and societies for the better, but it also introduces misuse and unintended consequences.

While there is an undeniable need for ethical and legal assessment of advanced AI, I am of the view that AI innovation should still forge ahead, but not blindly. For example:

    • There is a global focus to build international standards in AI governance, as evident in the World Economic Forum’s launch of AI Governance Alliance.
    • Last year, Singapore launched an innovative AI testing framework and toolkit to promote transparency and trust in AI products and services: by assessing AI accountability, oversight as well as safety and resilience.

As we remain clear-eyed about the promises and the perils of AI, apprehensions about risks should not overshadow the immense benefits that AI development offers.

With the deliberate emphasis for robust ethical and legal frameworks and governance, we can collectively steer a future of “AI for good”.

DigiconAsia thanks Dan for correcting some AI implementation myths and sharing his insights.