If disruption was the word of the times for much of 2020 to 2021, modernisation was the strategy that underpinned many digital initiatives during 2021, on top of goals like resilience and sustainability.
But as organisations allocate resources to pursue modernisation, Boards and the C-suite leadership are calling for greater scrutiny on the return on investment (ROI) given that economies may be trending upwards, but uncertainty remains.
Among the many technologies that continue to garner sponsorship, both formal and behind backroom discussions, is artificial intelligence. To help us understand how AI is helping accelerate digitalisation and modernisation efforts, we are joined by James Ang.
According to James Ang, senior vice president for APAC with Dataiku, there are six broad areas where AI is shown to accelerate an organization’s digitalisation and modernisation efforts.
“Firstly, productivity gains from optimising business processes. Second, augmenting the labour force. Powered by AI, you can drive employee productivity. Third, is increasing customer demand resulting from highly personalised AI-enhanced products and services.”
“Fourth, which is what the board is interested in, improving risk management, improving customer experience would be the fifth area and finally, increasing market share for your products and services,” he enumerated.
What are the current challenges business leaders face when incorporating AI into their business ecosystems?
James Ang: The challenge is not in technology. 95% of failures in AI projects happen due to organisations lacking the right culture and processes. Culture must be driven from the top, to build an AI-powered organisation. You must also have the right processes and governance in three areas – AI governance, data governance and explainability.
The ability to explain your models and algorithms. The second challenge is the lack of skills, expertise and knowledge. There is a worldwide talent shortage for data scientists. Finally, it's the fear of the unknown, and not knowing where to begin.
How should businesses approach their AI strategy?
James Ang: Begin with prioritising the business value you want to create. At Dataiku, we have the 5 E's strategy. First is to explore, Explore what AI means to your business, define your priorities and solidify your reasons to leverage AI.
That's where the top-down culture is important. Next, Experiment by estimating the value of AI with early projects and raising overall AI awareness. Then, Establish by creating tangible value from initial use cases and laying foundations for the scalability of your framework.
Fourth, is Expand, expanding the business use cases of AI across the organisation to accelerate overall business value. Finally, Embed the use of AI across all business activities ensuring that it’s part of your organisation's DNA.
What are the traps to avoid in planning and executing an AI strategy?
James Ang: AI projects fail because of a lack of clarity on the goal, the timeline, the cost, the leadership sponsor, and the methodology. Choose your use cases wisely and start with a business opportunity in mind.
Fortify the quality of your data, ensure that your data sets are comprehensive, accurate and up to date and remove biases. Third, ensure that you address your model’s explainability, the ability to continuously validate your models & algorithms and are compliant with data privacy regulations.
Finally, don’t build in silos. Democratise AI and involve stakeholders across the organisation to drive AI adoption.
Given that technology continues to evolve. How does one design an AI strategy that is NOT tied to any specific vendor solution but instead is dynamically aligned to the business?
James Ang: The goal is to have an agile and flexible solution that can accommodate large volumes of data. Also, ensure that the platform that is intuitive, scalable and extensive enough to involve business users so they can benefit from it. When considering buying or building, evaluate the core competency of your organisation and choose the best way to deploy your resources.
Both approaches have pros and cons and come with trade-offs, financially or in terms of resources. Leaders should plan for the long term and limit the technical debt when employing AI within their organisation.
Click on the PodChat player to hear Ang’s discord around regional AI development.
- Before we begin our PodChat, in 30-seconds or less, what makes Dataiku qualified to talk about Artificial intelligence?
- How does AI enable businesses to accelerate their digitalisation and modernisation efforts?
- What are the current challenges business leaders face when incorporating AI into their business ecosystems?
- How should businesses approach their AI strategy?
- How does one architect an approach that reflects an evolving business/operational environment?
- (95% AI implementation failure) What are the traps to avoid in planning and executing an AI strategy?
- Given that technology continues to evolve. How does one design an AI strategy that is NOT tied to any specific vendor solution but instead is dynamically aligned to the business?
