Evaluating the AI landscape
For Australian CEOs facing the challenge of juggling urgent operational needs with strategic imperatives, there is limited opportunity to devote headspace to contemplating the ramifications of Artificial Intelligence on their organization. Not only is the pace of AI development incredibly fast-moving, but the concept of AI is extremely diverse, covering everything from analytics to automation, machine learning to natural language processing. Moreover, the AI sector does a great job of keeping out non-technical people – complex mathematical and programming-based concepts mean that even the most determined business leader can quickly be dissuaded from wading into the technical swamp of AI.
But the strategic question remains – how important is it for executives to invest today in an AI strategy for their business? That is, are we living through a genuine tipping point in the way business is done or are we simply caught up in manufactured hype similar to the blockchain fever of a decade ago or the dot.com bubble of the late 1990s? The answer to this question is crucial, as it dictates whether a business should move now to embrace a new way of working or sit back and wait for the technology to mature and use cases to be proven up.
Early evidence suggests that AI is already having profound and transformative effects on the business world. According to a survey by McKinsey, 82% of executives believe that AI will have a substantial impact on their industry. Whilst BCG report that AI adopters experienced a 5% increase in productivity and a 10% reduction in costs compared to their peers. On the revenue side, AI is already the sharpest tool executives have for attracting new customers, increasing their share of wallet and optimizing pricing. But it is on the cost side where the most tangible benefits are being realized with the automation of a variety of front- and back-office activities, including sales and service, risk management, account creation, transaction processing, operations and support functions.
Unfortunately, Australian businesses significantly lag their global peers in AI with nearly 70% of Australian organizations having yet to succeed in delivering digital transformation – a critical first step to the implementation of artificial intelligence. In terms of AI maturity, BCG report that Australian companies have an average AI maturity of just 3.5 out of 10, lagging the global average of 4.3. Such a low level of AI maturity is unsurprising given that Australia spends only one third of what the US spends on AI on a proportional GDP basis. Not only are Australian AI efforts underfunded, but they are also scattergun – survey data suggests they are primary bottom-up, technology-driven projects with only limited alignment to the business. Without a tight link between business problems and AI capabilities, firms run the risk of being distracted by AI pilot programs that even if successful, are unlikely to move the needle or unlock the potential value AI has to offer.
Secret to a successful AI strategy
The key to unlocking AI value is the development of an AI strategy that links the organization’s overall purpose and strategy to specific AI initiatives and business outcomes. Companies should develop a clear line of sight between their strategic objectives, and their AI aspirations. This will translate into defined AI initiatives that drive business outcomes required by accountable business executives. Too often, AI initiatives are peripheral and owned by passionate technologists but largely ignored (and often resisted) by the business. This leads to many proofs of concept (PoCs) but not to broad adoption. A common failure is ‘letting 1000 flowers bloom’ with a corresponding lack of focus and dilution of scarce talent and funding.
An organization’s AI strategy needs to be distilled into discrete business use cases with clear problem statements and corresponding measures of value and risk. If you throw AI at a problem you don’t understand, the risk of getting it wrong is much higher. Executives should, therefore, have absolute clarity on the value they are driving for the customer.
Optimisation, personalisation and automation
Three key questions should be the foundation from which an AI strategy is developed:
a) Firstly, what AI can be deployed to optimize high-impact, high-frequency decision making using real time data and analytics? Pricing is just one example of low-hanging fruit in this area. Woolworths, for example, use optimization algorithms to track products stocked in each store and calibrate the timing and discount depth of promotions.
b) Secondly, how can customer insights be used to deliver a more personal message to customers with better timing to nudge the customers towards the behaviour the business is seeking? For example, retailers are increasingly exploring the use machine learning to better personalize product recommendations through app channels.
c) Finally, for labour-intensive activities, how can AI automate tasks currently requiring human intervention. This need not be robotic automation but could be automation of high-level interpretation or evaluative tasks. Examples of this are current trials using AI for medical imaging interpretation as well as IP Australia’s AI investment to image match trademark applications.
AI culture and adoption as an imperative
Developing an AI strategy is not solely about technology implementation; it requires a cultural shift within companies. There are many examples of companies that have invested large sums of money in AI, only to have the technology left in the proverbial “bottom drawer” by employees who continue to use more traditional ways of working. To fully harness the potential of AI, companies need to foster a culture of AI adoption. This involves investing in employee upskilling and reskilling, promoting cross-functional collaboration, and nurturing a data-driven mindset.
Companies that have scaled AI across their business and achieved meaningful value from their investments typically dedicate 10% of their AI investment to algorithms, 20% to technologies and 70% to embedding AI into business processes and agile ways of working. In other words, these organizations invest twice as much in people and processes as they do in technologies.
Companies that jump into AI thinking it will be a quick implementation exercise are mistaken – transformation driven by AI requires a highly structured approach and a long-term view to develop learning organizations that build their AI muscles over time. These companies gradually incorporate more AI capabilities into different areas of their business – first into adjacent business areas, by repurposing existing AI capability, and then into entirely new areas.
Executives should think big – setting ambitious business objectives, targeting large value pools, and identifying capability gaps – but start small using high-value use cases, focused pilots and a build, test, and iterate approach. It is crucial for organizations to build the digital and human capabilities needed to sustain and scale their artificial intelligence strategy. Companies should be prepared to develop new ways of working, create opportunities for reskilling and upskilling, reimagine processes to facilitate true human-machine collaboration, and deploy a robust AI architecture.