TECHNOLOGY Are you
TECHNOLOGY Are you missing out on the TRUE AI? potential of Artificial Intelligence is top of mind, but is its impact understood? Research suggests that the short-term impacts are vastly overstated and the longer-term implications remain unexplored. We see this same misapplied focus in enterprises where efforts either target micro-sized use cases with disproportionate expectations of returns or oversized, abstract dystopian concepts like replacing large chunks of the workforce. The structural implications are often unexplored. For example, with the adoption of smartphones and mobile broadband, the effect on the quality of phone voice connections is insignificant compared with the effect of Artificial Intelligence (AI) on industry value chains and the creation of new ones like the sharing economy, with disruptors such as Uber and Airbnb. A similar wave of structural change is likely with the widespread adoption of AI – embedded AI. Embedded AI refers to the deep intertwining and widespread adoption of AI in every step of the value chain (think Internet). Here’s what organisations can expect from a world of embedded AI. would be continually tested against claims, which could reimagine the “actuarial” model upon which DISRUPTING INDUSTRY VALUE CHAINS the insurance industry is based. Insurance premiums Embedding AI in the organisation has the potential to disrupt entire business measured in cents, anyone? models, much the way Amazon is using digital technology to upend retail. For example, consider the impact these changes could have on the insurance RECONFIGURING CURRENT OPERATIONS industry. Dynamically mining consumer preference and behaviour data could The multitude of micro use cases of AI being considered lead to faster, more accurate and personalised risk profiles. These profiles today is premised on wildly unrealistic expectations of www.kearney.com 4 FACTS EVERY MANAGER SHOULD KNOW ABOUT AI FACT 1: THE AI BOOM IS SUSTAINABLE AND SHOULD NOT BE IGNORED For the first time, machine-learning algorithms are beating humans in tasks such as image recognition and voice-to-text translation, and complex games such as Go. This AI boom is fuelled by a convergence of three factors: a breakthrough in deep-learning algorithms, the proliferation of big data (structured data) to train these algorithms and an exponential speedup in processing power for machine-learning hardware, such as the graphics processing unit (GPU) chipsets that cut down a machine’s training time from months to days and hours. 24 | www.opportunityonline.co.za
TECHNOLOGY impact. The true value of embedding AI in current operations is reconfiguration, not optimisation. For example, leveraging chatbots for customer service is a limited and narrow use case – still useful, but not game-changing. Embedding a suite of AI capabilities in the customer journey: behaviour analysis to predict issues/calls, dynamically routing queries based on customer context and mood, instantaneous solutions and “rebates”, and, yes, traditional chatbots, can upend customer service as we know it. Can you imagine a call centre without any employees, a call routing system without any prompts, or perhaps proactive information sent to the customer before they make a call? When you think about it, the answer is, of course, yes. AI: EVERYWHERE, EVERYONE, EVERY TIME AI has the potential to improve the lives and efficiencies of the workers in this new organisational structure. It would require companies to treat and train AI as a fundamental aspect of the business, available beyond isolated pockets of expertise. When companies enable every employee to utilise a suite of AI tools, those employees will be empowered to make the best possible decisions with the latest information. Embedded AI systems deliver data and information, freeing workers up to look at the bigger picture. This enterprise AI suite could transform knowledge workers’ jobs in the same way that Microsoft Office transformed the way we communicate. LAYING THE GROUNDWORK AI’s capabilities will surge in the future as we continue to develop exponential technologies, including augmented reality, virtual reality, nanotechnology and digital biology. To reach this stage of innovation, we must start with a solid foundation. So go ahead and build your micro-solutions that improve operations and enhance your operations now – they will provide good lessons, but perhaps not billions of rands, in efficiency. But as you invest in near-term efficiencies, spare a thought or two about the bigger picture: How do I embed AI in my business to deliver structural change? Consider the following when deciding where to use AI for enterprise automation: •One-time costs. Assess the initial capital outlay for a new AI solution, such as developing an algorithm and acquiring training data. Open-source access to algorithms and pay-as-you-go “AI as a service” platforms can lower the fixed-cost hurdles, but access to training data can be either an expensive bottleneck or a powerful source of differentiation. • Switching costs. Evaluate the costs associated with displacing the existing solution with an AI solution, including technical hurdles such as the ability to open the AI algorithm’s black box to trace and explain decisions and human obstacles such as political and cultural resistance to change. • Ecosystem requirements. Determine if an integrated solution will require any complementary technologies. For example, an AI solution that must be integrated with innovative IoT sensors and emerging robotics technology will be more complex to adopt. • System externality hurdles. Consider the extent to which the AI solution could negatively affect third parties that did not choose to use the new technology, bearing in mind that the value of the solution will increase as more users adopt it. AI automation is rapidly becoming a reality across organisations and value chains. Now is the time for forward-thinking business leaders to adopt a disciplined, portfolio-based approach to develop machine-learning capabilities, data and partnerships to remain relevant. FACT 4: ADOPTING AI IS ABOUT MORE THAN TECHNICAL FEASIBILITY Some AI applications will be adopted faster than others, even though the technical requirements are comparable. Broader solutions can ensure that a company’s portfolio of AI initiatives can unlock value in the near term while also paving the way for long-term aspirations. FACT 3: AI IS READY FOR DEPLOYMENT ON SELECT ACTIVITIES The Japanese insurer Fukoko plans to use AI to replace more than two dozen human agents who process claims, and Goldman Sachs used machine learning to transform its 600-person trader unit into a much leaner 200-person team between 2000 and 2016. However, not all organisational activities are suitable for AI automation under today’s narrow paradigm. FACT 2: AI IS BEING USED ACROSS ORGANISATIONS BUT WITH A LIMITED SCOPE So what will AI be able to do for enterprise automation in the next five to seven years? Most experts say companies will adopt narrow AI, or supervised machine learning that is focused on one task. AI algorithms will be able to use training data to learn how to automate a task, but once the task is mastered, the solution will be narrow, and in most cases, the machine will not be able to generalise that learning to perform other tasks. Widespread use of broad, human-like general intelligence, in other words, unsupervised and context-aware, could be decades away. The true value of embedding AI in current operations is reconfiguration, not optimisation www.opportunityonline.co.za | 25
