This article first appeared in Forum, The Edge Malaysia Weekly on April 9, 2018 - April 15, 2018
The world of artificial intelligence (AI) is no more fictional. It is very real — influencing and shaping the new world order, creating disruptions in the way we communicate and the manner in which leaders conduct their business. AI is more than just an idea today, with complexities that pose a threat to the conventional models of manufacturing, management and growth strategies.
How are businesses positioned to understand and cope with AI for business sustainability and growth? In the process of adopting AI solutions, what are some of the challenges faced by businesses? These are very critical questions to gain a perspective on how AI can be deployed for intelligent growth.
Based on a joint research study undertaken by BCG and MIT Sloan Management Review, there are five key areas of AI that need to be addressed.
1. The impact of AI: Many industry leaders are of the opinion that AI will enable their companies to not only move into new businesses but also gain competitive advantages. There is also a common understanding that AI will have sizeable effects on IT, operations and manufacturing, supply chain management and customer-facing activities. Business process outsourcing companies, for example, expect many of the jobs that have moved to low-labour-cost countries in recent years to be automated. Similarly, executives at industrial companies expect the largest effect in operations and manufacturing. For instance, the new A350 programme of Airbus is using AI to speed up and improve production. The company has combined data from past production programmes, with input from the A350 programme and a self-learning algorithm to identify patterns in production problems. In some areas, the system matches about 70% of seemingly unrelated production disruptions to solutions used previously — all in near real-time.
2. The big AI strategy: With the advent of Industry 4.0, there is a need for companies to strategise on how they can harness AI for new growth. In doing so, one of the biggest hurdles faced by companies is to identify and develop the right talent, establish priorities for AI investments and finally, resolve security issues associated with the deployment of AI solutions. While there are laggards, which are yet to develop a clear business case for AI, there are striking differences in the companies that have adopted AI even in the same industry. For example, on the one hand, there are companies such as Ping An — the world’s second largest insurer by market value — which employs 110 data scientists and has launched about 30 CEO-sponsored AI initiatives. On the other hand, there is a large Western insurer that describes the limited scope of its AI programme as “experimenting with chatbots”.
3. A holistic approach: A major difference between leaders and laggards is their understanding of the importance of data, training and algorithms. Success depends on understanding the process and having well-developed systems that can pull together relevant training and continue to integrate findings from data collected over time. At Wells Fargo, according to executive vice-president Agus Sudjianto, one of the important components is to look at unstructured data such as text mining and analyse enormous quantities of transaction data for patterns. This, in turn, helps the company improve customer experience as well as decision-making in relation to customer prospecting, credit approval and financial crime detection. In all these fields, there are significant opportunities to apply AI, but in a very large organisation, data is often fragmented. Dealing with data strategically is the core issue in large corporations, which may not necessarily understand the data needs of algorithms or the processes required to train algorithms.
4. Make versus buy: The need to train AI algorithms with appropriate data has wide-ranging implications for the traditional make-versus-buy decision that companies face with new-technology investments. Generating value from AI is more complex than simply making or buying AI for a business process. Training AI algorithms involves a variety of skills, including understanding how to build algorithms, how to collect and integrate the relevant data for training purposes and how to supervise the training of the algorithm. In other words, using AI for competitive advantage requires companies to build up their internal skills.
5. Three big challenges: Besides the managerial challenges common to technology-driven transformations, companies face AI-specific challenges. The most fundamental and important one is in developing an intuitive understanding of AI. According to J D Elliott, director of enterprise data management at TIAA, a Fortune 100 financial services organisation with nearly US$1 trillion in assets under management, “every frontline manager doesn’t need to understand the difference between deep and shallow learning within a neural network. A basic understanding that — through the use of analytics and by leveraging data — there are techniques that will produce better and more accurate results and decisions than gut instinct is important”.
The second challenge is organising for AI. Adopting AI broadly will likely place a premium on soft skills and organisational flexibility. There are different models — such as centralised, distributed and hybrid — but ultimately, a hybrid model emphasising cross-functional collaboration may make the most sense. Organisational flexibility is a centrepiece of all the AI models. For large companies, the culture change required to implement AI can be daunting.
The third challenge is figuring out how humans and computers can develop from each other’s strengths. This is not easy as companies may need privileged access to data, flexible organisational structures and a good mechanism for people and machines to work together effectively. All of this would also mean tough cultural changes for the companies and their employees.
Managers need to recognise that employing AI goes beyond improving the status quo. The real hard work is to understand the potential shift of the entire value chain and build a sustainable competitive advantage in a changing environment. In making the transition to AI systems and solutions, some of the key measures for companies to adopt are building and sustaining customer trust, performing an AI health check, prioritising AI projects and investments, adopting scenario-based planning and establishing a clear focus and work plan for AI initiatives — where and how they will be pursued, including regular communication, education and training.
Just about any company today needs a plan that makes AI a critical component of its growth strategy. While some are leading, others are lagging even in developing a basic understanding of how AI can support their future business growth. Those that continue to fall behind may find the playing field evermore tilted steeply against them.
Ong Ching Fong is a partner and managing director of BCG Kuala Lumpur
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