There is no doubt about it: Artificial Intelligence is not only part and parcel of our everyday lives already but also features strongly on business agendas as a way to make enterprises fit for the future. This new wave of transformation is approaching fast – it has taken just five to ten years of technology innovation to take AI from the sidelines and turn it into the Next Big Thing.
One reason for the hype is the sweeping visions that are being formulated. Technology providers such as Google, Intel or IBM talk of “improving people’s lives”, “reshaping business and society” and “the power of AI”. Meanwhile, enterprises in other industries are beset by uncertainty. Will AI genuinely revolutionize their business models? If so, how fast will it happen? And what form will the change take? Is it worth venturing beyond tentative pilot projects and splashing out on a major AI investment? What are the best areas for investment?
Even in the face of all these questions, one thing is for sure: What business leaders do today – how they get their organizations ready – will determine whether they will encounter AI as a destructive tsunami or a perfect wave for their enterprise’s renewal.
83% of telecommunications enterprises already use AI
The telecommunications sector is certainly engaging seriously with AI. Initiatives have so far centered around two main types of processes
Advances in speech recognition: with error rates down to just 4.9% in 20172, enterprises are now better able to deploy chatbots and thereby automate customer service.
In networks, artificial intelligence can ensure that loads are distributed intelligently and that demand predictions can be accommodated in infrastructure planning.
In general, a common place to start is by automating routines, or by deploying AI to improve inefficient processes, because these are areas where quick wins can be expected. However, the value added does not emerge automatically from the type of AI technology used.
What is in AI for my business?
The value derives essentially from what AI is at its core: it distils meaningful intelligence from data that is simply too extensive for human processing, makes predictions on the basis of this data and optimizes decisions. AI helps enterprises to reduce costs, offer their customers better service, or design products more closely around what customers want.
However, processes that directly translate into added value for customers are becoming more and more important, with great opportunities wherever AI can make the service not only more efficient, but more customer oriented, and wherever products can be smartly and quickly adapted to customer requirements.
The starting question: What problem should be resolved with AI?
Fixing cost and efficiency issues with AI should only me the initial stage for AI application. For telcos, for example, network infrastructure efficiency is vital in terms of cost and service quality. Looking ahead, Huawei and others anticipate that AI will be the only feasible way to manage and optimize fast-growing mobile traffic, including rising volumes of video data.3
In the mid- to long-term however, focus your attention on strengthening customer orientation through AI. A typical application scenario, where the algorithm learns and improves, are ‘next best offers’. Based on an analysis of buying behavior and individual needs, an AI-based system can distil this intelligence into identifying and offering the most appropriate upselling or cross-selling products or services. The better the data analysis, the greater the likelihood that the customer will bite.
Get ready for disruption!
The experts surveyed for the goetzpartners study “Reshaping Business Models: Understanding the Benefits of AI” (2018) agree: beyond enhancing existing processes, AI above all has the potential to be disruptive, and over the next five to ten years can be expected to radically transform entire industries.
It is of course inherently difficult to predict the exact nature of this disruption. What is certain, however, is that upheaval can always be expected where three factors converge:
Conventional analysis does not work: AI, with its self-learning capabilities, can however identify patterns in the data and make predictions.4
Decisions are complex, but are based on constant rules: here, AI can be used to aid decision-making.
Labor costs are high: AI-based automation will be relatively expensive, especially at the beginning. At the same time, AI can automate more complex tasks than present technologies and can for example simplify or support middle management activities.
The greatest changes are expected in areas where adaptive AI systems connect with other technologies, thereby forging new synergies. Used properly, AI creates the opportunity to identify early what customers are looking for, to discover niches in the market as they unfold, and to have greater affinity with customers by offering exactly the right products, services and innovations.
To make this happen, AI needs deep integration with internal processes and most enterprises will first need to set those up. By thinking beyond use cases that work in isolation from others, and applying the same technologies, these companies are poised to upend an industry, create a new product category, shape a new business model and totally reconfigure value streams. Instead of today’s single purpose AI applications, such integration is possible only with a multipurpose AI platform.
Invest in expertise!
The DIY approach to building AI competencies will only work for very few enterprises – and most of these will have an affinity with the tech sector and will already have specialist expertise in-house.
How you should proceed instead, will depend on your particular AI strategy and needs. For some applications, especially those focused on efficiency gains, it certainly makes sense to buy in AI solution packages. To take the new ideas, approaches and innovations of AI right to the heart of the business, you’ll need a different kind of investment. You can look into collaborations with partners with the requisite expertise or ideas of their own. Another option is to invest in a startup whose innovative strength and dynamism can have a positive impact. In any case, the investment strategy is a key part of your AI strategy and essential for success.
Align AI with the core of your business!
The main business reasoning for AI is still: what problem does an enterprise want to resolve, and what value does AI create for customers? On the technical level, if AI is cloud-based, data quality and Internet bandwidth are key to making the AI rollout a success. On the organizational level, what matters most – alongside the need for a clear strategy – is having the right expertise and enough cash to invest. Other success factors to single out are security and transparency.
AI is set to become a key business driver in the future, so it is imperative to over-haul legacy structures right now, before it is too late, and create a robust future basis for capturing high-quality, comprehensive customer data, process data and machine data. By this point, at the latest, enterprises that lag behind other players in AI adoption or have failed to adapt their business models at all will suffer serious consequences.
Yet despite the need for action, AI initiatives can only deliver genuine value if they are aligned with the core of the enterprise – they must fully embrace it and extend it. There is no point just jumping on the bandwagon without a sense of direction.