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How could a ‘conventional’ company transform itself into an AI-powered organization?

Christian Guttmann


Every conventional company has products and services offered to industries ranging as wide as manufacturing, financial services, health care, and transport. These industries change constantly and that change is often driven by customers that expect better quality at a reasonable cost. Companies that adapt to changing conditions and meet customer expectations will continue to grow their business and stay relevant in a competitive business landscape.

One of the most important success factors of the AI transformation journey is the role of the board and executive leadership, as it is their primary responsibility to set the direction and respond adequately to changing market forces. For many conventional companies, Artificial Intelligence (AI) proves to be a major force as it can transform a market fast. Customers quickly change their demands as emerging AI-driven products and services provide superior quality and performance. The most powerful decision-makers of a company, namely the board and executive leadership, need to understand the potential of AI - as otherwise the company may face fierce headwinds, and could be outcompeted quickly.

Machine Learning (ML) is one of many AI technologies that has transitioned from research labs into business use. The application of ML has already generated multi-billion USD in consumer markets (e.g., online marketing and content recommendations) and ML is well on its way to transforming traditional ‘non-AI’ and ‘non-data driven’ industries. By addressing the following three topics, the board and executive leadership may benefit in making the right decisions.

- How do AI and data-driven technology change a conventional company’s market position? This is an important question as a conventional company could be directly and immediately affected by competitors that use AI more effectively than they do. The size of this threat needs to be assessed while taking into consideration what is hype and what is real. If a market changes over a longer period then this is often due to long cycles of product introduction in a particular industry (e.g. the pharmaceutical and car industry). In these long-cycle industries, regulations and new frameworks will take time to put into action, but when they are, it often opens a flood gate of new products, and it is important to be ready when that happens.

- How can AI and data-driven technologies add value to its products, services, and operations? There are at least three AI value generators in a company: AI can improve existing products and services, AI can create a new portfolio of products and services, or AI can make operations more efficient and effective. Operational improvement is often the initial way for a conventional company to create value as the operations do not affect the main value creation of their business – it is, however, often a more conservative path. In operational practice, AI can improve processes, such as logistics and supply chain, sales, and IT processes much of which resides in ERP and CRM systems. The company’s services and products will then be created more effectively and efficiently, hence keeping the company competitive. In this context, it is worth mentioning the well-known ‘productivity paradox’, where a new technology does not seem to have a measurable ROI, that has an impact on the bottom or top line of the business. However, the introduction of AI keeps a company competitive, meaning that they can continue to build and sell their products and services under competitive operations. Successes might then result in spillovers into the first and second AI value generators mentioned above.

- How to assess the risk and cost of introducing AI technologies that bring wide implications on products, services, and operations. It is critical for the company to estimate the risk and cost that comes with AI transformation, as every company has a unique risk profile and will have an acceptable level of risk and cost. A low-risk approach often results in smaller market shares, less growth, and less future revenue, and that path is often chosen if AI technology is not well understood. Deep knowledge of AI in the board and executive leadership is valuable in predicting market developments and identifying what changes need to take place in the company.
These are tough questions, but as history has shown, companies that understand how new technology changes a market and adapt sufficiently quickly will prevail over those that do not. This has been the case with the steam engine, transistor, internet, and electricity. The need to understand and adapt will be no different for a business in the age of AI.
Each board and executive leadership will respond to the above questions differently, and influence a company’s path in their own way. However, there are three considerations that can help to address the above.

- Ensure that there is deep subject expertise for an adequate understanding of AI in the context of your company’s future success. Executive leaders and board members should interact with the smartest and most suitable AI leaders in the company as much as possible to gain a comprehensive understanding of what AI can do for the business. Such a leader has a PhD in AI and a strong business acumen. This will help to identify which business direction to take and what is technologically feasible. In short, hire the best AI talent that you can get your hands on.

- Ensure full awareness of the strength and weaknesses of the company’s existing product and service portfolio. Conventional business leadership is often caught up in group think where everyone agrees that “our business has been good so far, so let's do more of the same a little bit better, and we will be fine”. This is harmful to a company’s adaption of new market conditions. Hence, create an open space for transformational possibilities for your company’s future.

- Create meaning for key stakeholders in times of change. Initial AI projects or products should aim to be meaningful, rather than impactful, for as many stakeholders as possible. ‘Meaning’ here suggests that the application of AI in the business makes integral sense to key stakeholders that are often resistant to organizational change. These stakeholders can be middle management, employees, and shareholders who are sometimes not convinced of the value that new technology can generate (particularly when their own department or job is perceived to be under threat). For those stakeholders, the outcome of an AI initiative needs to meet a wide range of minimal acceptable - and often intangible - criteria. In practice, an initial AI product or project does not have to be the one that creates the most value, but the one that convinces important stakeholders about AI technology.

Artificial Intelligence is possibly one of the most transformational technologies that humanity will encounter. Transforming a conventional company requires the board and leadership to make a deep evaluation of the company’s business and apply the right expertise to identify how AI can drive business value and future success.

"Artificial Intelligence is possibly one of the most transformational technologies that humanity will encounter. "

Dr. Christian Guttmann is an Artificial Intelligence scientist and global AI executive with a strong track record in creating high-impact business and research with Artificial Intelligence, Machine Learning, and Data Science. Christian was recently named a Top 100 AI global leader by Deep Knowledge Ventures, based on my achievements in science, technology, and business.


Christian Guttmann

"Create an open space for transformational possibilities for your company’s future."

Executive Director, Senior Researcher




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