The term “strategy” has always been a central point of interest for all businesses. Companies are always concerned about their business future and growth and this is the reason why they are always concerned about strategy. The strategist Mckeown (2015) stated that “Strategy is about shaping the future”. When applied to business, the strategy is defined as “the match between its internal capabilities and its external relationships. It describes how the business responds to its suppliers, its customers, its competitors, and the social and economic environment within which it operates”. In other words, the strategy is a framework for making key decisions that define how the company will compete and achieve growth in the future.
Business strategy importance stems from its impact on the decisions made by the company to influence and serve customers on one hand and compete against rivals on the other. As businesses evolve, the term “business strategy” developed accordingly, and an increasing number of business consultants got more interested. Historically, the term was massively influenced by the manufacturing industry and was attached to the company’s deliberate competitiveness. Thanks to Harvard Business School strategy guru M.Porter (1980) who explained that studying a company’s strategy entails analyzing how the company competes in the marketplace and what is the competitive advantage created to differentiate the company from its rivals.
Nowadays and being driven and influenced by technology, businesses are entering or about to enter a new era led by the power of data processed by “Artificial Intelligence”. In the past decade, AI technology has played a key role in reshaping the landscape of many industries as it offered a newer competitive advantage to enhance business performance through advanced data analytics, enhanced decision-making and process automation, etc.
To lay down the foundation of Artificial Intelligence, it is defined as a computer-based system that has super processing power, which can perform complex analytical calculations on huge sets of data to produce new patterns and relations that reside within the data. Artificial Intelligence resembles human intelligence in its ability to process voice, video, and human sentiments which can open the door for new dimensions and endless opportunities, (Turing 1950, Domingos 2015 and Sterne 2017).
Realizing the power of AI technology in improving companies marketing activities and operations indicates that embracing AI is not a choice. If companies plan to stay in business and achieve growth, they need to adopt AI since it will be the bridge for the future. Sterne (2017) stated, “An increasing number of marketing decisions employ some sort of AI, and this trend will only increase”.
However, The reports coming out from big consulting companies such as PwC, Deloitte, KMPG, and others, indicate that the vast majority of companies across different industries struggle in formulating and integrating effective data strategy into their business strategies, due to the massive challenges being encountered in this domain. Hence, it utterly important to investigate and analyze these challenges to shape effective data and business strategy. The previous finding has been emphasized by the great business strategies Richard Rumlet who observed that “A strategy is a way through a difficulty, an approach to overcoming an obstacle, a response to a challenge. If the challenge is not defined, it is difficult or impossible to assess the quality of the strategy… if you fail to identify and analyze the obstacles, you don’t have a strategy. Instead, you have a stretch goal or a budget or a list of things you wish would happen”.
Studying insightful reports from highly esteemed consulting companies and meeting different technology and business experts across the world, offering a great opportunity to identify and categorize the top challenges being faced by companies in formulating artificial intelligence strategy. These challenges can be categorized into three main streams: Lack of national AI strategy, Financial challenges, and Organizational challenges.
Results show that governments are still in the early stages of developing robust national data access and governance laws that can effectively regulate data access and privacy. Following the previous finding, it’s being argued among chief executives that in knowledge-based markets, security and privacy issues are primary concerns for customers’ specially when this information is collected and used for profiling and creating commercial patterns about customer's preferences without prior approval. They further explain that existing acts and regulations are not sufficient nor convenient since these laws and regulations were legislated before the latest AI revolution. Therefore, current laws and regulations may not consider the unforeseen impacts of AI technology. In 2019, PwC highlighted that (73%) of chief executive officers believe that security and vulnerability concerns are holding AI progress back. They think that current AI practices cannot come without real privacy threats. Therefore, new laws and regulations should be in place to guarantee customers privacy and prevent businesses from using customer's information in a way that may destroy a business's reputation.
Financial concerns may be the most important challenges associated with AI technology from a top management perspective. The latest reports show two main financial challenges that hinder AI technology implementation: Firstly, the cost of implementation and initial investment. Secondly, AI technology monetization.
A Recent report by Delliote (2019) draws some sort of explanation about investment allocation issues. The report stated that AI investment should be dealt with as any other business in terms of cost-benefit analysis. However, investment concerns can be understood by indicating that AI's most powerful benefits are indirect, which may provide a reasonable explanation of why higher-level management may be reluctant in allocating more investment. McKinsey (2020), also supported this stance by reporting that many senior managers are reluctant in allocating more investments as initial results haven’t led to a significant rate of returns. The report further explained that lack of knowledge and confidence in frontline managers, make it harder for higher-level management to justify such investment.
Flipping the coin to better understand monetization concern, one may ask, is AI use cases the problem? Is it OTT fault? Or maybe other issues? Delliote (2019) and McKinsey (2020), argued that data should not be blamed, its company’s practices that should be revised. They drive an example of one telecom company that cooperated with a financial services company to answer an important question “How to meet the need of millions of low-income individuals for revolving credit, similar to credit cards, without a risk model?”. Executives realized that a treasure resides within their databases. The company designed a new innovative risk model that evaluates customers’ ability to repay loans. Accordingly, the company has added a product line powered by AI analytics, dedicated to this revenue stream.
Reports and recent studies reveal three main challenges related to organizational issues that are holding back AI technology implementation and integration. These challenges are lack of internal strategy and vision, lack of human resources, and competencies, and employee resistance.
Sterne (2017) addressed such practices by indicating that once strategies are set, they are part of history. Managers get immersed in daily activities and are involved with making a large number of decisions about marketing campaigns, generating new sales leads and enhancing operational efficiency, etc. This may lead to losing the main focus of why they are doing all these activities. He further highlighted supporting the finding, that (1770) managers across (14) countries spend less than (10%) of their time thinking about strategy and innovation.
In addition, a rising challenge is taking place which is a lack of competencies and employee’s resistance to AI technology. Chief Data officers pointed out that some employees try to obstruct or slow down AI technology implementation progress. Some do so, driven by fear of job loss while others try to hinder progress because they don’t understand AI technology benefits and challenges.
STS as most of the companies is going through a transformation phase from traditional business models into a new digital era, which adds more pressure on the management level to change the mindset of the company on different levels including the employees, processes, and technology to better understand and adopt the massive emerging changes. This can be seen across the discussions and debates about aligning business strategy with the rising hype of big data analytics through identifying, studying challenges, and trying to build a new data strategy that can take the company into a successful digitally transformed organization that can enable STS to uniquely compete and achieve growth in future.
As a conclusion to the previous discussion, Artificial Intelligence technology is transforming the landscape of different industries with its revolutionary applications especially in the field of data analytics. As explained and proved by a large number of investigating studies, AI is expected to continue growing in the future in a way that makes ignoring the implementation of AI technology across business activities and operations, a serious mistake that may threaten the future of the business sustainability.
Sr. Account Manager, Telecom Sector