The Prominence of Technology (AI) in Business Strategy: A Consulting POV Research Report

Background of the issue:

According to the economics of strategy, the main objective of any firm is to maximise profits (Adam Smith, 1776). The idea has been adapted to formulate numerous business strategies in every industry with the help of outdated yet notable methods such as “Competitive Strategy” (Michael E. Porter, 1980), “Thinking, Fast and Slow” (Daniel Kahneman, 2011) and Game Theory (Neumann and Morgenstern, 1944). These methods have helped firms design strategies for gaining a competitive advantage over other companies but are heavily reliant on human expertise and intuition.

The Consulting industry has leveraged these methods along with tools such as GE-McKinsey Nine-Box Matrix and Bain’s Balanced Scorecard for many years to help their clients make informed business strategy decisions for many decades. Data analytics has also been a major contributor to helping consultants understand the business challenges of their clients.

There are numerous challenges with these existing methods which a Strategy Consultant might face while crafting a strategy for their clients.

• Limited scope of Analysis: There is a very high probability that consultants even though experts in their domains might struggle to analyse huge chunks of raw data. Consequently, this may result in missed prospects and sub-par decision-making (Davenport, T.H. 2013).
• Bias in decision-making: Humans are susceptible to cognitive biases, which can lead to poor strategic planning and decision-making (Lovallo et al., 2003).
• Inefficiency: Manual data entry and analysis is a laborious and erroneous process which can impede the strategic planning process and result in subpar outcomes.
• Insufficient Data: The most common challenge which a consultant might face is the availability of the information by the client. Clients might be sceptical to share their sensitive data because of privacy and security issues which can create gaps in the suggested strategy (Alstyne et al., 2016).

We cannot shy away from the fact that we have an immense amount of data but we are still poor on information. While striving for running a successful business we have to ensure that our decision strategy is Agile. We cannot rely on processes, tools, and strategies that stretch over 3-5 years and are audited annually because the market and trends are changing at a velocity that most of us could not have imagined in the past. The most efficient weapon in the arsenal is probably technology. Artificial Intelligence along with machine learning and deep learning algorithms has paved the way to combat this issue by helping businesses analyse huge amounts of data and identify patterns and insights to make informed decisions.

Literature review

One of the earliest applications of AI in business dates back to the 1980s with the advent of expert systems which used historical data and rule-based reasoning to help decision-making in a particular domain. FORDCOST was designed to make the supply chain for the Ford Motor Company more efficient by reducing the cost of production taking into consideration factors such as the cost of raw materials, cost of labour and production schedules (Zaleski, 2002). Since then AI has been adopted in almost every industry with its nascent potential. The application ranges a wide range of spectrum varying from automation of a process to unleashing opportunities for the future.

In terms of AI’s capabilities, it wouldn’t be an understatement that we have just scratched the tip of the iceberg, nevertheless, Consultants are incorporating AI for business strategy in the following ways.

• Predictive analytics: This employs data, statistical algorithms, AI, and ML to forecast likely future events based on historical data. It can be used to assess potential market prospects, forecast financial results, and gauge client behaviour, providing insightful data for business planning (Davenport, T.H. 2013).
• Sentiment Analysis: AI and ML are used with natural language processing (NLP) for sentiment analysis to evaluate text data and determine the emotion or attitude it is attempting to convey. Business strategy can be aided by knowing what consumers think of a product or brand and identifying the areas that require improvement (Liu et al., 2004).
• Image Recognition: Deep learning algorithms are used with AI and ML to recognise or identify objects or patterns in images. Its use in strategy includes determining consumer preferences or trends through social media image analysis (Warren, 2017).
• Personalisation: To generate individualised experiences, AI and ML may analyse client data such as behaviour and preferences. Identifying areas where personalisation may be adopted through smart business methods, can help firms increase customer satisfaction and profitability (Kostenci et al., 2022).
• Forecasting: By examining historical data, AI and ML can forecast trends or events that are likely to occur in the future. Revealing potential dangers or possibilities and empowering businesses to take more informed decisions, can help shape strategy.

As a result, companies are re-evaluating their organisational structure and competitive strategy for the first time in 100 years realising that to not just expand, but also compete and survive, businesses must reinvent themselves around these new technologies and skills (Bhattacharya, 2021).

Research Question(s): How can Consultants leverage Artificial Intelligence to design business strategies for clients to achieve sustainable competitive advantage?

The aim of this research is to analyse how Artificial Intelligence along with various other embedded technologies help consulting firms craft the appropriate business strategies for their clients to achieve sustainable competitive advantage in an industry.

The main objectives of this research are:

• Review the literature on Artificial Intelligence and how it works in conjunction with Machine Learning, Natural Language Processing.
• Research and collect data on how consultants have successfully imbibed the said technology for various clients.
• Analyse the data collected, compare it with the literature and come to a conclusion.
• Suggest best practices and recommendations based on the conclusion.
• The following questions would be answered during the study of this project:

The following questions would be answered during the study of this project:

1. What are the different AI techniques used for business strategy and their specific applications?
2. How are AI technologies currently used in various industries to steer strategy and what benefits have they yielded?
3. What are the key challenges and limitations of using AI for strategy and how to overcome them?
4. What are the ethical considerations that need to be addressed while implementing AI for business strategy and how can they be managed?
5. How can AI be used to improve customer experience and engagement, and what are the best practices for implementing it in a customer-centric manner?
6. What are the key success factors for the implementation of AI for business strategy and how can they be measured or evaluated?

Method and Approach:

The research philosophy undertaken for this project is predominantly positivism as it seeks to generate empirical knowledge about the use of AI in Business Strategy, but certain aspects of the study (Questions 4 and 6) will also consider interpretivism and critical realism as they involve subjective experiences and perspective of stakeholders through qualitative methods and underlying social and organisational structures respectively. (Saunders et al., 2009).

The literature review will explore various disciplines such as Artificial Intelligence, Machine Learning and Natural Language Processing and their usage as various building blocks of Business strategy. The applications of these technologies to various operations in businesses and how they transform the business to achieve better results will also be researched. A study of ethical concerns with the use of these technologies will also be carried out along with solutions to minimise them.

The author will collect qualitative data by conducting interviews within his network and will try to schedule interviews with industry experts to gain insight into their experiences with the clients, the challenges they face and understand social obligations in terms of data privacy and ethical concerns which follows while using these technologies.

The author will also include some of his own learnings and knowledge which he has developed while working as an automation and innovation consultant for various clients.

Dissertation Timeline:

Key Deliverables:

A 12000-word dissertation to the Business School. The contents would comprise of Introduction, Literature review, technology and its application, limitations, ethical concerns, evaluation and conclusion, along with references and appendices.

Resources:

1. Time spent interviewing AI experts in Consulting Industry, Subject matter experts and Business Strategy consultants.
2. Expenditure:

• Travel
• Telephone calls
• Printouts and photocopies
• E-journals and books to be purchased

3. Software:

• Microsoft Office and Teams
• Google Data studio

4. Discussions with supervisor, university faculty and relevant people from previous organisations.
5. Research:

• Textbook, e-journals and e-books from the university library.
• Online resources such as Google Scholar and Research Gate.
• Online Business strategy consulting insights such as Bain, McKinsey and Deloitte.