Change Management in the context of AI-driven transformation

By Anna Carolina Maccarone

This is my final project article related to Change Management Program as Special Student at USP-S?o Carlos - Professor Mateus Gerolamo.

In this article, I apply my knowledge to provide insights regarding the intrinsic relation between change management in the context of AI-driven transformation. The present paper aims to investigate and to analyze the potential of value for applying change management practices in the relation of increasing the interest and investment in artificial intelligence.

Regarding the tools, I aim to use desk research using methodology of desk research and principles of design thinking to bring some evidence of the opportunity to use change management practices.

1. INTRODUCTION:

Change is the constant force shaping the landscape of organizations today. Whether spurred by? technological advancements, shifts in the market dynamics, or the need for cultural evolution, managing change effectively is a critical aspect of ensuring an organization`s resilience and success.

Business rules have changed. In all sectors of activity, the diffusion of new digital technologies and the emergence of new disruptive solutions are transforming business models and processes. How do we adapt and transform in the digital age? Digital Transformation is not about technology, it is about strategy and new ways of thinking. Transforming into the digital era required the business to update its strategic mindset, much more than its IT structure (ROGERS, David).

It is a fact to consider that work has been continuously changing thought history. Industrial revolutions represent "profound changes in the means of production"and they change work in a short period (Landes, David, 1969). New technologies and their combined use, such as artificial intelligence (AI), robotics, biotechnology are seen as the starting point of the 4th industrial revolution (Schwab, Klaus, 2017).

We live in an ecosystem of overlapping digital technologies - each one towering its predecessor and propelling its successors - is transforming not only our personal and community lives but also business dynamics for organization of all sizes and industries. Responding to these changes requires more than an approach taken into pieces, it requires a total integrated effort - an organization-wide change management process.

1. CONTEXT OF ORGANIZATIONAL CULTURE

Schein (2009, p. 17), describes organizational culture as"[...] the climate and practices that organizations develop when dealing with people, or the exposed values and creed of an organization". Furthermore, among the ideas most widespread by the author is the fact that culture is the meaning to achieve internal integration and adaptation of the group to an external environment. That is, without it, it becomes impossible to integrate the interests of those involved, as well as to appropriate and react appropriately to the influences and threats present in the environment. Thus, the capacity for organizational change is associated with the characteristics of its culture.

Everyone knows that the word innovation has become the center of the strategy of any organization seeking growth. Growth-oriented transformation is not easy. The big difference for companies and professionals in the new economy is generating value for people. Innovation is not just in technology, but in creating solutions that are relevant to today's challenges. Using data as raw material (data-driven), creating new business models, we prepare the entire company in change management to think and act according to user behavior and needs (user-centric).

The current corporate reality demands an organizational capacity to carry out innovation and continuous changes, whether in its products and services, or in its processes, in the technologies used, in the organizational context, or even in their business models. Two issues deserve special attention in this scenario:

What is the organizational capacity to execute the necessary changes? And what is the people’s ability to deal with these challenges? (Gerolamo, Mateus).

1. CONTEXT OF ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) ?is a technology that enables machines to do tasks that normally only humans can do and holds enormous power to transform business, government and society. The technology behind AI is a very complex system of algorithms, data, and models that work together to make complex decisions. Artificial intelligence can be used to teach machines how to make decisions, interact with the environment, and solve problems. When it comes to the ethical side of AI, it is important to think about how we use AI and how we can ensure that machines are not used in ways that violate our ethical and moral principles.

The use of technology by organizations as a strategic tool is not a recent practice (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013; Laurindo, 2008; Venkatraman, 2017),but the connection of the AI technologies usage with business strategy becomes significantly more complex in the relation to other technologies, since AI applications are able to perform tasks that require cognition and were formerly typically associated with humans ?(Bean, Randy, 2019). In this sense, obtaining value from AI investments is more complex than expected, due the paradox that the same person may have negative or positive attitudes towards AI, depending on the specific situation.

To summarize, Fig. 1 illustrates the relationship between the AI disciplines. The diagram shows how deep learning is a kind of representation learning, which is used for many but not all approaches of machine learning, which in turn is considered a kind of AI. The main difference among AI disciplines is the dependence of the human being on establishing rules or defining features to represent a problem. From the AI layer, human dependence on the learning process decreases towards inner layers.?

There are several important components and areas of knowledge in Artificial Intelligence (AI), but here are five main components that everyone should know about (MJV Technology and Innovation, 2023):

(i) Machine Learning: this is the area of AI that focuses on development algorithms and techniques that allow systems to learn from data.?

(ii) Artificial Neural Networks: there are models inspired by the functioning of the human brain, used in many deep machine learning approaches.

(iii) Computer Vision: This area deals with the processing and interpretation of images and videos.

(iv): Ethics and Regulation: As AI advances, ethical issues related to the technology`s use and impact become increasingly important.

These are just a few examples, and AI applications continue to grow across a variety of industries, including manufacturing, retail, healthcare, finance, energy, entertainment, and more. The key is to identify the processes where AI can bring value, understand the domain-specific challenges, and implement appropriate approaches to reap the benefits of the technology.These are just a few examples, and AI applications continue to grow across a variety of industries, including manufacturing, retail, healthcare, finance, energy, entertainment, and more. The key is to identify the processes where AI can bring value, understand the domain-specific challenges, and implement appropriate approaches to reap the benefits of the technology (MJV Technology and Innovation, 2023):

Customer Service: Chatbot and sentiment analysis systems can improve customer service by providing quick and accurate responses to common questions, as well as identifying issues and customer feedback.

Demand Forecasting: AI can be used to forecast product demand based on historical data and relevant variables, enabling more efficient production and inventory planning.

Medical Diagnosis: AI systems can assist doctors in diagnosing diseases by analyzing medical images, patient data and clinical evidence, increasing accuracy and speeding up the diagnostic process.

Predictive Maintenance: AI can predict failures in industrial equipment based on real-time data analysis, enabling maintenance before serious failures occur, saving time and resources.

Product Recommendation: AI-powered recommendation systems can analyze customer behavior data to offer personalized product recommendations, increasing sales and customer satisfaction.

Fraud Detection: AI can be used to detect suspicious activity in financial transactions, identifying fraud patterns and helping to prevent losses.

Natural Language Processing (NLP): Natural language analysis can be used to extract information from documents, answer questions, analyze sentiment on social media, and automate text processing tasks.

Autonomous Navigation: AI is fundamental to autonomous vehicles, enabling them to navigate complex environments safely by detecting obstacles, interpreting traffic signs and making decisions in real time.

Planning and Logistics: AI can optimize delivery routes, schedule maintenance tasks, optimize resource allocation, and improve project planning.

Complex Data Analysis: AI can handle large volumes of data and identify complex patterns that would be difficult to identify using traditional methods.

1. CONTEXT OF CHANGE MANAGEMENT

The topic of change management had its first foundations before 1990, becoming in the radar after 1990 and its formalization from the 2000s onwards. It is currently a central theme of several projects by multinational companies and large players seeking to differentiate themselves in the market.

Nowadays, the definition of change management gained shape and strength from the learning of several change agents who applied large-scale transformation models in organizations. By the definition of a collective professionals and academics,? "Change Management is a continuous process of individual and organizational learning. This process seeks to implement specific change efforts (projects) in order to achieve a major transformation, people-centric, focusing on results that are aligned with the evolving purpose of the organization. To succeed, the leadership team must understand and manage the organizational culture to decrease the level of resistance and increase people’s engagement. As a result, it is expected that the organization extends its longevity in a constantly changing world”. Definition developed by graduate students of the course SEP5835 – Change Management offered by Prof. Mateus C. Gerolamo, class number 2, Concepts of Change Management, in March, 23rd, 2018. The authors are: Jeanne L. M. Michel, Júlio C. Natalense, Valter Yogui, Welington J. R. dos Santos, e Willian Rossin, Anna Carolina Maccarone. Reviewed and adapted by Mateus C. Gerolamo.

Change management is a framework implemented for a successful transition to new processes, initiatives or goals in the organization. It is an ongoing process, typically led by leadership that begins with the initiation of an organization-wide change and continues as that change is implemented, executed and monitored over time.

There are some elements of change that help organizations to understand their weaknesses and strengths, which will determine their ability to change, according to the table below:?

There are several change implementation strategies that organizations can use to support change initiatives, however two of them are probably amongst the most commonly used ones. They are Kotter’s 8-Step Change Model, the 5C’s model for change (Culture, Communication, Courage, Conviction and Compassion, and Change Strategic Vision).

Regardless of the change process (new software, new process, organizational restructuring, merger, acquisition, etc), people that are involved and impacted by the organizations need to be trained in the future operational model. If there are people involved in the process, there? is organizational change management.

Analyzing the pillars of digital transformation, there are some examples;

(i) the new consumer: need to understand habits and, more importantly, consumer interest in order to act in the right way, at the right time.

(ii) Digital Culture: the new consumer demands from companies a cultural change and the constant search for innovation. Multidisciplinary and collaboration between areas are great stimulators.

(iii) New technologies: technology, such as AI for example, has the power to revolutionize methods and processes, in addition to adding speed and effectiveness by eliminating eros and reducing operational costs.

1. Change Management in the context of AI-driven transformation

Change management, often seen as a complementary function to technology adoption, emerges as a critical success factor in the AI journey. Organizations must recognize that AI implementation isn’t merely about deploying cutting-edge algorithms or optimizing processes. It’s also about restructuring workflows, upskilling the workforce, and realigning the organizational culture to embrace the AI-augmented future.

And a question usually comes to mind: Are people afraid of change? The answer is no. People are afraid of what they will lose with the change. By its nature, AI causes significant changes in the way an organization operates, which affect processes, professional functions and even business models. If this AI implementation process is not carefully managed with change management, this level of transformation can rock your organization, resulting in AI adoption challenges such as resistance to change, low utilization rates, and inability to realize the full benefits. of AI.

However, the successful implementation and adoption of AI goes far beyond sophisticated technological capabilities. They require an effective AI change management strategy that includes change management as well as a strong emphasis on the human aspect of change.

A people-centric approach is critical to successful AI adoption. Change management programs, when effective, carefully consider the dimensions of AI integration. These programs include job reconfiguration and ethical implications, as well as promoting an organizational culture that embraces change.

Involving employees to actively participate in the AI implementation process can significantly demystify the technology, showing its role as a tool to improve their work, rather than posing a threat to their employment.?

If managing changes involves changing behaviors and having a clear vision, the need to empower people becomes evident. What seems like a simple task can create learning anxiety. Why is transformational learning so hard?

Diane Coutu has interviewed the renowned Edgar Shein (2002) about organizational learning. By the vision of Shein, learning and the change that inevitably accompanies it is a complex process. and argues that requires blood, sweat, tears, and a certain level of anxiety to achieve the desired effect.?

In this article, he describes two basic types of anxiety--learning anxiety and survival anxiety-that drive radical relearning in organizations. Schein's theories spring from his early research on how American prisoners of war in Korea had been brainwashed by their captors. He cites the parallels between the "coercive persuasion" tactics the Chinese communists used to control their prisoners (isolating powerful ones and overseeing all communications) and the corporate boot camps that American companies use to indoctrinate their managers.

In his studies, he warns, often more a source of frustration than achievement for groups and for individuals. Schein dismisses the popular notion that learning is fun; he focuses instead on the guilt and anxiety associated with radical relearning and draws some disturbing parallels between organizational learning and brainwashing.?

Shein compares the study of the war with organizational learning. The basic principle is that learning only happens when survival anxiety is greater than learning anxiety. Most companies prefer to increase survival anxiety because that’s the easier way to go. If leaders really want workers to learn new things, they have to educate them about economic realities in a way that makes their messages credible. When management gains that credibility, it can create the kind of anxiety that leads to a safe learning environment.

By the end, Shein synthesized that corporate culture can be changed. Certainly a relief for everyone.

Using ChatGPT, an artificial intelligence tool, I have used the following prompt: "How change management can support AI-driven transformation?".? Here is the answer by the platform:

Change management is crucial in any organizational transformation, and when it comes to A0-driven transformation, it becomes even more significant. Here are ways in which change management can support AI-driven transformation:

• Create Awareness and Understanding: Training and Education: Provide comprehensive training programs to help employees understand AI concepts, benefits, and implications
• Communication: Regularly communicate the goals, benefits, and progress of AI implementation to all stakeholders to ensure transparency.
• Engage Stakeholders: Leadership Alignment: Ensure that leadership is aligned with AI objectives and can effectively communicate the strategic vision to the rest of the organization. Employee Involvement: Involve employees in the process to gather insights, address
• Address Cultural Challenges: Cultural Assessment: Evaluate the existing organizational culture to identify potential barriers to AI adoption and address them proactively. Cultural Change Initiatives: Implement cultural change initiatives that align with AI adoption, fostering a culture of innovation, collaboration, and adaptability.
• Mitigate Resistance: Change Champions: Identify and empower change champions within the organization to promote the benefits of AI and help address concerns. Incentives: Provide incentives or recognition to employees who actively contribute to the success of AI initiatives, fostering a positive attitude towards change.
• Adapt Work Processes: Process Redesign: Modify existing business processes to integrate AI technologies effectively, ensuring that workflows are optimized for AI-driven improvements. Workflow Integration: Ensure that AI tools seamlessly integrate into existing workflows, minimizing disruption and optimizing efficiency.
• Data Governance and Privacy: Data Policies: Establish clear data governance policies to address concerns related to data privacy, security, and ethical considerations. Compliance Training: Provide training on compliance requirements related to data usage and privacy regulations associated with AI implementation.
• Continuous Learning: Skill Development: Offer ongoing training and development opportunities to help employees acquire the skills needed to work alongside AI technologies. Adaptive Learning: Implement adaptive learning programs to keep employees updated on the latest advancements in AI and its applications within the organization
• Feedback Mechanisms: Feedback Loops: Establish mechanisms for collecting and acting upon feedback from employees, allowing for continuous improvement in AI implementation strategies. Agile Approach: Adopt an agile approach that allows for iterative adjustments based on feedback and changing organizational needs.
• Risk Management: Risk Assessment: Conduct a thorough risk assessment to identify potential challenges and proactively address issues related to AI implementation. Contingency Planning: Develop contingency plans to manage and mitigate risks associated with AI projects, ensuring a smoother transition.

1. WHY THIS TOPIC?

I have chosen the topic Change Management in the context of AI-driven transformation for professional reasons. For professional reasons, as a consultant advisor we have been applying change management in multinational companies. Besides of we also have:

(i) Change is the constant force shaping the landscape of organizations today;

(ii) Business rules have changed. In all sectors of activity, the diffusion of new digital technologies and the emergence of new disruptive solutions are transforming business models and process.;

(iii) Digital Transformation is not about technology, it is about strategy and new ways of thinking. (ROGERS, David);

(iv) We live in an ecosystem of overlapping digital technologies and are transforming not only our personal lives but also business dynamics for organizations of all sizes and industries. Responding to these changes requires more than an approach taken into pieces, it requires a total integrated effort - an organization-wide change management process. (Schwab, Klaus, 2017).

1. METHODOLOGY

The present paper was developed through bibliographical research and aims to investigate and to analyze the potential of value for applying change management practices in the relation of increasing the interest and investment in artificial intelligence.

Regarding the tools, I aim to use desk research using methodology of desk research and design thinking to bring some evidence of the opportunity to use change management practices. The data collection was based in the following parameters:

(i) describe the general understanding of AI by different profiles of people;

(ii) describe the increasing market of AI (number of academic publications, searches of the topics on the internet, number of jobs)

(iii) examples of companies in Brazil with investments in change management practices (survey (change agents, change departments, transformation office, other initiatives)

The source of research was based on bibliographic material, exploratory reading, selective and reflective reading and interpretative reading in which I seek keyworks of change management and artificial intelligence to relate the correlation of the issue that I would like to bring in this study.

ANALYSIS OF THE STUDY:

Following the research of the study, there is some data and information related to the topic to be studied.

(i) describe the general understanding of AI by different profiles of people;

Following research from Ipsos (2023) "How people across the world feel about artificial intelligence and expect it will impact their life", the understanding of AI is still lagging, they have mixed feelings about AI, just half say AI impacted their life in the past few years, but 2 in 3 expect it will soon change it profoundly and not all changes are expected to be the better.

Despite rising geopolitical tensions, the United States and China had the greatest number of cross-country collaborations in AI publications from 2010 to 2021, increasing five times since 2010. The collaboration between the two countries produced 2.7 times more publications than between the United Kingdom and China—the second highest on the list. ? In 2021, China continued to lead the world in the number of AI journal, conference, and repository publications—63.2% higher than the United States with all three publication types combined. In the meantime, the United States held a dominant lead among major AI powers in the number of AI conference and repository citations. ? From 2010 to 2021, the collaboration between educational and nonprofit organizations produced the highest number of AI publications, followed by the collaboration between private companies and educational institutions and between educational and government institutions. ? The number of AI patents filed in 2021 is more than 30 times higher than in 2015, showing a compound annual growth rate of 76.9%.

FINAL CONSIDERATIONS:

Although people are afraid of dealing with artificial intelligence, data indicates that the search for this topic has grown over the years. It is also important to say that people still do not have in-depth knowledge about AI and are still afraid of changes in their lives, especially at work.

Companies that are investing and applying artificial intelligence tools are aiming to use change management methodology in order to provide a future-oriented and human-centric organization. Using some business cases, we bring some evidence of how companies are applying change management practices? in order to perform their transformation offices, bringing harmony to embrace AI application.

The result of the research demonstrates that companies from all over the world are aware of the importance of using artificial intelligence in order to be able to compete in the market and include the topic strategically in their decisions. The survey regarding AI shows that business leaders have, continuously and increasingly, recognized the importance of AI technologies in the transformation process.

Following research from Ipsos (2023) "How people across the world feel about artificial intelligence and expect it will impact their life", people still do not have appropriate knowledge, the change process is necessary to adapt the company's culture and changes in a new scenario.?

In the era of AI transformation, change management stands as a crucial pillar for successful implementation. By adhering to a robust change management framework, organizations can navigate the complexities of AI integration, unleash its transformative potential, and mitigate the risks associated with resistance and disruption. Recognising and addressing employee concerns about job impact are essential for fostering a positive work environment and securing employee buy-in. As AI continues to reshape industries, organizations that prioritize change management will emerge as leaders, driving sustainable growth and unlocking the full potential of AI technologies.

There is a great market opportunity to apply? change management in the organization culture and also a job opportunity for experts, since that on LinkedIn, the main professional platform, aims to fulfill 36,000 positions related to change management in the market.

REFERENCES:

BEAN, Randy. Why fear of disruption is driving investment in AI. Sloan Management Review. 2019.

BHARADWAJ, El Sawy, Pavlou, & Venkatraman, 2013; Laurindo, 2008; Venkatraman. Digital Business Strategy: Toward of a next generation. 2017.

COUTU, Diane, Shein, Edgard - The anxiety of learning - Harvard Business Review (2002)

FEHLING, Ronny, MESSEB?CK Reinhard, LEWIS, Mike, KIRCHHOFF, David, HILBERATH, Christoph, GOSSY, Gregor, KLEVENZ, Markus, GRUBER, Diego and STOLBA Simon. AI Is Revolutionizing How Companies Manage Transformations, 2023.

GEROLAMO, Mateus, BERTASSINI, Ana Carolina, PONCE, Liliana Graciano. Introdu??o à Gest?o de Mudan?a em Organiza??es. Ed. Pecege, 2023.

KOTTER,, John - Leading Change. Harvard Business Review. 2012.

LANDES, David. The unbound prometheus: technological change and development in Western Europe from 1750 to the present. Cambridge University Press. 1969.

ROGERS, David. The Digital Transformation Playbook. Columbia Business School, 2021.

SCHEIN, Edgar Henry. Cultura organizacional e lideran?a. S?o Paulo: Atlas, 2009.

SCHWAB Klaus. The fourth industrial revolution. Cross Business. 2017.

The state of AI in 2023: Generative AI`s breakout year. Quantum Black by Mckinsey Consultancy.

How people feel across the world feel about artificial intelligence and expect it will impact their life. Ipsos Research. 2023.

Artificial Intelligence Index Report. 2022.

Average of search of AI word: https://www.statista.com/statistics/1398211/ai-keyword-traffic-volume/?

ChatGPT, Open AI, 2023 - How change management can support AI-driven transformation

Delloit, 2019 - Becoming an AI-fueled organization: how to build an AI-ready culture

*Typographical erros may occur