Digital Transformation and Professional AI Governance

Introduction

We are experiencing a wave of continuous innovation, widely spread, that is reshaping the way we think and act.

According to Amy Webb, "The last technological supercycle that happened was the industrial revolution. But unlike the industrial revolution, there are three general-purpose technologies in this supercycle.

To better understand the impact of this technological supercycle, it is important to explore the three general-purpose technologies: artificial intelligence, biotechnology, and interconnected devices. Each of these technologies is playing a role in digital transformation and the way we interact with the world around us.

Artificial intelligence (AI) is a technology that enables machines and systems to perform tasks that normally require human intelligence, such as speech recognition, decision-making, and language translation.

Biotechnology involves the manipulation of living organisms or their components to develop useful products, such as medicines and genetically modified crops.

Interconnected devices, also known as the Internet of Things (IoT), refer to a network of physical devices that communicate and exchange data with each other over the internet.

Compared to the technologies of the industrial revolution, which included the steam engine, electricity, and mass production, today's technologies are more integrated and interdependent. While the industrial revolution transformed production and manufacturing, the current supercycle is transforming the way we interact with the digital and physical world.

AI, for example, is being used to optimize production processes, improve energy efficiency, and develop new products. Biotechnology is revolutionizing medicine and agriculture, while interconnected devices are creating a more connected and intelligent environment.

Another important characteristic of these technologies is PEOPLE. This is an important aspect to be observed! People are the WHY of these three technologies being representative; they directly impact the way we understand, interact, relate, and benefit from these new resources.

In healthcare, AI and biotechnology are transforming the diagnosis and treatment of diseases, offering more precise and personalized solutions. In everyday life, interconnected devices are making homes and cities smarter and more efficient, improving the quality of life.

It is also important to consider the challenges and ethical implications of these technologies, such as data privacy and the need for responsible governance to ensure that benefits are widely distributed and risks are mitigated.

From this perspective, companies, governments, and civil organizations need to invest in professional AI governance. This investment is not motivated by being something new to be controlled; it is about the importance of its impact in this supercycle, in our society, and the potential to change the way we know the world today.

What is this transformation and what is its relationship with professional AI governance?

Happy reading!

Digital Transformation

Digital transformation is not just about technology; it is about how technology is used and how it impacts our society, our habits, and behaviors, that is, how we relate to all this transformation.

Digital technologies enable tools for a profound transformation in the performance of governments, civil society, and the competitiveness and productivity of companies, as well as in the training and inclusive participation of society, creating conditions for everyone to prosper.

The economy has been undergoing a data-driven transformation. The volume of data has been growing in increasingly larger proportions. This exponential growth has been demanding more robust technologies, with increasingly significant computational and storage capacities. To deal with this exponential growth of data, several robust technologies are being used.

One of these technologies is Big Data, which allows the analysis and processing of large volumes of data in real-time. Big Data provides the necessary data foundation for Artificial Intelligence (AI) to operate effectively.

AI, in turn, improves data quality and automates complex processes. AI algorithms can be used to detect patterns in large datasets, predict future trends, and make informed decisions.

For example, while Big Data collects and organizes huge amounts of data from various sources, AI can analyze this data to identify valuable insights and automate actions based on these insights.

This synergy between Big Data and AI allows companies not only to better understand their data but also to use this information to optimize operations, personalize customer experiences, and innovate in their products and services.

Another technology is the Internet of Things (IoT). This technology is contributing to data growth, connecting devices and sensors that generate large volumes of information with smart sensors and communication networks, allowing real-time data collection and analysis, improving operational efficiency and decision-making in various sectors, such as manufacturing, agriculture, and smart cities.

According to McKinsey, the adoption of artificial intelligence (AI) and advanced analytics (AA) has the potential to unlock an annual value of about $0.6 to 1 trillion in productivity gains in Latin America. OECD estimates show that companies that rely on data analytics increase productivity by 5% to 10% compared to those that do not.

These productivity gains estimates do not depend solely on the use of data and analytics but on other factors, such as data analysis and management skills, innovative processes, and sector-specific characteristics.

The way institutions think about this transformation is an indicator of how much innovation will be present in their daily lives.

Innovative institutions have stopped questioning how their applications and structuring elements will be and be executed to ask how digital transformation can create environments, processes, and efficient use of technologies for their business needs and people's well-being.

The main challenges in digital transformation are based on five interdependent pillars, which together form the foundation for a successful transformation:

1. Understanding and conscious and sustainable use of data: This pillar involves classifying and unifying data into intelligent flows to generate real-time insights, reducing costs, and increasing operational efficiency. For example, a company can use Big Data to collect and organize data from various sources, while AI analyzes this data to identify patterns and predict future trends.
2. Robust and resilient technological infrastructure: A robust infrastructure is essential to support the processing and storage of large volumes of data. It must be secure, flexible, and able to adapt to market needs and variations. For example, a well-designed technological infrastructure can support the implementation of AI and IoT systems, allowing real-time data collection and analysis.
3. Digital Security: In a constantly changing and data-centric environment, digital protection is fundamental for business continuity and the preservation of personal and transactional information. Digital security ensures that the data collected and analyzed is protected against unauthorized access and cyberattacks.
4. Hybrid and innovation-friendly workplaces: Digital transformation also involves creating work environments that encourage collaboration and innovation. This includes implementing technologies that facilitate remote work and digital communication, as well as creating physical spaces that promote interaction and idea exchange.
5. Sustainability: Digital transformation must be sustainable, seeking a balance between innovation and continuous efficiency. This includes adopting practices that minimize environmental impact and promote social responsibility. For example, using technologies like Big Data and AI can help companies monitor and reduce their energy consumption, contributing to environmental sustainability.

These pillars do not work in isolation; they complement and reinforce each other. For example, a robust and resilient technological infrastructure is necessary to support real-time data collection and analysis, while digital security ensures that this data is protected.

Similarly, creating hybrid and innovation-friendly workplaces depends on implementing technologies that facilitate collaboration and digital communication. Additionally, sustainability must be considered at all stages of digital transformation, from data collection to the implementation of new technologies.

Finally, all elements of digital transformation are directly influenced when driven by artificial intelligence, directly impacting people, processes, and essential services for any business.

With all these considerations, responsible and professional AI governance is an important and necessary requirement, but what is it?

Professional AI Governance

Before talking about AI governance, I would like to clarify some fundamentals of what will be governed, as this seems sensible to me. The term Artificial Intelligence, or simply AI, was coined in 1956 at a conference at Dartmouth University in Hanover, United States. This event marked the beginning of unified research as an AI discipline, some of which influence AI development to this day.

Often the term AI can be somewhat confusing and lead to misinterpretations. Let's look at the isolated definition of "intelligence":

"Intelligence is a set of intellectual characteristics that allow an individual to know, understand, reason, think, and interpret. The word has its origin in Latin, and its original meaning refers to an individual's ability to choose between various possibilities or options."

Intelligence is not something unique and universal; it can be classified by contexts such as linguistic, logical, spatial, musical, and emotional, among others. Each context, in turn, involves specific skills to make choices and evaluate possibilities.

Artificial intelligence, in turn, can simulate certain aspects of human intelligence, but it is not truly intelligent because it lacks self-awareness or emotional understanding and, most importantly, because it is not individual.

Individuality is a human characteristic that makes us exclusive and unique in our choices.

Even if two people are subjected to the same experiences, the same sources of knowledge, and the same values, they may make similar choices and analyses, but rarely identical ones. When we expand our context to thousands of people subjected to different conditions and knowledge, we start to have a diversity of choices and analyses that are increasingly broad and often conflicting, including in ethical and social values.

From this perspective, AI cannot be considered ethically safe either, because to be able to simulate intelligence, it requires training techniques with data generated by humans or sensors, and with that, it can respond to events and stimuli.

AI can be configured not to execute its algorithm for certain conditions and contexts, but these "guardrails" come from a chain of values defined by a particular society and within the updated timeline, that is, values and conditions practiced in the past are not relevant to current definitions. An almost universal example of this scenario is slavery; at a certain period in history, it was an ethically accepted value in many societies

With this, we also come to a reflection and another foundation about AI:

"We, as human beings, are creating our AI algorithms, therefore, our AI algorithms inherit any biases we may have." (Carl E. Mathis, CIPP/E, CIPP/US, CIPM, CIPT, Privacy Architect, Privacy Engineering Center of Excellence, HP).

Comparing the uses of AI, we arrive at some common elements:

• Technology: Its use to achieve specific goals.
• Autonomy: The level of autonomy in using the technology to achieve predefined objectives.
• Human Involvement: The need for human contribution to train the technology and identify the goals to be followed.
• Output/Results: Produces results with the technology, such as performing tasks, solving problems, and producing content.

These considerations about the nature of intelligence and artificial intelligence highlight the complexity and ethical challenges involved in the development and use of AI. Given this, the need for professional AI governance to ensure that technology is used ethically and responsibly becomes evident.

Thus, professional AI governance refers to the set of policies, procedures, and frameworks that guide the development, implementation, and use of artificial intelligence in an ethical and responsible manner. This includes defining ethical principles, creating diverse review committees, and implementing transparency and accountability practices. AI governance is essential to ensure that technology is used fairly and beneficially, minimizing the risks of biases and negative impacts.

For companies, AI governance brings significant gains in maintaining the trust of customers and partners, as well as ensuring compliance with regulations and ethical standards. Effective governance can help companies avoid legal and reputational issues and promote responsible innovation.

Governments, in turn, need AI governance to protect citizens' rights and ensure that technology is used for the public good. This includes creating policies that promote transparency, fairness, and inclusion.

Civil organizations also benefit from AI governance, as it allows technology to be used to promote social well-being and address global challenges such as inequality and climate change. AI governance can help these organizations maximize the positive impact of their initiatives, ensuring that technology is used ethically and responsibly.

Implementing AI governance requires comprehensive planning and adequate training for all employees. The adoption of AI directly impacts both employees and the way customers interact with products and services. A stance of responsibility for the resources and benefits offered should be one of the values adopted by everyone and the organization.

In various countries and continents, this governance is evolving, and regulations are being created for its use. Having a broader view of these movements is a strategic way to anticipate and adapt to regulations, ensuring continuity and evolution of AI adoption within the values and needs of your organization.

Currently, AI technology advances faster than existing laws and regulations. Implementing an internal policy of constant review to ensure that the use of AI is aligned with an ethical context will help promote solutions that comply with current regulations, avoiding conflicts with regulations or future expectations.

Conclusion

Digital transformation and professional AI governance are interdependent pillars. When implemented together, they complement and drive the success of organizations in the digital age.

The adoption of AI by product and service providers can significantly accelerate the digital transformation of companies. AI enables the automation of complex processes, real-time analysis of large volumes of data, and personalization of customer experiences.

For example, AI algorithms can be used to detect patterns in large datasets, predict future trends, and make informed decisions. This not only improves operational efficiency but also allows companies to offer more innovative and personalized products and services. Additionally, AI can optimize production processes, improve energy efficiency, and develop new products.

While the adoption of AI by product and service providers brings numerous benefits, it is necessary to consider some implications to ensure that technology is implemented ethically and responsibly.

It is important to evaluate the transparency and accountability of suppliers regarding the use of AI. This includes defining ethical principles, reviewing and implementing transparency practices with these suppliers. Additionally, it is essential to ensure that suppliers have robust digital security policies to protect personal and transactional data.

These precautions allow professionals and employees to focus on strategic and higher value-added activities.

Finally, we have reached the end of this article for now.

In summary, professional AI governance is essential to ensure that artificial intelligence is developed and used ethically, transparently, and responsibly, benefiting companies, governments, and society as a whole, especially in a digital transformation environment.