By Vishal Rambharose, AI Transformation Lead - Bermudez Group Limited, Trinidad and Tobago
Artificial Intelligence (AI) adoption has taken off and is poised to help revolutionize organizations through a vast range of technologies, including Machine Learning (ML), Generative AI (GAI), and Large Language Models (LLM), among others.
AI solutions and training are trending topics, as organizations seek to bridge inefficiencies in conventional operations, and stakeholders that are demanding more automation, efficiency, and data-driven decision making to provide that competitive edge.
According to PwC’s 27th Annual Global CEO Survey (2023), most organizations are making strides toward reinvention. However, 45% of them lack confidence that their organizations will endure beyond a decade on their current trajectory. To thrive, these leaders understand that a more transformative approach is essential.
CEOs in this survey, who have already adopted AI believe in the fast pace of adoption and its potential for disruption. Additionally, 50% expect generative AI to enhance their ability to build trust with stakeholders, 60% anticipates it will improve the quality of their products or services and about 33% are notably more optimistic about its transformative potential in the next 12 months to the next three years.
So, how can the successful transition to an AI-powered organization happen? There isn’t a universal solution as there is no one size fits all and the approach depends on the unique needs and circumstances of each organization. However, it begins with the most important factor - the role Leadership. The Executive as it is their responsibility to chart the direction; and the AI Transformation Leader to drive the integration and strategic implementation of AI technologies. Together they can effectively lead and take the organization thorough the transformation journey as this is an ongoing process that demands dedication and continual optimization.
Understanding the types of AI:
It is important the People (from leaders to employees) understand the types of AI and what they each represent. For the layperson, there is a misconception that AI means to replace humans in the workforce. There are three classifications for AI – Artificial Narrow Intelligence (ANI), Generative Artificial Intelligence (Generative AI) and Artificial General Intelligence (AGI). ANI is predominantly utilized in applications that are designed do specific tasks. Some examples include facial recognition, recommendation systems like those used by Netflix or Amazon, virtual assistants like Siri or Alexa, self-driving vehicles, predictive maintenance models. This means it cannot do tasks outside of the defined tasks and humans are still required to “train” it.
Generative AI can create original content such as text, images, videos, music, code or other data based on the descriptive prompts supplied by a user. It was the next evolution in AI that reuses training data to solve new problems. Some uses cases include chatbots, media creation, and product development and design. It is meant to help humans reach new productivity levels.
AGI on the other extreme, aims to use previous learnings and skills to accomplish new tasks without the need for humans to train the underlying models. If AGI wants to learn how to perform a new task, it must figure it out by itself. It should be able to perform reasoning, abstraction, and learning in any situation regardless of any explicit instructions. As of now, this has yet to be realised. It’s a future that may one day become a reality, but that day is decades away, but this is what the average person associates with AI.
Technology and AI are meant to bring about improvements and yes, they can potentially replace or reduce the employee labour force depending on the organization’s AI initiatives. However, with the available options of ANI and Generative AI, the relevance remains on accelerating analysis, enhancing the customer experience, optimizing business processes and boosting employee productivity.
Bring people along:
Leaders must embrace understanding and managing employees. When leaders are transparent, purpose-driven, and trusted employees are likely to feel more comfortable with change. The inevitable anxiety, fears and tensions that come with a transformation project can be addressed though upskilling and reskilling.
“Bring people along” is a term that implies involving the people from the organization in effecting change from the early phases of concept and design to providing them with the necessary support and training for the transition. These people most likely go on to become “champions” for the cause and can influence others to see the benefits. Where the goal is boosting employee productivity, Generative AI can serve as an assistant, handling tasks from searching to creating thereby saving the employee time, reducing cost and enhancing efficiency. Ideally, it provides the opportunity for the employee to engage in other meaningful job-related tasks, but to achieve this the employee must be trained on the AI system to effectively adoption and maximize the benefits.
Upskilling and Reskilling are eventualities in the coming years as the pace of technology advances. Upskilling involves learning new skills or teaching an employee new capabilities to enhance their existing role. Reskilling, on the other hand, entails acquiring new skills to transition into a different role or training for an alternative role. Upskilling advances talent on a linear path such as progression whereas reskilling depicts a lateral movement.
Upskilling can be achieved by introducing training, literacy programs and certifications, or by encouraging access to the open learning management systems so that employees can keep up to date with the latest developments and new trends related to data and AI. These efforts will encourage knowledge sharing and the development of multidisciplinary teams, fostering synergy across the various business areas.
A 2023 interview conducted by Harvard’s Digital Reskilling Lab and the BCG Henderson Institute with leaders from forty organizations around the world, indicated that upskilling alone won’t be enough, and workers may need to be entirely reskilled. This is a fundamental and complex challenge that will require workers not only to acquire new skills, but to use them to change occupations. The reskilling revolution has arrived, and organizations must do what they can to make it happen with minimal disruptions. To do this, the survey concluded that leaders must be aware of five paradigm shifts:
In the age of AI, upskilling and reskilling are strategic imperatives that can build competitive advantage quickly by developing talent that is not readily available in the market and by filling skills gaps that are instrumental in achieving strategic objectives before and much better than the competitors can.
It is also important to recognize, that a mix between hiring experts and training existing employees may be required. Outsourcing or partnering with a third party for the first AI initiatives can kickstart the activity and the knowledge gained be transferred to the employees.
Create a thriving Ecosystem:
In this context, an ecosystem refers to an environment where interactions occur between people and AI systems. Data serves as the fuel for AI, while its derivative, information, empowers human decision-making. For it to thrive, the organization would need to adopt a data-driven culture. This means that all employees would need to be data-literate and understand how to use data to make better decisions.
The transformation must infiltrate all the people layers (stakeholders) from the top- level executives and senior managers to the employees, where each level has its own critical role to play in the use of data and AI. For instance, the top level is responsible for defining the strategic lines and setting the strategic objectives. Middle management are expected to create value through innovative use cases that leverage this and bring employees together by communicating, executing and promoting a culture that emphasizes the use of data and AI. The employees provide detailed information on processes and are instrumental in training and testing AI models and eventually become users of these AI systems.
An AI system must deliver meaningful value to stakeholders, who are the key individuals within the organization. When AI aligns with their vision and makes practical sense, they are less likely to resist change. They are usually wary of the benefits of new technology, particularly if they see it as a threat, but a successful buy in would ensure the commitment to embrace, collaborate to ensure its success.
One of the AI ethical principles, respect for human autonomy, put forward by the European Commission best encapsulates the relation that should exist between People and AI. It states that, AI systems should not unjustifiably subordinate, coerce, deceive, manipulate, condition or herd humans. Instead, they should be designed to augment, complement and empower human cognitive, social and cultural skills. The allocation of functions between humans and AI systems should follow human-centric design principles and leave meaningful opportunity for human choice. With this guiding principle, the organization can establish a foundation for the coexistence and success of both people and AI. They can now leverage this synergy to differentiate themselves from the competition.
