I recently read this book – Radically Human, by Paul R Daugherty and H James Wilson.
The authors are with Accenture and had written a book : Human + Machine: Reimagining Work in the age of AI, where they had discussed various case studies of how many organizations have been using AI, in the area of Human Machine collaboration.
In this book the authors propose five approaches – that they call as IDEAS as the acronym, that can be used to significantly improve the success rates of AI initiatives taken by organizations.
The key message is reassuring that AI is not to replace humans.
They talk about the first stage when the applications of AI was primarily to make routine and repetitive tasks automated. This led to discussions on whether AI will replace humans from their jobs.
The second stage was when AI approaches were used to augment or supplement human capabilities. This meant that humans could be much more accurate and much more productive. This calls for a more collaborative approach between humans and AI based solutions.
In order to achieve the benefits of this approach, one needs to relook at the five elements of innovation and by using many examples from their consulting experience with organizations across the world, the authors clearly demonstrate the adoption of these revised – possibly inverted – approaches to these five aspects.
Taken together, the IDEAS framework can be used for driving innovation across organizations.
IDEAs stands for: Intelligence, Data, Expertise, Architecture and Strategy.
While one should read the book to get a deeper dive into these factors, I will just summarize these for a quick reference:
Intelligence:
To make AI more meaningful and humanlike, one must think beyond the traditional deep learning approaches. Machines need to think and respond like humans. For example, Emotional AI is now finding applications in areas such as helping children in the autistic spectrum or in the autonomous automobiles. Two aspects they highlight are awareness and adaptability.
Data:
In order to address the struggle between the data scientists and the developers to have access to adequate data, for training or testing, approaches such as data echoing [to reuse data], active learning or synthetic data would be very useful
Expertise:
The shift that the authors propose is from ‘machine learning’ to ‘machine teaching’, where the humans can become more active teachers to help the machines learn. This will help understand a lot more context and the machine to become more intelligent!
Architecture:
The adoption of AI approaches widely has also led to the use of IoT types of devices that can constantly sense and generate a large volume of signals. For a system to be able to continuously learn and adapt to newer and emerging situations, it needs to be adaptive and scalable. This has an impact on the overall architecture of the systems. Adopting cloud based [if not cloud native] architectures and a distributed model of the systems [including microservices] to develop larger more complex solutions based on smaller components will help in making the architecture ready for the emerging future.
Strategy:
Ai technologies can significantly improve the friction usually found between strategy exercises and actual execution. With shorter cycles and closed feedback loops, strategies may be refined and fine tuned based on the execution experience. The authors call out three specific approaches that are effective for strategy. These are Forever Beta, Minimum Viable Idea and Co-lab approaches.
The book is divided into two parts.
The first discusses the IDEAS elements in detail and many examples under the theme of transforming innovation.
The second part is about competing in the radically human future, under four sub themes of Talent, Trust, Experiences and Sustainability.
The style of narration makes it a very readable book and I could learn a lot about the work already being done or that has been going on in the recent times, across the globe.
Overall, a highly recommended book for anyone wanting to get a quick update on the state of practice on AI adoption in enterprises.