The Imperative of Artificial Intelligence Expertise for Engineers in 2023
Both large and small businesses are giving generative AI priority because, according to a Gartner survey, 80% of CEOs anticipate artificial intelligence (AI) to increase corporate productivity. Rakesh Ravuri, CTO and SVP – Engineering at Publicis Sapient, speaks with Sudhir Chowdhary on the importance of using this opportunity right now, the ethics of doing so, and how AI will lead to the creation of new types of employment in the future Excerpts.
How significant is corporate digital transformation for organisations? How does generative AI support long-term expansion?
In today’s economic environment, firms must undergo digital transformation. It’s all about moving more quickly and being nimble, which is more important than ever. Innovations in generative AI (GenAI) are the most recent drivers of growth in this domain. With GenAI, enterprises may experiment with fresh processes and move beyond traditional problem-solving, which offers a huge opportunity to accelerate value creation. It gives businesses the ability to test ideas quickly, refine procedures, and find new solutions, enabling continuous development, optimising efficiency, and producing distinctive value propositions with less resources.
Nevertheless, AI or GenAI cannot be the exclusive driver of digital business change. The five essential competencies of strategy, product, engineering, experience and data, as well as AI, must be combined. Businesses will only be able to maximise the growth from their company transformation if they adopt this all-encompassing strategy.
What challenges do emerging technologies for digital transformation face? What advantages may AI have in this regard?
The two primary obstacles are opposition from people who are accustomed to using these older systems and resistance from legacy systems that have not been optimised to integrate easily with modern technology. For many businesses, especially small ones, the cost of deploying new technology is a major roadblock. Another difficulty brought on by new technology is a lack of experience in implementing and managing significant change.
For businesses looking to monetize their data assets and use them for operational optimisation and a competitive edge, AI is a desirable investment despite its challenges. It allows employees to concentrate on more sophisticated and strategic work by intelligently automating monotonous operations. It provides tailored consumer experiences and insightful behavioural data.
Additionally, with the most recent developments in GenAI, it is possible to upskill current staff. a need for specialist hiring by acting as adaptable generalists.
In order to ensure justice and the ethical usage of AI systems, how can businesses successfully address and eradicate AI bias?
Four important considerations must be given top priority when businesses use AI as a revolutionary tool: bias, ethics, governance, and legislation. By identifying unfair tendencies in data and algorithms and reducing them through preprocessing and a variety of viewpoints, bias has to be addressed. By setting detailed rules for justice, openness, accountability, and privacy, ethics may be ingrained. Monitoring inputs, outputs, and utilisation will all be part of managing AI.
Businesses may play a critical role in defending social interests while successfully resolving difficulties brought by AI by combining ethics with established norms.
In the age of rapid technological advancement, one subject has been gradually moving from the realms of sci-fi and academia to the core of countless industries: Artificial Intelligence (AI). Today, in 2023, AI is not merely a niche technology; it’s a catalyst for innovation, growth, and sustainability across all sectors. As a result, it is becoming increasingly necessary for every engineer, irrespective of their field, to acquire some level of AI expertise.
Whether it’s automation, predictive analytics, or decision-making algorithms, AI has cemented its place in modern technologies. With its applications stretching across sectors such as healthcare, energy, transportation, and telecommunications, AI is reshaping the way we operate, optimize, and innovate.
For engineers, this transformation means that AI is no longer just a domain for computer scientists or data analysts. Civil engineers use AI for designing, planning, and optimizing infrastructure. Electrical engineers utilize AI algorithms for energy management and fault detection. Mechanical engineers leverage AI for predictive maintenance and quality control. The line between AI and other engineering disciplines has blurred, necessitating a new skillset for engineers in the 21st century.
The necessity for engineers to have some AI expertise is not simply about understanding the basics of machine learning or data analysis. It is about being competent in the era of the Fourth Industrial Revolution, where physical, digital, and biological worlds are merging.
Aerospace engineers, for example, may need to work with AI algorithms for real-time decision making in autonomous drones. Environmental engineers might need to utilize AI-powered predictive modeling to better manage and conserve resources. Chemical engineers might be tasked with designing AI-enabled processes for optimal production efficiency. As such, engineers who lack AI expertise will find it increasingly challenging to excel in their respective fields.
Furthermore, AI tools and techniques have become vital for problem-solving, innovation, and design in engineering. Engineers are expected to understand AI principles, develop AI models, and implement AI solutions to enhance system performance, product quality, and service delivery.
Recognizing this demand, many universities and technical institutions are incorporating AI courses into their engineering curriculums. By incorporating AI knowledge and skills into their programs, these institutions are preparing future engineers to cope with the dynamic technological landscape. Additionally, online platforms and Massive Open Online Courses (MOOCs) offer engineers a flexible way to gain AI knowledge and stay ahead of the curve.
While discussing AI expertise, it’s crucial not to overlook the ethical implications of AI. Engineers need to understand not only how to develop and use AI but also the potential impacts of their creations. As such, topics like algorithmic bias, data privacy, and transparency should be integral parts of AI education for engineers. This ethical literacy will ensure that AI is used responsibly and equitably, avoiding potential misuse or abuse.
The integration of AI in various sectors has transformed the field of engineering, redefining the skillset required for engineers. As we move further into the AI-driven era, it is no longer sufficient for engineers to be proficient solely in their specific discipline. They need to understand, apply, and navigate AI technologies. By acquiring AI expertise, engineers can ensure they are prepared for the opportunities and challenges that lie ahead in the age of intelligent machines.
Every workplace had a specialised staff for switchboards in the 1950s. In the same way that this employment changed and made room for better alternatives, AI will do the same and open up new chances. A fast search on the internet today reveals that prompt engineering was not a talent a year ago. However, the emphasis right now will be on upskilling, which is not a novel idea for technologists.
Similar to the cloud, every engineer will be required to have some knowledge of AI, including data analysis, AI system administration, AI programming, and the use of AI models and libraries. Now, organisations are even debating the position of a chief AI ethical officer.
In order to improve client relations and operational efficiency, we’ve leveraged AI to develop tailored chatbot solutions for finance. By utilising GenAI, we developed strong intelligent chat interfaces for retail that automates merchandising while facilitating improved consumer engagement and product descriptions. Our customers utilise our AI-powered predictive analytics solutions to make data-driven choices that maximise the effectiveness of their supply chains. Most recently, we debuted PSChat, an internal generative AI tool that uses cutting-edge models to meet the unique requirements of Publicis Sapient.
