In an era where operational excellence and progressive innovation have become fundamental to enterprise success, harnessing new-age capabilities to revolutionize business models has become an imperative.

For hyperconnected value ecosystems spanning phygital (physical and digital) domains, artificial intelligence (AI) supports the value creation of new products, services and experiences that enhance customer satisfaction, optimize resource utilization and drive innovation. It helps businesses reinvent themselves and create a cognitive ecosystem of connected experiences for resilient operations in the long run.

The last two decades have witnessed rapid adoption of the Internet of Things (IoT), advanced analytics, cloud and edge computing in revolutionizing productivity, automating workflows and integrating ecosystems. However, business resilience is far from a reality. Security challenges, privacy concerns, interoperability issues and high costs continue to prevail across the value chain, posing several challenges to business growth.

AI helps overcome some of these challenges and enhances the possibility of reimagining business models by accelerating permeable innovation and driving hyper-automation and experienced leadership for a sustained competitive edge.

Paving the way for renewed industry propositions

The rapid growth of AI ushers in a wide range of possibilities for enterprises looking to build efficient, secure and targeted applications that are not only practical but differentiated too. Placing emphasis on productivity and revenue generation across various industry use cases, this article will highlight the interdependent relationship that exists between improved operational efficiencies and these technologies, as well as how these could potentially be embedded into business operations.

As an example, the growth of intelligent supply chain management paves the way for generative AI (GenAI) to streamline inventory management, organize logistics routes and leverage near-accurate demand forecasting. How these use cases play out largely depends on the organization; still, on a higher level, these could help facilitate improved supplier performance, contingency planning and of course, real-time management of supplier relationships.

Similarly, IoT-centric cognitive supply chain scenarios need AI/ML model creation, which needs data for simulation. In the event of data unavailability, GenAI can help build synthetic data for model creation. This can also be refined with multiple iterations of data science workflows and data versioning, which is an integral part of Machine Learning Operations (MLOps).

In the manufacturing industry, the enablement of predictive maintenance will help organizations forecast and minimize machine downtime, accelerate quality control and ensure early interventions across the entire manufacturing lifecycle. With remote and continuous access to production processes, equipment and inventory, organizations can strengthen operations, reduce equipment downtime and improve service quality at scale.

As robotics converge with autonomous agents, embodied AI will make robotics more acceptable to humans and the workspace. The urge for humans to converse with robotics is a reality, and natural language will make it more acceptable. Tasks provided by humans to the robots like "find an item for me” or “suggest the next best alternative” or “tell me how much battery is left?” will become a reality.

One of the ways that GenAI and IoT can create value is by embedding data-driven intelligence into energy consumption and sustainability practices. Take, for example, insights into the energy consumption patterns of a shop floor. With GenAI, you could use these to create optimized and energy-efficient solutions, helping promote organizational sustainability and cost savings. It will suggest emission factors, a path toward net zero transformation while enabling the best fit alternative to reduce cost.

From a productivity standpoint, GenAI opens up avenues ranging from product personalization to predictive intelligence across multiple applications and use cases. One of the more exciting ways the Artificial Intelligence of Things (AIoT) is revolutionizing productivity in the warehouse and factory floor is through collaborative robotics (cobots) that largely streamline and automate manual labor tasks like picking, packing and shipping, thus paving the way for increased productivity and reduced human error.

Next, there is access to untapped data through which organizations can shorten the product-development life cycle, introduce new features and offer increasingly personalized solutions. For enterprises struggling to interpret complex volumes of IoT data, GenAI helps streamline data for analysis and prompts faster decision-making.

AIoT and GenAI: Shaping the future of industry

AIoT is also reshaping the automotive industry with advancements in autonomous driving, using real-time vehicle information from IoT sensors and deep AI to enable safe and independent navigation. This simplifies traffic management too, as it allows for dynamic signal control and route optimization, leading to reduced congestion on the roads. And finally, as we look to the near future, we could witness the rapid growth of shared mobility services and enhanced connectivity, smoothly integrating vehicles with the smart city infrastructure.

Another area that is mainly going to benefit from AIoT is the energy and utilities industry, which is undergoing rapid transformation as more enterprises adopt smart grid optimization practices and Distributed Energy Resource (DER) management. In this context, AIoT facilitates smooth energy distribution, grid stability and peak demand prediction, allowing for balanced supply and demand forecasting and maximization of renewable energy usage and contributing to a secure, resilient and sustainable energy infrastructure.

According to a report from McKinsey, AI could boost the life sciences and healthcare sector to up to $410 billion annually by 2025, with huge potential to improve health outcomes, access and affordability. AIoT is creating breakthroughs with new avenues for personalized medicine plans facilitated by collecting and analyzing genomic, molecular and clinical data from patients, which is especially powerful because of the potential that it holds for early disease prediction and optimized drug therapy based on individual patient profiles.

Predictive customer engagement models are also creating a huge opportunity for aftermarket services, as they enable a new approach to offer product improvements, tailored solutions and superior field service operations. Much of this would involve capturing data with IoT technologies and leveraging advanced AI capabilities toward monitoring and improving the performance of products, strengthening maintenance services, customizing customer service outreach and eventually building brand trust.

Embedded with natural language and speech, hands-free dashboards with natural language will be the new norm with GenAI. On a similar note, with seamless dialogue made possible with GenAI, virtual assistants are opening up avenues for sophisticated customer service and meaningful assistance with user queries, enhancing the overall client experience at scale. It must be noted that such automation is vital in conserving the bandwidth of trained professionals to help solve other complex problems.

And finally, on a grassroot level, the existence of Large Language Models (LLMs) can generate and combine software code much quicker than manual data inputs. These versatile capabilities will serve as valuable aids for both seasoned professionals and aspiring developers in the creation of innovative applications. It is important to note that although GenAI has made its way into Integrated Development Environments (IDEs), it probably won't replace developers soon. Instead, it should be perceived as an additional tool, just like no-code/low-code software that has recently become popular in the coding and development space. It will also accelerate the adoption of GenAI in the space of software documentation, test case writing and specification creation.

The above-listed value drivers are not only complementary to one another but also fosters an era of innovation across industries, smoothly resolving challenges surrounding data availability, personalization of services and process automation in the wake of a new era of transformation.

Looking into the future

As the emergence of new tech continues to transform the fabric of operational excellence across the business environment, enterprises must tap into a thoughtful and well-defined approach to leveraging this to incorporate innovation.

With Industry NeXT, businesses stand the opportunity to create unique value by building a cognitive ecosystem that connects products, services, operations and stakeholders.

This is supported by the principle of accelerating permeable innovation, allowing businesses to access and utilize the innovation happening in the external ecosystem, like AI, to enhance their value proposition, enhance possibilities for hyper-automation and build improved leadership decision-making.

The conventional playbook of successful tech transformation will continue to remain relevant, and organizations must arrive at novel approaches while ensuring their deployments happen strategically and in the most responsible way possible.

The journey to innovating with AI has just begun, and those at the forefront of the transformation will gain a competitive edge in realizing new business models and creating massive value.