By: Parag Mhaiske, Specialist - Business Analysis

Welcome to an era where AI is reshaping how business interact with customers. Today, AI automation in enterprises is no longer an aspirational goal but a transformative reality impacting all aspects of our lives. With AI breakthroughs like OpenAI’s ChatGPT, Sora, and DALL-E making waves, customer expectations have shifted dramatically. No longer are customers patient with outdated technology. Instead, they expect seamless, real-time, and highly personalized experiences at every touchpoint.

Customers have always valued smarter, faster, and more connected experiences. Nowadays, AI for smart manufacturing and other sectors is driving the expectation of fully automated, real-time interactions across all customer touchpoints. To illustrate the complexity of such automation, consider a customer using a digital service for tasks like booking a flight, ordering a pizza, or checking a bank balance. These services often require multiple confirmations from human counterparts under the simple-looking UI boilerplates, triggering a series of pre-configured decision trees to achieve the desired outcome. Recognizing this need for high-efficiency customer experiences, integrating AI automation in enterprises is the way forward.

AI as the crucial enabler

Business transactions might appear as straightforward information exchanges, but they typically involve several services that previously required human intervention. Traditionally, robotic process automation (RPA) helped streamline these tasks in controlled settings. However, while RPA enhanced efficiency, it came with limitations. AI automation in enterprises, on the other hand, goes beyond basic automation by embedding intelligence into processes, enabling adaptive, goal-driven decision-making.

Think about how far automation has come. Early advancements focused on simplifying user interfaces, reducing the friction between humans and machines. This was followed by the integration of business process management (BPM), which optimized workflows and made systems more adaptable to changing business needs. Then came RPA, which was a game-changer. By mimicking human actions for rule-based tasks, it significantly reduced operational costs and boosted productivity. But RPA had its limitations, it could only handle predefined tasks and was not equipped to deal with unexpected situations.

Enter AI, which can analyze vast amounts of data, learn from it, and make decisions independently. By integrating AI into enterprise applications, we’re moving beyond predefined tasks toward more context-aware, intelligent automation. AI doesn’t just execute; it interprets. It can act on behalf of human counterparts, handling complex, connected transactions in real-time. In essence, AI has the potential to automate intelligence itself, bringing us closer to true autonomy in business operations.

From RPA to Machine Customers

RPA can be seen as the early steps toward a future where machine customers become commonplace. As AI for smart manufacturing and other fields continues to advance, RPA systems are becoming increasingly intelligent, adapting to user needs and making autonomous decisions. This progression could give rise to machine customers, intelligent agents that handle complex transactions on behalf of users, creating a more seamless interaction between human demands and digital solutions.

The potential of AI automation in enterprises is significant. It can process real-time data, learn continuously, and make intricate decisions without human input. This capability allows for the development of intelligent services that don’t just support customers—they act as them. Imagine a machine that interacts with suppliers, manages inventory, or even negotiates contracts. The possibilities are endless, and the implications are profound.

Expanding AI’s Role in Enterprises

The ultimate goal is to embed AI in every aspect of business operations. By doing so, we can create intelligent, real-time customer experiences at every touchpoint. To give you an idea of the scale, by 2025, there will be over 15 billion connected products, each with the potential to act as a machine customerⁱⁱⁱ. The public sector is already seeing the benefits, with cities like Barcelona, Spain[i] using AI and IoT to manage everything from waste disposal to street lighting. This has led to a 30% increase in efficiency in municipal services.

While some governments have embraced AI, businesses often struggle to keep up. The high complexity and lack of standardization of B2B operations are limiting the potential of enterprises to leverage AI. Further, the rise of AI has led to concerns about unexpected misuse. As highlighted in a CNBC article[ii], deepfake scams have looted millions from unsuspecting victims, using AI to create convincing fake videos or audio recordings that mimic real individuals. As AI becomes more integrated into daily life, businesses must ensure their systems are trustworthy, ethical, and transparent.

Machine customers: the next frontier for autonomous transactions

Machine customers are intelligent software or hardware machines, leveraging AI to process various types of information from their environment and perform transactions on behalf of the user. It stands at the intersection of AI innovation and practical application. Machine customers have frequently appeared in various Gartner hype cycles, primarily in the innovation trigger phase. A recent book by Gartner analysts titled “When Machine Becomes Customers” states that many CEOs believe by 2030, up to 20% of their companies’ revenue will come from machine customers ⁱⁱⁱ. Hence, understanding the role and impact of the machine customer is crucial for businesses aiming to thrive in a rapidly changing digital ecosystem.

One example of machine customers in action is Tesla’s self-diagnosing vehicles. These cars can detect issues, automatically order necessary parts for service, and notify the user to schedule a service appointment. Similarly, Amazon’s Dash Replenishment service uses smart shelves in homes or offices to automatically reorder consumable products when their supplies run low. These examples demonstrate the potential of machine customers to transform industries, but they also raise important questions.

Is it feasible to scale machine customers for real-world autonomy? How close are we to achieving this level of sophistication? And most importantly, how can businesses prepare for this future?

Preparing for the future

As we look ahead, it’s clear that the rise of machine customers will reshape how businesses operate. To thrive in this new landscape, companies must take proactive steps to integrate AI into their processes. This means investing in technologies that can support machine customers and ensuring that their AI systems are secure, transparent, and reliable.

Moreover, businesses need to foster collaboration across industries to develop standardized AI solutions that can be easily integrated into different systems. By pooling resources and expertise, companies can share the costs associated with AI infrastructure and accelerate the development of machine customer capabilities.

The future of business interactions is evolving rapidly, and machine customers are at the forefront of this change. While true autonomy may still be a few years away, the foundations are already being laid. Businesses that embrace AI now will be better positioned to lead in this new era of customer interaction.

In the next part of this series, we’ll dive deeper into the questions surrounding machine customers and transactional autonomy. What steps should businesses take to scale these technologies? And what does the road ahead look like for fully autonomous transactions? Stay tuned as we explore the answers and outline the next steps for preparing your organization for the future of machine customers.

[i] Smart City Technologies Beginners Guide, Henri Nyakarundi, ARED Group, May 23, 2024: https://aredgroup.com/smart-city-technologies-beginners-guide/

[ii] Deepfake scams have looted millions experts warn it could get worse, Dylan Butts, CNBC, May 27, 2024: CNBC article

^[iii] Machine Customers Are a Trillion-Dollar Opportunity, Don Scheibenreif, Mark Raskino, Gartner, Inc.:

https://www.gartner.com/en/publications/when-machines-become-customers