The Future of Business Processes: Three Functions GenAI Will Transform
Genative AI (GenAI) is leading a large-scale evolution of business processes across industries. The rapid development of use cases for the technology makes it critical that organizations invest now or risk being left behind. A recent report shows that 82% of organizations plan to introduce AI agents across their businesses in the next three years. It’s easy to see why. AI agents, and GenAI in particular, have enormous potential to accelerate digital transformation – from improving operational efficiency to helping multi-channel contact center agents deliver experiences that delight and delight customers.
Executive Vice President and Global Head of Business Process Services at Hexaware.
The race towards an AI-driven future
As technology evolves, organizations are under increasing pressure to demonstrate to customers and investors that they are at the forefront of innovation. They want to embrace GenAI to create new value by improving traditional business processes with new efficiencies and driving better customer experiences. However, there is a risk that people will only invest in technology if they do not start their adoption strategies with a clear goal in mind. This is especially true when it comes to GenAI. Two years after ChatGPT’s rise, we’ve seen countless use cases explored and developed. The time for experimenting is over.
By 2025, organizations should focus on proven, value-driven applications of GenAI, aligned with clear business objectives. Without this more targeted approach, they will only scratch the surface of the benefits the technology can provide. With the clock ticking, organizations must quickly determine which use cases to focus on and how to integrate them into their operations to create value. Here are three of the most impactful GenAI use cases that have already been proven to take business processes to a whole new level:
1. Improved central business functions
GenAI shouldn’t be about reinventing the wheel. As a first step, organizations should focus on improving the core business functions they already perform well. Initial use cases should be tailored to streamline key processes such as document processing and supply chain management. These use cases will be more effective with targeted AI models trained on targeted datasets that provide the rich context needed to automate specific functions with precision.
As a result, organizations will increasingly embrace small language models (SLMs) by 2025. These approaches are more cost-effective, easier to customize, and have fewer parameters than large language models (LLMs), making them better suited for targeted business functions. In fact, 56% of organizations plan to adopt SLMs within the next three years, demonstrating the central role they will play in future GenAI strategies. As they continue to build SLMs for new use cases, organizations will be able to drive further AI solutions to streamline additional business functions.
2. Improved CX and quality of life
Customer experience (CX) is another function that organizations can take a step further with GenAI. Most importantly, GenAI assistants can make life easier for service agents and help them achieve better results for the business, especially for those in CX roles. For example, contact center teams must search multiple systems for the information they need to handle customer complaints or reschedule during a support call. This process takes time, causing the customer to wait longer and worsen their experience, especially if they are forced to recount previous conversations with other team members.
With a GenAI assistant, time-consuming tasks related to document processing and information collection can be automated. This allows agents to deliver experiences that go far beyond what callers expect, allowing organizations to surprise and delight their customers. It also allows service agents to focus on higher-value tasks such as building relationships with customers, increasing job satisfaction and alleviating burnout. As these capabilities mature, using a mix of humans and GenAI agents will revolutionize the customer experience through predictive analytics and process automation, allowing organizations to remain agile and gain a more decisive competitive advantage.
3. Advanced communication skills
Finally, GenAI has real potential to reduce language barriers and enable service agents to support customers from anywhere. A recent report shows that AI can deliver a 90% reduction in document translation time, resulting in efficiency savings of up to €2.79 million. GenAI can use a similar capability to detect language and automate responses to common customer questions via web chat, email, social media and even telephone. In many cases, this eliminates the need for customers to speak to service agents. This not only improves customer satisfaction by speeding up resolution time, but also expands the organization’s reach by going beyond traditional communication channels.
Human agents can also use AI-powered voice assistants to translate conversations during live service interactions, allowing them to talk to customers without being fluent in their language. This means that by 2025, human empathy and experience in dealing with complex customer challenges will be the skills that matter most to service agents, not the number of languages they speak. As their use cases for GenAI mature in this area, organizations will take the customer experience to the next level by streamlining workflows and dramatically reducing case resolution times.
Generating value with AI
GenAI has enormous potential to transform business processes, but organizations urgently need to move beyond the experimentation phase to seize this opportunity. As more tangible use cases emerge, organizations must have clear, customer-focused objectives and a well-defined roadmap to ensure they integrate these technologies in a way that drives value. With this considered approach, they will be much better positioned to reap the benefits of GenAI and deliver market-leading innovation.
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