Generative AI in Outsourcing and its Impact on Business Models

Artificial intelligence or AI has undoubtedly been ruling the world with technological developments almost every single day with an army of startup companies venturing into new product development and services. The world has been debating on the good and bad of the developments after the public introduction of OpenAI’s ChatGPT to the public. Experts across domains have been reviewing the product/services and providing their points of view. While many are fascinated with the growth and convenience it brings to perform mundane tasks, there are an equal number of experts highlighting the dark side of these innovations and the impact they can have on humans with lightly governed policies around them.

Generative AI and its Current Applications

GenAI is the ability of machines (a.k.a., large language models or LLMs) to generate and contextualize content such as images, audio, videos, text, and codes on their own based on certain human commands. Companies have been using the GenAI services for multiple use cases to have human-like conversations, write codes, and create new content, thereby reducing manual work through this transformational technology.

In the current scenario, companies use generative AI for training their voice assistants, for image generation, video creation, music creation, and text-to-speech conversion. GenAI is already in use and is evolving in few sectors, however there is a growing need for experimentation across other industry segments and use cases. This technology has already proven efficient in various sectors, such as IT, education, media, and entertainment, to provide desired results. It is evolving in new industry segments such as medical, healthcare, law, business financial sectors.

Growing Ecosystem and What Our Clients Say

Based on multiple conversations with more than 25 clients of TechM across industries, including telecom, hi-tech, retail, travel and logistics, and banking and capital markets, our observations are:

• 80% of the clients want to implement AI
• 20% of the clients want to focus on a specific area
• 100% of the clients want to experiment using GenAI
• 100% of the clients wanted to experiment in a controlled manner while also handling security, data protection, and copyright issues

Common Hurdles in Client Experiments with Generative AI

Uncertainty in Demand

• Managing uncertain demand and visibility
  to requirements
• Aligning requirements with product outcomes and use case implementation
• Navigating the nascent development of GenAI to identify new possibilities

Lack of Agile and Flexible Models

• Developing/implementing flexible and innovative working models for execution
• Expanding the spread and improving the ability to provide services across domains and verticals
• Achieving cost-effectiveness and optimizing project delivery

Lack of Resource Scalability

• Acquiring skilled resources with niche and specialized domain knowledge
• Rapidly scaling up resources on short notice and on-demand

Lack of a Proven Innovation Partner

• Ensuring seamless support for end-to-end requirements
• Delivering high-quality project outcomes while fostering a culture of continuous improvement
• Cultivating thought leadership and point of views

Service providers may need to adapt to the changing needs of the business environment and offer innovative, data-driven insights that enable clients to make informed decisions and achieve their unique goals more efficiently.

Strategic Approaches for Thriving in the GenAI Space

The technology sector has been going through a change due to enormous growth in AI. This innovation in technology has been disrupting businesses and this paradigm shift has leveled the ground for organizations to collaborate more often than ever with enhanced processes, tools, and technologies. The current generative AI sector is one such sector that is at a nascent stage of development with the potential to do more and to be leveraged across all industry segments to transform businesses. While clients are going through changes at their end, there is an equal need for service providers to also look at these changes as opportunities to adapt to client requirements.

Collaborative Efforts to Enhance the AI Journey

Diverse talent and expertise across industries can combine to push the AI journey further, through collaboration, shared knowledge, and investments in technology.

• Flexible models and partnerships with niche players to support the development of LLMs across domains across locations/languages can be favorable. Given the applicability of the technology use case across industry sectors and the domain knowledge required to create content, this change is inevitable.
• Investments in AI service offerings to develop and build a skilled workforce for the future are key. AI providers require building a community network for niche domain skill requirements, collaborating with niche institutes to develop custom learning models, establishing a domain center of excellence (CoE) for a new generation of services, and creating a pool of domain consultants.
• The industry needs innovation in tools, technology, and processes to enhance efficiency. We must build smart tools and use the automation technology within existing tools to improve the quality of work at scale. Integrations to improve tool efficiency and handle end-to-end process requirements can also help move swiftly in the AI journey.
• Collaboration with clients to present thought leadership or POVs can eliminate the confusion around the GenAI technology. We have been undertaking pilots for enabling unique use cases and to develop relevant service offerings.

Conclusion

Any new technology that disrupts the market goes through change, this is not new, and businesses have navigated several such cycles in the past. However, when the developments are at this pace with upgrades happening very frequently without policy guidelines, it is imperative that solutions are sustainable. Collaborative efforts between brands, service providers, and vendors, along with a consulting-based approach to building strong processes will enable an ecosystem that can effectively deliver the intended transformation. When technological developments are huge and the implementations are limitless, the knowledge required to address project outcomes will need more expertise to collaborate, build and strengthen the existing processes.