Artificial intelligence or AI is undoubtedly dominating the world with technological developments almost every day with an army of startups venturing into developing new products and services. The world has been debating the good and bad developments after OpenAI’s ChatGPT was publicly introduced to the public. Experts from all fields reviewed the products/services and expressed their views. While many are fascinated by the growth and convenience they bring to performing mundane tasks, an equal number of experts point out the dark side of these innovations and the impact they can have on humans with lightly implemented policies. governed.
Generative AI and its current applications
GenAI is the ability of machines (i.e. 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. Enterprises use GenAI services for multiple use cases to have human-like conversations, write codes, and create new content, reducing manual work with this transformational technology.
In the current scenario, companies are using generative AI to train their voice assistants, for image generation, video creation, music creation and text-to-speech conversion. GenAI is already in use and evolving in a few industries, but there is a growing need for experimentation in other industry segments and use cases. This technology has already proven effective in various industries, such as IT, education, media and entertainment, to deliver the desired results. It is expanding into new industry segments such as the medical, healthcare, legal and corporate finance sectors.
Growing ecosystem and what our customers are saying
Based on multiple conversations with over 25 TechM clients across industries including telecommunications, high-tech, retail, travel and logistics, and banking and capital markets, our observations are the following :
- 80% of customers want to implement AI
- 20% of customers want to focus on a specific area
- 100% of customers want to experience GenAI
- 100% of customers wanted to experiment in a controlled way while also managing security, data protection and copyright issues.
Common Obstacles in Customer Experiences with Generative AI
Uncertainty in demand
- Managing uncertain demand and visibility
according to the requirements - Align requirements with product outcomes and use case implementation
- Navigating the nascent development of GenAI to identify new possibilities
Lack of agile and flexible models
- Develop/implement flexible and innovative working models for execution
- Expand reach and improve capacity to deliver services across all domains and verticals
- Achieve profitability and optimize project delivery
Lack of resource scalability
- Acquire qualified resources with niche and specialized domain knowledge
- Rapidly increase resources on short notice and on demand
Lack of a proven innovation partner
- Ensure seamless end-to-end requirements support
- Deliver high-quality project results while fostering a culture of continuous improvement
- Cultivate thought leadership and perspectives
Service providers may need to adapt to the changing needs of the business environment and offer innovative data-driven insights that enable customers to make informed decisions and achieve their unique goals more effectively.
Strategic Approaches to Thriving in the GenAI Space
The technology sector is experiencing a change due to the enormous growth of AI. This technological innovation has disrupted businesses and this paradigm shift has allowed organizations to collaborate more often than ever with improved 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 be leveraged across industry segments to transform businesses. As customers experience changes on their end, service providers must also view these changes as opportunities to adapt to customer requirements.
Collaborative efforts to improve the AI journey
Diverse talent and expertise across sectors can combine to push the AI journey further, through collaboration, knowledge sharing and investment in technology.
- Flexible models and partnerships with niche players to support the development of LLM in all fields, in all locations/languages, can be favorable. Considering the applicability of the technology’s 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 essential. AI vendors should create a community network to address skill needs in niche domains, collaborate with niche institutes to develop personalized learning models, establish a domain center of excellence (CoE) for new generation of services and create a pool of domain consultants.
- The industry needs innovation in tools, technology and processes to improve efficiency. We need to build smart tools and use automation technology within existing tools to improve work quality at scale. Integrations to improve tool efficiencies and manage end-to-end process requirements can also help move forward quickly on the AI journey.
- Collaborating with clients to present thought leadership or POVs can eliminate confusion around GenAI technology. We have undertaken pilot projects to enable unique use cases and develop relevant service offerings.
Conclusion
Any new technology that disrupts the market undergoes changes, this is not new and companies have gone through several such cycles in the past. However, with developments happening at this pace and upgrades happening very frequently without policy guidelines, it is imperative that solutions are sustainable. Collaborative efforts between brands, service providers, and suppliers, along with a consultancy-based approach to creating robust processes, will help create an ecosystem that can effectively achieve the desired transformation. When technology developments are huge and implementations are unlimited, the knowledge required to meet project outcomes will require more expertise to collaborate, build and strengthen existing processes.
About the Author
Mothiraj Ramalingam
Managing Director, Digital Business Operations, Tech Mahindra Business Process Services
Mothiraj has over 20 years of experience managing clients across various service lines. His expertise includes setting up delivery operations, establishing centers of excellence, designing solutions and leading best practices. In his current role, he leads the Digital Data Services practice and collaborates with internal and external stakeholders, among others. clients from different industrial sectors to provide our AI/ML data service solutions.
