The global technology landscape is changing with advances in generative AI (GenAI), multimodal data formats, and edge computing. Moreover, the explosion of data from Internet of Things (IoT) devices, social media, and intelligent enterprise systems is fueling the demand for advanced AI and ML functionalities. But the rise of ethical AI and regulatory scrutiny is influencing how these technologies are developed and deployed, moving from standalone functions to fully integrated AI operations. This shift is pushing technology providers to develop more sophisticated algorithms, AI platforms, transparent models, and innovative services that can help their customers better handle complex IT tasks with minimal human intervention, thereby enhancing enterprise efficiency and productivity.
Amid this revolution, Frost & Sullivan’s recent AI Think Tank delved into the fascinating realm of Disrupting Business Landscapes and Driving New IT Service Opportunities with AI and ML.
Here, the following growth experts collaborated to share their views on transformation drivers, challenges, disruptive technologies, and emerging opportunities in the global AI ecosystem: Rajesh Rajappan, Senior Vice President and Chief Strategy Officer at Hitachi Digital Services, Nishchal Khorana, Growth Expert and Vice President- ICT at Frost & Sullivan, and Kiran Kumar, Growth Expert and Director, Emerging Technologies at Frost & Sullivan.
Gain valuable perspectives from these experts by clicking here to access the recorded session of this Think Tank.
- Implementing AI in enterprises of the future: Enterprise AI is rapidly advancing beyond proof-of-concept, with 89% of IT decision-makers viewing it as vital for efficiency, innovation, and customer experience. However, only 1% of global businesses have achieved ubiquitous levels of AI maturity. This presents significant growth opportunities for providers to streamline and automate AI across functions like sales, administrative support services, IT, risk and compliance, manufacturing, and supply chain management – How will you assess your organization’s AI maturity, and what strategies will you implement to scale AI deployments across various business functions?
- Scaling AI and overcoming challenges: Enterprises are grappling with multiple challenges that hold them back from production-ready AI. Many find measuring the impact and return on investment (ROI) potential of new technologies challenging, while others contend with obstacles like data quality, skill shortages, privacy and security concerns, IT/OT functional silos, as well as the need for standardized, ethical AI frameworks. This implies that AI adoption in the near term will be promising, but slow – Which AI best practices will help providers and customer navigate these growth challenges and accelerate AI adoption in their organizations?
- Capitalizing on early adopter verticals: Regulated industries like banking, insurance, and healthcare are more cautious with AI, but sub-segments like fraud detection and risk analysis are gaining traction due to more explainable outcomes. Meanwhile, verticals like manufacturing, retail and eCommerce, technology and IT services, and healthcare are set to capitalize on early successes in consumer applications like chatbots, virtual assistants, and personalized customer experience (CX) – Has your growth team identified the most lucrative opportunities that will help your company thrive amid the ongoing AI revolution?
- Enterprise GenAI solutions: Businesses are increasingly integrating GenAI and traditional, algorithmic AI, particularly within operational functions. For larger enterprises, the convergence of IT and OT systems, network automation, and predictive maintenance are key drivers of AI innovation. This blend of AI technologies is crucial for optimizing processes, enhancing efficiency, and maintaining a competitive edge in a rapidly evolving ecosystem – What partnership strategies and growth processes will help your organization harness the benefits of GenAI?
- Building a Responsible AI Ecosystem: Responsible AI practices are essential for minimizing the risks of individual and societal harm. Providers must establish guardrails to prevent discriminatory biases, ensure protection against cyberattacks, and maintain transparency within algorithms. This highlights the growing need for more ethical and secure AI systems – Do you have the right guardrails and frameworks in place to protect AI systems from cyberattacks, algorithmic biases, and data breaches?
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“AI has shown evidence that it has the potential to redefine how we operate across every sector moving forward. Now, IT service providers are playing a crucial role in enabling a shift in approach i.e., from using AI as a standalone solution to being fully integrated into all aspects of business operations.” – Kiran Kumar, Growth Expert and Director, Emerging Technologies at Frost & Sullivan.
“AI has shown evidence that it has the potential to redefine how we operate across every sector moving forward. Now, IT service providers are playing a crucial role in enabling a shift in approach i.e., from using AI as a standalone solution to being fully integrated into all aspects of business operations.” – Kiran Kumar, Growth Expert and Director, Emerging Technologies at Frost & Sullivan.
