In the realm of disruptive technologies, artificial intelligence (AI) has emerged as a transformative force with the potential to reshape industries of the future, revolutionize manufacturing processes, automate workflows, personalize service portfolios, enhance customer experience (CX), and augment conventional business processes. This is intensifying the pressure on businesses to accelerate digital transformation, with a renewed emphasis on unlocking data’s potential and leveraging AI to spark innovation. Moreover, the following forces are spurring transformative change in the global AI landscape:
- By leveraging neural networks and deep learning algorithms, generative AI (GenAI) is maximizing the potential of machine learning (ML) technologies, thereby simplifying content, image, code, and video creation.
- Exponential growth in data generation from digital infrastructure is necessitating multimodal foundational models that enable integrated analysis across different data inputs (image, text, and speech).
- Conversational AI is driving greater employee productivity by integrating multiple technologies, such as natural language processing (NLP), speech-to-text, image recognition, and sentiment analytics to generate human language with more contextual relevance.
- Maturing machine learning (ML) and deep learning capabilities are pushing software vendors to deliver AI-first applications through innovative, ‘as-a-service’ business models, customized for different functional/ industry-specific applications.
- The need for responsible AI is pushing regulators, industry bodies, and enterprises to implement best practices that ensure fairness in outcomes, minimize bias, build design transparency, protect data privacy, and mitigate cyber risks.
Now, the move to large-scale and sustainable AI adoption is intensifying the pressure on technology and service providers to deliver holistic strategies and ethical implementation frameworks that allow businesses to seamlessly scale AI deployments across various functional areas.
To empower business leaders with actionable intelligence that maximizes innovation, Frost & Sullivan has launched a series of Think Tanks on AI and data analytics. These bring together cross-functional experts to identify growth opportunities, address strategic imperatives, and implement best practices, while unlocking future-proof competitive strategies.
Which growth opportunities will help your teams build recurring revenue pipelines in AI?
- Tapping Into the Power of GenAI: Today, GenAI applications are commanding considerable attention across both enterprise and consumer domains. This transformative technology represents a pivotal shift in AI, bringing with it the promise of effective knowledge management, analyzing massive datasets, creating new content, and providing conversational support. Now, to maximize the potential of GenAI, organizations are being pushed to foster out-of-the-box thinking and experimentation, keeping in mind the accuracy of foundational models, intellectual property (IP) guardrails, and change-management best practices.
- Capitalizing on AI Implementation Platforms: The emergence of AI and ML implementation platforms is enabling enterprises to accelerate their AI adoption journey, facilitating end-to-end ML life cycle development, from data ingestion to the visualization of business insights. Now, enterprises can either build their own ML platforms in-house for great flexibility and control or outsource AI implementation to platform vendors through strategic partnerships that support their growth objectives.
To delve deeper into these opportunities, explore the companies that have embraced them successfully, and to hear from experts in the field, click here.
In conclusion, as AI continues to upend conventional business practices, investing in the right technologies holds immense promise for customers. Further, as organizations grapple to confront growth barriers such as intricate technology/device integrations, the lack of data readiness, the limitations of legacy infrastructure, and data disparity, embracing strategic partnerships can help them successfully capitalize on emerging growth opportunities in AI.
