The proliferation of artificial intelligence (AI) and machine learning (ML) is driving the demand for innovative technology solutions across various industries. Further, advancements in cognitive capabilities and sentiment analytics are expanding the application space for AI, enabling machines to better grasp context, tackle complex tasks, and deliver more intuitive interactions. But keeping track of the latest solutions and services across the evolving AI and ML ecosystem is becoming increasingly difficult
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Furthermore, complexities associated with AI that stem from technical difficulty, ethical concerns, and evolving regulatory environments make growth in this space challenging. As a result, mitigating the impact of the following barriers is crucial for the adoption and effective implementation of AI technologies:
- Identifying AI applications and use cases that provide a faster return on investment (ROI): Measuring the impact of AI can be difficult because its value often lies in intangible areas like efficiency gains, improved decision-making, or enhanced customer experience.
- Businesses rely on legacy systems that were not originally designed to integrate with AI solutions. Consequently, incorporating AI into these systems requires significant re-engineering, high upfront infrastructure costs, and frequent maintenance.
- Data quality and quantity: High-quality data is crucial for training effective AI models. Yet, collecting, cleaning, standardizing, and labeling data can be time-consuming and expensive for providers.
Which Growth Opportunities Is the AI Revolution Unleashing?
To guide business leaders with actionable intelligence that maximizes innovation amid this transformation, Frost & Sullivan has launched a series of Think Tanks on AI and Data Analytics. These bring together cross-functional experts to identify new opportunities, navigate growth challenges, and implement disruptive technologies, while unlocking robust strategies for competitive differentiation. Here are some promising opportunities our recent Think Tanks have uncovered:
- AI-Powered Data Readiness and Management: As data architectures evolve to become more decentralized, co-existing in edge, cloud, and on-premises environments, the demand for robust data management strategies, comprehensive advisory services, and data readiness frameworks is intensifying.
- Small Language Models (SLMs): SLMs are opening up lucrative opportunities for providers in applications like optical character recognition, language translation, code generation, chatbots, customer service, virtual agents, and sentiment analysis.
- Multimodal Foundational Models: Advancements in voice, video, text AI capabilities, and GenAI are creating a strong growth foundation for multimodal AI, enabling enable organizations to process and derive value from data, resulting in smarter AI solutions that learn rapidly and handle a range of tasks.
To explore strategic imperatives, growth opportunities, and best practices in the AI ecosystem, click here.
In conclusion, businesses that navigate AI’s complexities, such as legacy system integration and data quality, will unlock significant growth opportunities. By actively exploring emerging technologies, implementing new business models, and upgrading infrastructure backbones, organizations can lay the foundation for sustainable, AI-powered growth.
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