AI platforms

Trend 18: AI democratization unlocks business potential

Over two-fifths of APAC firms have implemented generative AI without realizing business value, compared with just three-tenths in North America. Challenges in talent, expertise, poor data, lacking AI strategy contribute much. AI democratization maximizes business value when the whole firm has access to AI tools, scripts, and frameworks. Low code, no code (LCNC) platforms, with visual interfaces, empower nontechnical users to build and deploy applications without complex coding.

Key AI platforms propel advancements in enterprise adoption. LCNC tools with intuitive visual interfaces democratize AI, fueling market growth from $17.7 billion in 2021 to a projected $125 billion by 2027. Prebuilt models for tasks like image recognition and automated workflows streamline implementation and reduce technical barriers. Cloud-based infrastructure enhances affordability and scalability, eliminating the need for costly hardware investments. Collaborative features facilitate secure cross-functional teamwork, fostering innovation. Additionally, explainable AI tools promote transparency, build trust, and empower users to make informed decisions based on AI insights.

A global firm partnered with Infosys to develop an LCNC solution tailored for its oil segment, automating more than 200 processes to streamline IT operations. For another client, Infosys built over 300 processes in just 10 months through Infosys IP and FastApp.

AI platforms

Trend 19: Enterprise-level perspective for generative AI

Generative AI requires a platform-based approach for enterprise-scale deployment, utilizing agile techniques to abstract engineering complexity. The platform should embody a forward-looking approach with a layered architecture, data readiness, and in-flow AI; embrace democratic elements such as unified visibility, self-service, and crowd-sourcing; and demonstrate scalability through cloud-native design, rapid adoption, and self-governance features.

The platform should capture evolving business needs, embracing responsible by design principles, with safety, bias, security, explainability, and privacy throughout the AI life cycle. This builds trust, ensures regulatory compliance, and addresses legal considerations. A poly AI approach ensures various tooling and processes are transparent, measured, and monitored homogenously across multiple hyperscalers. In a layered architecture strategy, each layer functions as an independent application with distinct user personas, interfaces, technology, services, and deployment.

A major US telecom company tackled fragmented AI development by implementing an enterprise-wide self-service AI platform. This platform fosters collaboration among data scientists, engineers, business analysts, and others, breaking down silos and enabling the development of cross-functional AI solutions across sales, customer service, and finance functions.