The Converging Evolutionary Journey of GenAI – Part 1
Implications for Business Strategy
The generative AI (GenAI) space is undergoing rapid change, marked by the increasing convergence of standalone digital AI solutions and GenAI integrated into cloud data platforms. For businesses that are navigating the complexities of taking the initial success of AI PoCs to Production, this coalescence requires a pause and deliberation. This analysis explores recent progress, key alliances, and emerging trends in this evolution, emphasizing strategic considerations for businesses incorporating GenAI into their operations.
Standalone AI or GenAI Digital Solutions
Enhanced Specialization and Expanding Modalities
Standalone AI or GenAI in some cases driven BPM solutions are evolving from general-purpose workflow models to specialized capabilities for specific industries and functions driven by AI. The rise of ML and now smaller language models (SLMs) is a key contributor to this trend, offering reduced computational demands for integration into everyday devices and specialized business applications. These ML/SLMs can be trained more efficiently using targeted datasets, enabling quicker and more cost-effective deployment of customized AI-generated outputs.
The focus is shifting towards refining existing models for specific data sources, improving performance within domains. This specialization signifies a maturing GenAI market, moving towards focused value creation. Domain-specific models address the limitations of general-purpose models by delivering greater accuracy for applications, driven by enterprise demand for practical business results.
Well before foundational models like OpenAI and DeepSeek, solutions for specific business functions driven by BPM platforms or horizontal solutions like Kore.ai, the market is seeing a rise in specialized AI development companies and platforms enabling customized applications. These providers focus on delivering tailored AI capabilities across various industries and use cases. For instance, Hoonartek’s RealizeAI offers an advanced suite of predictive, generative, and conversational AI solutions specifically designed for industries like banking, insurance, and retail, addressing particular business challenges and aiming to accelerate value realization from AI investments. AI capabilities with low-code/no-code tools for building custom AI and GenAI applications, such as Sparkflows, also play a significant role in this ecosystem offering model management and MLOps capabilities.
Implementing a Data Catalog in FSI
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Predictive, Conversational, and Generative AI Solutions for Industries
Implementing a Data Catalog in FSI
GenAI Embedded in Cloud Data Platforms
Integrating AI into the Data Fabric
Cloud data platforms are offering integrated GenAI capabilities to enable AI-driven analytics, automation, and insights which has direct access to large amounts of data stored in the platform. This integration emphasizes scalability, governance, and data context, though it may sometimes lag behind standalone solutions in specialized LLM advancements. This has been addressed by them by offering a model selection capability based on the specific use case.
Databricks has been a leader in this area, incorporating new GenAI features into its offerings with Databricks Mosaic. Recent enhancements include improved AI assistance for chart generation, streamlined dashboard development, and upgraded tools for data exploration. These integrations aim to empower users with AI-driven tools within their workflows, facilitating wider GenAI adoption for data-driven decision-making.
Snowflake has also advanced in embedding GenAI within its data cloud through Snowflake Cortex. Recent functionalities include AI Observability, Cortex COMPLETE Structured Outputs, and enhanced semantic models for Cortex Analyst. Snowflake focuses on providing managed AI services within its data cloud, simplifying AI applications for a broader user base.
Microsoft Fabric also exemplifies deep AI integration within a cloud data platform. Recent updates emphasize “agentic AI” through integration with Azure AI Foundry, enabling secure grounding of AI agent outputs with enterprise knowledge. Copilot and other AI features are being made available across paid SKUs. Microsoft’s strategy is to create a unified data and AI platform with AI capabilities integrated across the data lifecycle, empowering users with intelligent tools.
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The Convergence Catalysts
Partnerships and Embedded Innovations
The convergence of standalone platforms with AI and embedded GenAI solutions is driven by strategic partnerships between AI vendors and cloud/data platform providers, along with the increasing development of foundational models by data platforms. This is also driven by the gravity of data problem – where data is becoming too large to move.
- The partnership between OpenAI and Microsoft illustrates this trend, offering benefits for developing data-aware models.
- Azure OpenAI Service provides a secure environment for deploying advanced AI models, with enterprise-grade security and scalability.
- The expanded partnership between Snowflake and Microsoft integrates Azure OpenAI Service with Snowflake Cortex AI, providing direct access to OpenAI’s models within Snowflake’s environment.
- The collaboration between AWS and Anthropic is another significant catalyst. AWS is partnering with Anthropic on “Project Rainier” to build a large AI compute cluster.
These partnerships combine specialized AI model development expertise with the data infrastructure and scalability of cloud platforms, accelerating innovation and broadening AI adoption.
Data platforms are also developing their own foundational models.
- Databricks’ DBRX, a large language model, has demonstrated strong performance.
- Snowflake Cortex includes Snowflake Arctic, alongside access to other LLMs.
Developing in-house models offers enterprises more integrated AI options, potentially reducing reliance on external APIs and improving data locality, security, and latency. These platforms can offer performance advantages, simplified data governance, along with potentially lower costs.
Concluding Thoughts
Hybrid GenAI Architectures – Combining Strengths
Hybrid GenAI architectures combine the agility of standalone models on premise with the governance and data context of cloud data platforms. This approach allows enterprises to tailor AI strategies, balancing flexibility and control.
Ultimately, the choice of architecture—whether standalone, cloud-embedded, or a hybrid—hinges on an organization’s specific strategic goals, existing data infrastructure, and regulatory requirements. As GenAI continues its evolutionary journey, organizations will increasingly refine their strategies to navigate complex enterprise decision-making, emphasizing interoperability through open frameworks and unified tooling.
This strategic imperative aims to leverage GenAI convergence for enterprise scalability and competitive advantage, while simultaneously prioritizing the responsible deployment of these powerful technologies through robust data security and compliance measures. The next part of this series will delve deeper into these critical considerations for successfully harnessing GenAI in the enterprise.
Authors
Peeyoosh Pandey
Peeyoosh is a passionate business leader with 25+ years of industry experience and a proven track record of building businesses for scale. He is a veteran of the IT services industry. Peeyoosh thrives on building deep executive relationships and long-standing customer engagements and excels at managing stakeholders across BFSI, Healthcare and ISV with a focus on Digital Transformation, Cloud, Security & CRM solutions. Connect with him here.
Aijaz Ansari
Aijaz is the VP of Global Marketing at Hoonartek. As a lifelong learner, he takes a keen interest in hearing about technology transformation journeys that lead to generating significant value for businesses. Aijaz is a strong proponent of using data and AI to aid with decision-making in marketing and other functions. When not at his battle station, he spends time on Xbox with his sons and watching football (soccer). Connect with him here.
