Umesh Sachdev is CEO & co-founder of Uniphore, a large AI-native, multimodal enterprise-class SaaS company. He’s based in Palo Alto, CA.
In today’s rapidly evolving digital landscape, the role of artificial intelligence (AI) in reshaping enterprise tech stacks cannot be overstated. As businesses strive for competitive advantage, they increasingly recognize the transformative potential of integrating AI across every layer of their operations.
Enterprise organizations are not merely seeking point solutions; they are pursuing a comprehensive architectural overhaul that places AI at the core of their strategies. This is where the concept of enterprise AI comes into play—a paradigm that transcends isolated AI applications to offer a holistic platform for leveraging data, building models and deploying intelligent solutions.
This transformation is happening in real time. As I’ve traveled around the world meeting with CEOs and CIOs of some of the largest companies, the conversations around AI have evolved from understanding the basics of the technology to understanding how they can maximize the value of the technology for their organization. That means reimagining the tech stack.
I’ll explore the vision of transforming the enterprise tech stack into an AI-driven powerhouse, offering unparalleled choice, innovation and sovereignty to organizations worldwide.
Data Empowerment
At the foundation of this reimagined tech stack is data. An organization’s data lake is where all data is gathered—no matter where it’s from or in what form (text data, multimodal data, call center data, etc.)—before it’s prepared for AI models. Knowing where your data is, its quality, its lineage and the ability to capture it is the lifeblood of the modern AI stack.
Knowledge Retrieval
However, data alone holds limited value without the ability to extract meaningful insights and knowledge. From data ingestion and cleansing to predictive analytics and pattern recognition, AI can streamline the entire data life cycle—empowering organizations to make data-driven decisions with confidence. I refer to this as the Knowledge Lake.
This layer of the AI-powered architecture reveals one of the biggest challenges for CIOs today—how to leverage the power of their data while making sure it is usable for the AI models they choose to build. This is the part of the AI stack that is quickly evolving to meet the CIO’s goals of knowledge retrieval, compliance, future regulation and AI sovereignty or control over the output of their AI models.
Building Models That Deliver Choice
The Knowledge Lake feeds into AI-powered models that empower enterprises to develop predictive models and algorithms tailored to their unique business needs.
It’s my view that enterprises should have a choice when it comes to model building and should consider multiple large language models (LLMs) in various sizes based on the use cases and opportunities they are looking to address. They can build their own pretrained models, look to implement guardrails from the Knowledge Lake to ensure proper responses, use proprietary models, or work with models from AI-native companies that have the depth of experience needed to minimize hallucinations and maximize actionable data insights.
Applications As A Service
Moving beyond data and models, the framework of an AI-powered architecture emphasizes the delivery of applications as a service. Here, enterprises leverage AI-driven applications to streamline business processes, enhance customer experiences and drive operational efficiency.
From intelligent chatbots and virtual assistants to agent assistance capabilities to predictive maintenance and fraud detection systems, AI-powered applications can unlock new realms of possibility across diverse industries. By offering applications as a service, organizations can leverage prebuilt AI solutions or customize their own, accelerating time-to-market and maximizing ROI.
I often get asked, “Once I set up my AI platform, how often will I need to retrain my models?” The answer is never—if every application connects back to the AI model with reinforcement learning with human feedback (RLHF). By opening up the application to RLHF, the feedback gathered by each user creates a constant loop of learning for the model. One CEO I was working with described the power of RLHF this way: The AI model starts out as an adolescent, and RLHF allows it to become an adult.
The Vision: AI Across The Front Office
Finally, the ultimate goal of an AI-powered architecture is to integrate AI seamlessly across the front office, serving as a copilot and augmenting human capabilities. By embedding AI into everyday workflows and interactions, enterprises can enhance productivity, automate repetitive tasks, unlock new opportunities for growth and drive efficiency. Whether it’s automating sales forecasting, personalizing marketing campaigns or optimizing supply chain operations, AI serves as a strategic enabler that empowers organizations to thrive in the digital age.
Pioneering The Future Of Enterprise AI
The reimagining of the enterprise tech stack with AI running through each layer represents a paradigm shift in how organizations leverage technology to drive value and innovation. Organizations can harness the full potential of AI to transform their operations, deliver unparalleled customer experiences and secure a competitive edge in an increasingly digital world.
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