Enterprise AI is moving from a software feature to a broader corporate strategy, and this week’s developments on the New York Stock Exchange Trading Floor highlight three parts of that transition: AI-powered work delegation, workforce intelligence and the infrastructure required to run increasingly demanding models.
The enterprise AI market is entering a phase where the conversation is shifting from what AI can do to how deeply companies can integrate it into everyday work.
That shift was visible in the New York Stock Exchange’s August 26 pre-market update, which put enterprise AI company Glean, workforce technology provider Lattice and semiconductor giant Nvidia at the center of the day’s technology discussion.
Glean is using its annual conference to frame enterprise AI as a defining moment for businesses adopting the technology. Founder and CEO Arvind Jain is scheduled to appear on NYSE Live to discuss the challenges facing organizations as AI moves deeper into enterprise workflows.
The company is also preparing to introduce a new offering designed to provide users with the context required to delegate more complex work.
That direction reflects one of the most important trends in enterprise AI: the move from conversational assistants toward AI agents and delegated workflows.
Traditional workplace AI largely helps employees retrieve information, draft content or summarize documents. Agentic systems aim to go further, using context from enterprise data and connected applications to complete multi-step tasks.
The distinction is important because enterprise delegation depends on more than model intelligence. An AI system needs access to relevant company information, appropriate permissions, reliable integrations and safeguards around what it is allowed to do.
Glean has built its enterprise AI proposition around connecting workplace knowledge with applications and organizational context. Its latest product push suggests the company sees an opportunity to move from helping employees find answers toward helping them execute work.
That puts Glean into an increasingly competitive market that includes Microsoft Copilot, Google Gemini for Workspace and Salesforce’s Agentforce, among other enterprise AI platforms.
The competitive battle is increasingly about context.
A general-purpose chatbot can generate an answer. An enterprise agent needs to understand the organization’s internal systems, policies, documents, people and workflows before taking action.
Lattice Takes AI Into Workforce Intelligence
The second enterprise AI development highlighted by the NYSE involves Lattice’s acquisition of Pando.
Lattice, a human-resources technology company focused on people management and performance, is acquiring Pando as it expands its AI-native workforce intelligence capabilities.
The deal brings Pando founder and CEO Barbra Gago into Lattice as chief marketing and strategy officer. Lattice CEO Sarah Franklin is also scheduled to discuss the transaction on NYSE Live.
The acquisition points to another emerging application for enterprise AI: turning workforce data into operational intelligence.
HR platforms have historically collected large amounts of information about employees, goals, performance, skills and organizational structures. AI creates the possibility of connecting those datasets and using them to identify patterns or support decisions that previously required substantial manual analysis.
For enterprise buyers, however, workforce AI comes with additional considerations. Employee data is sensitive, and automated recommendations involving performance, promotion or talent management can raise questions around privacy, transparency and bias.
Lattice’s acquisition therefore illustrates how AI adoption is expanding beyond IT departments. Human resources, finance, sales and operations are becoming active battlegrounds for enterprise AI platforms.
Nvidia Remains the Infrastructure Bellwether
While Glean and Lattice represent the application layer, Nvidia remains central to the infrastructure side of the AI economy.
Investors were awaiting Nvidia’s quarterly results after the market close, with analysts cited by the NYSE expecting earnings per share of approximately $2.09 and revenue above $92 billion for the quarter.
Those expectations underline the scale of investment still flowing into AI computing infrastructure.
Nvidia’s GPUs have become foundational to training and running many of today’s leading AI models. Its position extends beyond chips into networking, software and complete AI infrastructure systems, making the company’s financial performance an important indicator for the broader AI supply chain.
The market is therefore watching two connected stories.
At the application layer, companies such as Glean and Lattice are trying to make AI useful inside specific enterprise workflows. At the infrastructure layer, Nvidia and its competitors are racing to provide the computing capacity required to support those workloads.
The two markets are increasingly dependent on each other.
More capable AI agents require more context, more tools and often more inference. That can increase the demand for compute even as enterprises become more focused on controlling AI costs and measuring productivity gains.
What It Means for Enterprise Technology Teams
For CIOs and technology leaders, the developments offer a useful snapshot of where enterprise AI is heading.
First, AI is becoming increasingly workflow-oriented. The next generation of enterprise products is less concerned with simply generating text and more focused on completing tasks.
Second, context is becoming a competitive asset. AI systems that can securely understand an organization’s proprietary data may deliver more value than generic models alone.
Third, enterprise AI is spreading across organizational functions. Workforce intelligence, customer operations, finance, software development and knowledge management are all becoming potential AI-agent environments.
Finally, the infrastructure bill remains significant. Nvidia’s continuing prominence shows that the AI transformation is not purely a software phenomenon. It depends on a rapidly expanding physical computing layer.
The result is an enterprise AI market increasingly divided into interconnected layers: foundation models and compute at the bottom, data and orchestration in the middle, and specialized applications and agents at the top.
Glean, Lattice and Nvidia occupy different positions within that stack, but their appearance in the same market conversation is telling.
Enterprise AI is no longer a single product category. It is becoming an ecosystem.
4. Market Landscape
The enterprise AI market is developing across several interconnected layers:
| Layer | Major developments |
|---|---|
| AI infrastructure | Nvidia GPUs, networking and AI systems underpin large-scale model workloads. |
| Foundation models | Google, Microsoft/OpenAI, Amazon and other providers are competing around model capability and enterprise deployment. |
| AI platforms | Cloud providers are adding agent development, orchestration and governance capabilities. |
| Enterprise applications | Glean, Salesforce, Microsoft and others are embedding AI into knowledge and business workflows. |
| Workforce AI | Lattice and HR technology vendors are applying AI to performance, skills and workforce intelligence. |
The competitive advantage is increasingly shifting toward platforms that can combine models + enterprise context + permissions + workflow execution + governance.
5. Top Insights
- Glean is positioning enterprise AI around delegated work, reflecting the industry’s shift from conversational assistants toward context-aware AI agents and workflow automation.
- Lattice’s Pando acquisition expands workforce intelligence capabilities, bringing AI deeper into HR workflows involving performance, skills and organizational decision-making.
- Nvidia’s earnings remain a major AI-market indicator because demand for accelerated computing continues to underpin foundation models and enterprise AI infrastructure.
- Enterprise buyers increasingly need AI systems that combine proprietary context, secure application access, workflow execution and governance rather than standalone chatbot capabilities.
- The convergence of AI applications, workforce platforms and semiconductor infrastructure shows that enterprise AI is developing into a broad technology ecosystem.
