The real magic of AI happens when deep, system-specific smarts meet cross-enterprise intelligence — together they transform work.
As enterprises embrace the next wave of digital transformation, one truth is becoming increasingly clear: AI is not a layer to be bolted on. It’s a foundational shift — one that reshapes how people work, how systems interact and how decisions are made. But to truly harness its potential, organizations must look beyond isolated deployments and siloed use cases. They must embrace an integrated approach that combines vertical, system-specific AI with horizontal, cross-enterprise intelligence — all while keeping the end user’s persona at the center.
The two dimensions of AI in the enterprise
Today’s enterprise AI landscape is evolving along two distinct but complementary axes:
1. Vertical AI: Embedded and process-specific
Vertical AIs is embedded directly into enterprise platforms — Salesforce, ServiceNow, Icertis, Workday, SAP — and are purpose-built for the workflows and data structures of each system. These AI capabilities are deep, precise and contextual. For example:
- Salesforce Einstein GPT recommends next-best actions for sales reps based on opportunity history and communication logs.
- ServiceNow AI Search automates incident triage and resolution using past case data.
- The Icertis Agentic Platform orchestrates complex workflows that manage contract operations, uncover insight and enhance performance — offering enhanced efficiency, workflow management, increased compliance and scalability.
- Workday’s AI assistant supports managers in workforce planning and approvals based on structured HCM data.
These AIs understand domain semantics, enforce process integrity and offer real-time assistance where the user already works.
2. Horizontal AI: Cross-system knowledge discovery
In contrast, horizontal AI like Glean, Perplexity, Microsoft 365 Copilot and Google Gemini provide a unified layer of intelligence that spans across applications, documents, chat messages and enterprise content repositories.
These platforms function as the enterprise’s collective memory — surfacing relevant information regardless of where it lives. A user might ask:
“Summarize the status of a customer” or “Customer 360”
and receive synthesized insights pulled from Salesforce, ServiceNow, Gong, Freshworks, Zendesk, Google, Microsoft — all in a single response.
Where vertical AI is precise and operational, horizontal AI — referred to as centralized system-agnostic AI — is adaptive and seamlessly integrates across multiple systems or domains, unlocking unified intelligence and innovation.
Why both matter: Complementary, not competitive
Rather than competing, these two layers of AI create a symbiotic relationship:
- Vertical AI optimizes execution — streamlining domain-specific tasks and reducing process friction.
- Horizontal AI enables discovery — connecting silos, accelerating research and powering enterprise-wide knowledge flow.
Together, they offer a 360-degree intelligence framework. One powers the engine; the other provides the map. Think of vertical AI as the engine — focused, specialized and deeply knowledgeable in a specific domain. It delivers targeted insights and automates key processes unique to industries like tech, manufacturing, healthcare or real estate, powering critical business functions with precision.
Horizontal AI, in contrast, acts as the map — connecting data, systems and teams across the organization. It provides a unified view and guides decision-making by integrating diverse information, enabling collaboration and strategic alignment across departments.
When combined, the partnership ensures businesses not only run efficiently but also navigate complexity with agility and clear direction, driving faster innovation and sustainable growth.
The role of data pointers: Vertical vs. horizontal context
To function effectively, both types of AI depend on data — just not the same kind.
Vertical AI data is:
- Structured and domain-specific (e.g., opportunity-quote-transactions metadata in Salesforce, ticket journey in ServiceNow).
- Optimized for workflow automation, pattern recognition and predictive modeling.
- Anchored in system-specific data enabling precise understanding, reasoning and automation.
Horizontal AI data is:
- Semi-structured or unstructured (e.g., PDFs, chat logs, email threads).
- Indexed for semantic understanding, contextual search and multi-system summarization.
- Designed to help data traverse through multiple systems and relate information with better contextual clarity
To unlock full value, organizations must design systems where structured and unstructured data is aligned through shared metadata, consistent identity and secure access.
Persona-driven AI: A unified experience
AI’s real power lies in contextual relevance — delivering the right insight to the right user at the right moment. This is where the persona-based model shines.
By aligning AI capabilities to specific user personas — sales reps, support agents, procurement managers — enterprises can create seamless and proactive experiences in the following:
| Persona | Vertical AI Role | Horizontal AI Role |
| Sales Rep | Suggests next-best action based on opportunity stage and account insights | Summarizes recent cross-functional communication (Slack, Sharepoint, Emails) about a client to prep for a meeting |
| Support Agent | ServiceNow AI assigns and resolves cases | Perplexity retrieves knowledge base articles and chat logs |
| Procurement Lead | SAP Ariba AI might analyze purchase orders and build incremental process automation | Horizontal AI might summarize market insights and compare it with purchase orders |
This dual-layered approach elevates productivity, improves decision quality and drives faster outcomes by understanding both what the user is doing and what they need to know.
Making it work: The AI operating model
To operationalize this convergence of AI layers, CIOs must embrace a modern enterprise architecture built on:
- Federated data indexing. Securely connect application data to enterprise-wide AI models.
- APIs and event frameworks. Enable communication between vertical and horizontal AI layers.
- Unified identity and access. Ensure consistent entitlements across platforms.
- Metadata management. Apply taxonomies that work across systems.
- Feedback loops. Use engagement data to fine-tune both models over time.
“AI adoption is no longer about isolated wins” is the new Guru Mantra.
The future lies in blending Vertical AI — deep domain-specific expertise in areas like sales, finance, marketing, legal or procurement with Horizontal AI — enterprise-wide intelligence that connects data, teams, processes and workflows.
This approach delivers both precision and scale, breaking solos and turning AI from a set of disconnected tools into a unified growth engine.
Looking ahead: Agentic workflows and AI-first design
The next frontier is agentic AI — autonomous agents that execute multi-step tasks across systems on behalf of users. Imagine a revenue operations agent who identifies a missed upsell opportunity, drafts a proposal, schedules a meeting and alerts the account executive. Revenue operations should always seek to equip sales teams with AI-driven insights to proactively manage deal health, mitigate potential risks and close deals faster..
Every enterprise function across legal, procurement, finance and sales will be supported by a digital twin — powered by AI agents and orchestrated by agentic workflows. These workflows empower agents to act as a force multiplier for enterprise productivity and performance. For example, by developing contract intelligence agents built for the enterprise, Icertis is driving the next era of AI innovation for commercial relationships, accelerating strategic outcomes and maximizing contract value.
However, this requires not just smarter models but AI-first process design, robust orchestration layers, accurate, reliable and quality business-grade data, and tight system integration.
These AI-first systems aren’t just tools or another platform on the shelf. Leaders should treat them like smart, trainable interns — digital co-workers that are highly motivated to learn, observe and act — reshaping how enterprises operate. Investment in governance, trust, clarity and explainability is key to adapting agentic workflows responsibly. As organizations mature in AI adoption, they’ll move from passive assistance to proactive orchestration and eventually to autonomous collaboration.
Final thoughts: Digital transformation reimagined
Digital transformation used to mean migrating to the cloud, digitizing workflows and deploying platforms. Today, it means infusing intelligence into every layer of the enterprise—from process to person, from system to strategy.
By combining the precision of vertical AI with the reach of horizontal AI and tailoring it to user personas, organizations can build a truly intelligent enterprise.
This isn’t just transformation. It’s reinvention!
This article is published as part of the Foundry Expert Contributor Network.
Want to join?
