Rethink AI: Building a Horizontal Layer for Enterprise-Wide Transformation
There is no question Artificial Intelligence (AI) is changing how we approach problem-solving and innovation in business.
For most companies, AI is a vertical layer they are looking at to solve specific areas. Company A sees it as a way to streamline sales by automating client profiles and sales recommendations, while Company B sees it as a way to gain efficiency in route planning for their field service teams.
What both these companies are missing is in order to leverage the full potential of AI, they need to stop treating AI as an add-on to existing software or processes (vertical) and view it as a true horizontal layer that permeates across the entire organization, driving efficiency, scalability, and transformation at every level.
Looking at AI as a Horizontal Layer
In the new era of AI, businesses have adopted AI as an enhancement to specific software or processes—an "AI-powered add-on" bolted onto existing systems to augment isolated functions. We’ve all seen this with the explosion of AI copilots and companions showing up daily in the market.
While this approach can certainly deliver short-term value, it only scratches the surface of AI's transformative potential.
In contrast, business innovation leaders and those moving rapidly on AI are treating it as a horizontal layer that integrates it into the core of business operations. They are looking at AI differently, understanding that it can positively disrupt every area of business from operations to sales to manufacturing, and even HR.
The forward-thinking or “horizontal” view is all about implementing AI to enabling seamless interaction across all departments and systems.
Think of AI as the connective tissue that binds disparate business or system functions together. Whether it’s customer service, supply chain optimization, or financial modeling, AI-powered applications provide insights and automation that transcend our traditional silos. This holistic approach to integration ensures that AI influences decision-making and operational efficiency throughout the entire organization, not just as an add-on to a single application.
A Practical Example: AI in Retail Inventory Management
A good use case that illustrates AI’s role as a horizontal layer is in retail inventory management.
Imagine a retailer with hundreds of stores and an large online presence. Rather than using AI as a standalone tool to forecast demand for specific locations, they implement a horizontally integrated AI solution that works across the business connecting multiple systems and data streams including inventory systems, supply chain logistics, and sales data across all channels.
Here’s how it works:
- Real-time Insights: AI-powered applications analyze real-time sales data, seasonal trends, and even external factors like weather or regional events to predict demand.
- Automated Replenishment: Instead of relying on manual inventory checks, the AI layer triggers automated replenishment orders, ensuring stock is at optimum levels while reducing waste.
- Dynamic Pricing: By continuously evaluating market trends, competitor pricing, and inventory levels, AI adjusts prices dynamically to maximize profitability without humans spending tons of time on research, comparisons and compiling data.
- Enhanced Customer Experience: AI personalizes product recommendations based on inventory availability and customer preferences, bridging online and offline shopping to create a single, seamless experience.
The impact? A unified, AI-powered system that reduces costs, boosts sales, and enhances customer satisfaction, all while operating inconspicuously in the background. This is not an add-on feature but a fully transformative horizontal layer that redefines how the business operates.
Moving Beyond Single Application, AI Co-Pilots
AI co-pilots—tools that assist users in specific tasks—are valuable but also represent a limited scope of AI’s potential. Co-pilots are often applied to individual applications or workflows, such as answering complex questions, compiling time-intensive data or creating reports. In contrast, an AI horizontal layer influences and affects multiple facets of the organization simultaneously and in concert.
Let’s look at customer service as a key example. An AI co-pilot might assist agents in looking up information and drafting responses, but an AI-powered horizontal layer would proactively identify trends in customer inquiries, suggest systemic improvements, and automate routine interactions—impacting not just the service desk but also product development, marketing, and logistics around it.
Benefits of AI as a Horizontal Layer
- Unified Data Utilization: By integrating AI across the organization, businesses can break down data silos, facilitating smarter, data-driven decisions.
- Opps Efficiency: Automation at scale reduces the need for manual intervention, freeing up resources to focus on areas that matter most.
- Scalability: AI-powered applications grow with the business, adapting to new challenges and opportunities without requiring separate integrations.
- Resilience: Horizontal AI layers offer a strong foundation that responds dynamically to disruptions, helping ensure continuity and adaptability throughout the business.
Embracing the Shift
To fully implement and harness AI as a horizontal layer, businesses must invest in infrastructure that prioritizes interoperability and scalability.
Cloud-based platforms and data warehouses (think Databricks or Snowflake), and modern APIs are essential to ensure that AI applications can seamlessly interact across systems.
Second but not secondary, fostering a culture of collaboration between AI experts, IT teams, and business leaders is crucial to align AI’s capabilities with organizational goals. Without conjoining these roles, companies risk AI remaining silo’d without by-in from internal leaders and collaboration across the entire org.
Conclusion
The future of AI lies in its integration as a horizontal layer within businesses, not as an afterthought or add-on.
Like many iterations of software before it, AI is becoming a layer of abstraction that will ultimately sit on top of all systems and data within an organization. Forward-thinking enterprises are already seeing this principal emerge and embedding AI into the foundation of their operations as a horizontal layer.
This change in thinking allows them to tap a nearly unlimited well of efficiency, increasing ease of innovation while gaining new levels of resiliency and flexibility
Use cases like retail inventory management truly illustrate how this approach transforms not just processes but entire business models.
If there is one takeaway from this article, its this -- It’s time to think beyond co-pilots and single applications of AI and start building AI into and across the entire ecosystem. Why limit our thinking to a single application when it is already clear AI, when applied as a horizontal layer, can drive meaningful, enterprise-wide efficiency and change.
