Lori Schafer in VKTR: The 3 Elements Every AI-Driven Tech Stack Needs to Compete

Read the original article on VKTR here.

Discover how IT leaders are building AI-ready architectures with 3 core elements: visibility, interoperability and homogeneity.

With agentic AI, generative AI and other fast-moving innovations launching regularly, it’s no surprise that businesses are looking to their IT departments to do more than simply deploy and maintain software. As modern businesses increasingly rely on complex systems and analytics, IT leaders are no longer just installing new tools. Instead, they are instilling a new vision and culture for how technology integrates and builds value across the organization.  

This modern vision brings digital and business teams together as one, powered by AI and centered on one common goal: Get solutions and tools to synchronize and communicate in real time. The way to achieve it: Deliver three essential elements inside the tech architecture — visibility, interoperability and homogeneity.

Grounding AI Inside the 3 Elements

No doubt, generative and analytic AI perform a powerful function inside the three elements of visibility, interoperability and homogeneity. AI burst onto the scene as both a new tool and a disruptor of old tools, challenging businesses to leverage the newly found power of AI without dislocating proven processes.

However, both GenAI and numeric AI facilitate powerful new operations, from processing diverse data sets to discovering patterns and driving automation. Given these powers, it becomes even more important for IT teams to stay grounded and true to their core mission.

In other words, before getting too far down the road of implementing AI into a tech stack, IT leaders need to ensure they’re designing a sustainable technology roadmap. A plan that can ultimately deliver on visibility, interoperability and homogeneity.

1. Exploring Visibility

At its core, visibility within an enterprise tech architecture means systems being able to see each other so that different business functions can integrate and work together. Visibility also means c-level executives can see what technology is being used across a platform, the insights being generated in real time and the workflow that’s occurring.

Whether it’s a top-line leader seeing a more accurate, timely and holistic view of their business, or a line worker being able to understand how their actions impact downstream tasks, access to visibility has been proven over time to transform organizations, grow businesses and save a company millions of dollars.

For example, the CEO of a major international retailer learned that c-level executives had zero visibility into purchase orders and inventory on order, leading to millions of dollars in open-to-buy overruns. However, after implementing AI-powered solutions that analyzed unified data and provided executive visibility in real time, the retailer has seen a more than 6% increase in gross margin improvement and a 2% uptick in sales.

2. Understanding Interoperability

Once solutions are visible, there’s also a need to connect solutions and business needs, making one system part of another system’s processes. Just because something is visible doesn’t mean it can generate insights and results until they’re interoperable.

In a healthcare example, a hospital system can pull up a patient’s records and see medication orders, summaries of patient visits, pharmacies, etc., but until those systems are working together, the results can’t be unified.

Synergy is an old but accurate by-word for interoperability. But what makes interoperability truly powerful today is the role of AI. The technology reads through data flowing between solutions and creates actionable insights along the way.

Opportunities for such synergy are abundant. A national specialty apparel chain with 1,200 locations nationwide and a fast-growing ecommerce business suffered margin erosion because its pricing system for fast-moving fashion was blind to non-store channel inventory levels. By connecting omnichannel fulfillment to store and web pricing, the system was able to drive the most profitable inventory-managed price and avoid deeper discounts by using forecasts to inform near-daily stock re-balancing.

3. Integrating Homogeneity

Finally, with solutions visible and working together, an IT team can push toward cleaning all the data flowing through the systems and unify the insights and operations so they’re funneling through one homogenous system. AI is a major asset in automating the task of cleaning data and enriching it for analysis and decision-making.

Homogeneity is achieving total harmony inside an enterprise tech architecture, something many companies aspire to reach but haven’t quite mastered. AI and data are at the core, as data scientists work to define the scope of their AI models to access diverse, massive data sets. It’s a large footprint to work through, but once an organization establishes guardrails and cleans data appropriately, leaning on GenAI, a system will run in sync.

AI solves a company’s problems by structuring massive amounts of data and creating correlations and interlinking insights so that solutions and business functions that are visible and working together also work in total harmony.

A good example of AI’s power is unleashing it to analyze disparate data sources to find out what really drives consumer demand. A luxury retailer with extensive customer loyalty information combined it with traditional transaction data and discovered significantly higher demand for specific “wear now” items. These insights supported higher margins than previous class analysis. The reason? Only traditional sources of demand had been looked at because, prior to this, there was no way to flow the loyalty data and transaction performance data together into a single model.

Preparing for the Future

As evident, AI plays an integral role in how companies build tech architectures that provide visibility, interoperability and homogeneity, but it doesn’t happen all at once. It’s best for companies to work through these three phases in a crawl, walk, run approach. For example, start by making sure all solutions are visible to one another. Legacy tech can remain, too, while teams adjust before moving into the next phase.

As digital teams continue adapting to all the fast-moving AI capabilities, these three essential elements will shape how organizations grow and remain competitive.