How can Organizations Get More from Enterprise Tech?

In 2026, the challenge for leaders isn't a lack of technological innovation; it’s an overwhelming abundance of it. As organizations face decisions such as where to integrate AI, whether to replace humans with Agents, where to host operations (on-premises or in the cloud), and more, many fall victim to a fragmentation tax—where disconnected technologies and systems generate more work than they save. 

To regain efficiency, leaders must shift from a "Project" to a "Systemic" mindset when making tech decisions. When organizations successfully make this transition, they get more from enterprise technologies, moving from treating different them as separate components to treating them as a unified Value Stream. 

In this blog, we’ll explore key strategies on how enterprise technology, when viewed collectively as an ecosystem, can improve IT performance in large organizations.

What Do we Mean by "Enterprise Technology"?

When we discuss enterprise technology, we are moving beyond the narrow definition of back-office software. In today’s context, we refer to the organization's entire digital engine. To understand how to increase ROI from enterprise technology, we must first look at the three distinct roles this engine plays:

This unified ecosystem can be understood via three main layers:

1. Core Product Engineering & DevOps (The Building Blocks)

This is where the core application development begins. It is the "factory floor" where engineers build enterprise software solutions that power businesses. DevOps acts as the conveyor belt, ensuring that new features are tested and delivered to the business quickly and securely, preventing the bottlenecks that legacy enterprise systems pose.

2. Hosting & Orchestration (The Command Centre)

Once the enterprise software solution is built, it requires a stable host—whether a secure on-premises server, a public cloud environment, or a hybrid of both. 

To host workflows on an on-premises server, you need an entire infrastructure (including hardware) to run your ops. With cloud computing, you either use cloud-managed services or leverage virtual components on public clouds (AWS, GCP, Azure) via a pay-per-use basis. 

3. Artificial Intelligence (The Brain)

This is where AI in enterprise comes into the picture. In the early stages, AI in enterprise looks like a series of isolated pilots/PoCs, such as a chatbot that answers basic HR questions. In later stages, it moves from a "peripheral tool" to the organization's central brain, which analyzes data for better decision-making. 

To do this at scale, you need MLOps. It ensures that your AI tools for enterprise productivity don't "hallucinate," or go stale, by creating a continuous loop in which the system continually learns from new data, validates its accuracy, and re-trains itself without human intervention.

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How Can Organizations Get More from Enterprise Tech?

Aligning all technologies into a unified enterprise technology ecosystem is not easy, though. This is because it isn’t just a question of licensing software or spending on additional AI tools to improve enterprise productivity. It involves changes at both the operational and behavioral levels across every function. Additionally, it’ll call for more collaborative workflows. 

Here are 6 key imperatives that more than 100 tech leaders have collectively formulated to help organizations maximize the value of enterprise software.

1. Rethink IT & Engineering Economics

The cost considerations of enterprise technology are changing. AI tools for enterprise productivity reduce per-unit overhead costs by largely automating redundant processes. However, costs associated with high-value AI systems, such as those required for inference, will increase.

Consequently, organizations should recalibrate their IT investments to maximize ROI. According to McKinsey, increasing annual budgets by 4% over the next four years and channeling funds toward AI tools for automation should improve IT performance in large organizations while reducing runtime costs. 

2. Rebuild Tech Platforms

Forward-looking organizations are re-engineering their platforms, with AI at the core of the enterprise. These efforts not only make it easier for them to scale and integrate emerging technologies but also help them overcome technical debt. 

Moreover, when organizations re-engineer their platforms to be "AI-native," they experience three systemic shifts that make IT modernization strategies more effective:

• Manual Integration to Agentic Interoperability: Instead of writing custom code to integrate your CRM with your ERP, AI Agents can be integrated at the core to navigate different systems independently.
• The Development of a "Unified Data Estate": With AI in enterprise at the center, the focus shifts from storing data in separate silos to creating a single source of truth that acts as a contextual knowledge layer.
• Self-Optimizing Infrastructure: An AI-core platform uses MLOps practices to replace manual tracking & troubleshooting with a predictive, closed-loop system. This system analzyses signal across all layers, filters out noise, and identifies root causes. It doesn't wait for the entire system to crash but automatically adjusts cloud resources to maintain performance and uptime. This can result in 40-50% shorter timelines. 

If this seems like a stretch, you can consider outsourcing legacy application modernization services. You can continue innovating while professionals rebuild your solutions without disrupting operations. 

3. Improve Enterprise Data Quality

Engineer your data to provide more context to AI tools for enterprise productivity. Inaccurate or "dirty" data forces AI to guess, leading to errors that require manual human correction. On the other hand, high-quality data serves as the ground truth, ensuring its outputs are strategically relevant. It enables AI in the enterprise to understand the nuances of your specific supply chain, customer history, and internal policies.

4. Rework Your Talent Model

Enterprise technology cannot work alone; it requires people. Invest in IT modernization strategies and engineering capabilities that allow your people to work alongside AI Agents. This means re-designing all processes and workflows. A study on human-agent collaboration found that human-agent teams achieved a 70% increase in work completion rates compared with agents operating alone.

5. Look into the Vendor Equation

With AI in enterprise software solutions, the traditional rules for vendor selection must change.

• Move away from subscribing to hundreds of separate SaaS tools. Instead, build your stack around API-first AI platforms that can integrate open-source AI models and perform cross-system tasks.   
• The old choice between "outsourcing" and "insourcing" is fading. With AI, you can keep the "brain" of your operations in-house while using vendors only for the basic infrastructure.

6. Re-think Resilience

While AI in enterprise technology offers many advantages, it introduces new risks, primarily around bias. Moreover, interconnected enterprise technology systems expose more endpoints to exploitation. Consequently, organizations must remodel risk both with and for AI. To do so with AI, they can leverage predictive insights derived from attack patterns. To optimize AI risk, they must implement human-in-the-loop validation to maximize accuracy and minimize bias. 

Also Read: Back Office as a Service (BOaaS)

How to Increase ROI from Enterprise Technology?

Companies that can reprioritize their investments in line with the above imperatives can achieve significantly higher ROI from AI in enterprise technology. Allocating funds early to AI-agnostic platforms can reduce future spending, resulting in a greater EBITDA lift over a five-year period. [Source: McKinsey]

As observed, maximizing enterprise technology ROI requires a shift from maintenance to innovation. Traditional 20% IT investment leads to stagnant growth and rising costs. However, reallocating about a third to IT reduces long-run costs while tripling EBITDA gains through superior engineering productivity and systemic integration.

The Challenge with AI in Enterprise Technology: Business Support

Driving ROI from enterprise technology requires not only strategic, timely investments but also management support. However, findings suggest that only 13% of tech leaders reported having support from their business counterparts.

Leaders must bridge this gap to improve IT performance in large organizations. Tech leaders can enhance their credibility by delivering better digital products on time and within stipulated budgets, leveraging AI’s efficiency and expert validation. At the same time, business decision-makers can specify requirements and equip their tech teams with frameworks that allow them to work with AI in the loop. 

The Path Forward: Collaborative, AI-First Digital Engineering

The era of treating technology as a collection of individual solutions is over. Going forward, organizations that succeed will be the ones that view diverse technologies as an ecosystem, a single value stream referred to as “enterprise technology.” This is done by realigning investments, improving data context, and fostering human-agent collaboration. 

The roadmap to improve IT performance in large organizations is clear: it requires a bold transition from tech silos to systemic innovation. Whether you do it internally or collaborate with a digital engineering service provider, the goal is to build a sustainable ecosystem that supports business growth and increases long-term ROI.