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What’s next for artificial intelligence (AI) in enterprise technology?
Our 2025 Workforce & Business Priorities Report, publishing in April 2025, reveals that 84% of IT decision-makers plan to invest in AI—and they’re moving well beyond the experimentation stage. Today’s tech leaders are seeking advanced AI integration opportunities to drive efficiency, security, and innovation across the digital ecosystem.
This year, the focus is on leveraging AI and automation for three key benefits:
- Accelerating application development
- Optimizing IT infrastructure
- Revolutionizing knowledge management
This report explores the developments driving these trends, as well as how these advancements are reshaping IT and business operations.
AI-Augmented Application Development & Testing
The mainstream adoption of AI-driven development is quickly transforming how applications are built, tested, and deployed. IT teams are streamlining the coding process with artificial intelligence and, occasionally, automating it altogether.
What’s driving this trend? AI-powered coding assistants and pair programming software are advancing beyond simple coding suggestions. Tools like GitHub Copilot, Amazon CodeWhisperer, and Google Gemini now help developers refactor code, identify and fix bugs, and optimize applications for security and efficiency. McKinsey estimates that generative AI can reduce the time spent on individual coding tasks by up to 50%.
| Development Task | Time Savings |
| Code Documentation | 45-50% |
| Code Generation | 35-45% |
| Code Refactoring | 20-30% |
Source: McKinsey & Company
Efficiency gains are further growing as low-code/no-code applications advance with AI integrations. Platforms like Microsoft Power Apps and Salesforce Einstein now auto-generate workflows, suggest business logic, and optimize performance, requiring only minimal human oversight to achieve target outcomes. ADP Research shows cooling demand for developers, indicating a decreased reliance on traditional software teams.
Suffice to say, the role of IT workers in the age of AI is shifting—but the need for technical professionals is not going away. According to MIT Sloan, the use of generative AI is simply elevating capabilities—improving the performance of highly skilled workers by nearly 40%.
This shift is best illustrated by the transformation of continuous integration/continuous delivery (CI/CD) pipelines, which are moving toward self-optimization. Modern AIOps tools like Datadog, Dynatrace, and Harness now automate performance and security monitoring, test case generation, and deployment rollback strategies. IT teams are improving reliability while saving time for high-value tasks.
Strategic Implications for Tech Leaders
So, how is AI-augmented development changing the IT landscape? Tech leaders can now achieve:
- Faster Time-to-Market – AI-driven automation slashes development cycles, enabling rapid iteration.
- Enhanced Software Quality – Leveraging AI integrations for testing processes reduces human errors, making software more resilient.
- Scalability & Cost Efficiency – AI-powered solutions optimize cloud resources, reducing operational costs.
Resilient & Autonomous IT Infrastructure
AI-powered enterprises have largely capitalized on driving efficiency gains in recent years. However, artificial intelligence solutions also have the potential to entirely reshape IT infrastructure—enabling more secure, reliable systems that derisk the organization.
There are several breakthroughs driving this area of transformation. First, there’s the rise in AI-driven predictive monitoring and AIOps tools. Platforms like Splunk AI, ServiceNow ITOM, and Dynatrace use machine learning (ML) to analyze IT logs, detect anomalies, and predict failures before they impact users.
There’s also the advancement of self-healing IT systems. Leveraging tools like Microsoft Azure Automanage and AWS Systems Manager, tech leaders can achieve the automatic detection, diagnosis, and resolution of performance issues. If a cloud server experiences high latency, AI can restart the instance or redistribute workloads with no human intervention needed.
Zero-touch provisioning (ZTP) is further automating the deployment of devices and network components.
What’s next for artificial intelligence (AI) in enterprise technology?
Our 2025 Workforce & Business Priorities Report, publishing in April 2025, reveals that 84% of IT decision-makers plan to invest in AI—and they’re moving well beyond the experimentation stage. Today’s tech leaders are seeking advanced AI integration opportunities to drive efficiency, security, and innovation across the digital ecosystem.
This year, the focus is on leveraging AI and automation for three key benefits:
- Accelerating application development
- Optimizing IT infrastructure
- Revolutionizing knowledge management
This report explores the developments driving these trends, as well as how these advancements are reshaping IT and business operations.
In theory, AI-based provisioning tools like Cisco DNA Center and Juniper Mist AI can help IT teams scale global infrastructure deployments up to 10x faster while maintaining security compliance.
Strategic Implications for Tech Leaders
New AI implementations that optimize IT infrastructure can effectively help tech leaders achieve:
- Reduced Operational Risks – AI proactively identifies and resolves IT issues before they escalate.
- Enhanced IT Productivity – Automating infrastructure management frees teams for innovation.
- Scalable & Adaptive IT Environments – AI ensures IT operations adjust dynamically to business demands.
AI for Intelligent Knowledge Management
The true test of organizational resilience goes beyond robust infrastructure by ensuring that critical knowledge flows freely regardless of personnel changes, market shifts, or unexpected disruptions. Increasingly, the AI-powered enterprise is empowered to democratize access to knowledge. This shift breaks down silos and enables real-time, contextual insights.
Let’s first explore how AI-powered enterprise search enables context-aware results. Tools like Microsoft Copilot, ServiceNow AI Search, and Coveo AI empower companies to leverage Graphic Retrieval-Augmented Generation (GraphRAG) to interpret user intent rather than find keyword matches. This semantic understanding reduces knowledge retrieval time while providing the most precise answers possible. In fact, Anthropic reports its contextual retrieval method can reduce failed retrievals by 49%.
Internal AI assistants and chatbots can similarly support knowledge retrieval. With AI-powered virtual agents, organizations can reduce internal help desk tickets with more effective self-service options. For example, Salesforce Einstein GPT and ServiceNow Virtual Agent equip employees with real-time solutions to human resources, IT, and operational queries. Employees can ask, “How do I approve a purchase request in ServiceNow?” and get instant step-by-step guidance.
Artificial intelligence can also support the ongoing development of more productive knowledge bases with automated knowledge tagging, summarization, and classification capabilities. For instance, AI can scan legal contracts and classify them by risk level, compliance category, or client type.
Strategic Implications for Tech Leaders
Leveraging AI for advanced knowledge management is a relatively new frontier, but it’s already driving exception results, including:
- Accelerated Decision-Making – AI-powered knowledge management that enables real-time access to critical information.
- Enhanced Employee Productivity – AI reduces time spent searching for data, boosting efficiency.
- Future-Proofing Organizational Knowledge – AI continuously learns and updates knowledge bases, keeping them relevant.
AI Is Reshaping the Technology Landscape
AI-driven automation is no longer a luxury—it’s a necessity for efficiency, scalability, and resilience. Tech leaders must actively invest in AI-powered development tools, autonomous IT operations, and intelligent knowledge management systems to stay competitive.
Key Takeaways for Q1 2025
- AI-driven development is accelerating software release cycles and improving code quality.
- Self-healing IT infrastructure is reducing downtime and optimizing cloud efficiency.
- AI-powered knowledge management is breaking silos and enhancing organizational intelligence.
By leveraging these innovations, organizations can unlock new levels of efficiency, agility, and intelligence in an increasingly AI-driven world.
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