AI is taking over tasks but what happens to the time it gives back

Artificial intelligence (AI) is entering a different phase of enterprise adoption. The first wave was about assistance. Employees used copilots to write, summarize, analyze, code, and search. The next wave is about execution, with AI agents capable of carrying out defined tasks and agentic systems coordinating multiple steps across a workflow.

That shift changes the technology problem. An AI assistant can help a person work faster. An agent can change how the work itself is structured.

In a conversation with Nirav Rawell, Product Head at ProHance, the focus was on this transition and what it means for software development, quality assurance, workforce capacity, and enterprise technology. The discussion points to a future in which AI productivity will depend less on the number of tasks automated and more on how intelligently machines and people work together.

Nirav Rawell, Product Head at ProHance

From copilots to systems that act

The distinction between today’s AI tools is becoming increasingly important. A copilot generally sits beside a human and provides assistance. An autonomous agent takes responsibility for a defined task and can execute it with less intervention. Agentic AI extends the model further by allowing multiple agents to work together, with each handling a particular capability within a larger process.

That architecture changes the role of the underlying large language model (LLM). Instead of treating an LLM as the entire application, enterprises can use it as a reasoning component inside a broader software system.

The system can give an agent access to tools, business data, applications, and predefined rules. Another agent can review the output. A human can remain in the loop when a decision requires judgment or carries significant risk. This is where enterprise AI starts looking less like a chatbot and more like an operating layer for software workflows.

The technology challenge is no longer just generation

Generative AI has made producing content, code, and other outputs relatively easy. The harder engineering problem is determining whether those outputs are good enough to use. This is particularly visible in software development. AI coding systems can generate code rapidly, but speed alone does not tell an engineering team whether the code is secure, maintainable, or aligned with the requirements.

One emerging approach is to introduce an AI evaluator into the workflow. The first system generates the code, while another system evaluates it against defined criteria. The process can then iterate before a human reviewer sees the final result. That creates a new layer in the AI software stack. Generation becomes one component. Evaluation becomes another.

The idea extends beyond coding. AI systems can also be evaluated against expected answers, business rules, quality thresholds, or other criteria. This is helping push enterprise AI toward a more structured model of testing often referred to as evaluations or “evals.” For enterprises, this matters because probabilistic software needs a different approach to quality than conventional deterministic applications.

AI can create capacity but cannot decide where it goes

The productivity argument becomes more interesting once automation removes part of a person’s workload. Imagine a software engineer who previously spent a substantial amount of a development cycle writing routine code. An AI coding system reduces that effort. The organization has gained capacity, but the technology has not automatically created additional business value.

That capacity could go into architecture, security testing, debugging, product experimentation, or learning. It could also disappear into more meetings or simply be used to produce additional low-value output.

This is why the idea of human reallocation is becoming important. Instead of asking only how much time AI saves, organizations can examine whether employees are moving into work that requires greater reasoning, creativity, and domain expertise. It is a subtle change in the productivity equation. Automation becomes valuable when the human work surrounding it changes as well.

Benchmarking is becoming a technology problem

The ProHance Global Productivity Benchmarking Report 2025 offers an interesting lens here. Rather than looking at productivity as a single measure of logged working time, it separates activity into areas such as time on systems, productive time, time away from systems, and idle time. The report draws on three years of data covering more than 198,500 users across 68 organizations. Its value is less about any individual number and more about the way it frames productivity as a combination of activity, focus, workload, and capacity.

The latest edition adds workload analysis and comparisons across hybrid, work-from-home, and office environments. That makes the report particularly relevant to AI adoption because automation is likely to alter not just how much work employees perform, but also how workloads are distributed.

The analysis shows that organizations can have two very different problems at the same time. Some teams can be overloaded while others have unused capacity. That is a useful warning for AI programs. Automating a process does not necessarily solve a capacity problem if the organization does not know where the newly available capacity should be deployed.

Enterprise AI is moving deeper into workflows

This is also visible in Global Capability Centers (GCCs), which are increasingly being positioned as technology and innovation hubs rather than purely delivery centers.

Research from Zinnov and ProHance describes GCCs as taking on three roles in enterprise AI. They can orchestrate AI roadmaps, incubate new use cases, and act as stewards for governance and adoption. The research also identifies India as having a significant AI talent advantage, supported by strong AI skill penetration and a large installed talent base.

The technology is already moving beyond isolated pilots. GCCs are increasingly exploring AI in software engineering, research and development, fraud detection, pharmacovigilance, demand forecasting, claims management, and other data-intensive processes.

The progression is important. Back-office automation can establish trust and demonstrate early value. Middle-office processes often provide richer opportunities because they combine large amounts of data with human judgment. Customer-facing systems offer potentially greater business impact, but they also introduce higher compliance and reputational risks.

The infrastructure underneath AI matters

Agentic systems also expose weaknesses that traditional software architectures could sometimes hide. AI needs access to data, computing resources, applications, and APIs. If those systems remain fragmented, an agent cannot reliably move through a business process.

The Zinnov-ProHance research found that data readiness, governance, and infrastructure remain major barriers to scaling AI. Fragmented datasets, privacy restrictions, legacy systems, and limited computing capacity can all prevent AI projects from moving beyond experimentation. That makes enterprise AI an infrastructure story as much as a model story.

Cloud platforms, GPUs, data platforms, integration layers, security controls, and governance mechanisms increasingly form part of the same architecture. The model may be the visible part of an AI application, but the surrounding infrastructure determines how reliably that application can operate.

The next AI advantage may come from orchestration

The industry is gradually moving away from the idea that a single model will solve every enterprise problem. Instead, the emerging architecture looks more distributed. One system can generate. Another can retrieve information. A third can evaluate the output. An agent can coordinate the process, while a human takes control when the decision requires judgment.

That architecture creates new engineering questions around permissions, evaluation, observability, data access, and accountability. It also creates opportunities for organizations to redesign workflows rather than simply add AI to existing software. The productivity benchmark data adds another piece to that puzzle. Across the three-year dataset, productive hours have risen faster than logged hours, suggesting that measuring time alone gives an incomplete picture of how work is changing.

The next phase of enterprise AI, therefore, is unlikely to be won simply by having access to a more capable model. The bigger advantage could come from building better systems around those models. Systems that know when to act, when to ask for help, how to evaluate their own output, and where human judgment remains essential.

That is a much bigger shift than automating individual tasks. It is the beginning of an AI-native productivity stack in which models, agents, software infrastructure, and people become parts of the same operating system for work.

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