Why Time Allocation Matters

One of the biggest questions IT leaders wrestle with is: how should my team spend its time?
Lean too far into operations and innovation stalls. Lean too far into projects and the business risks outages, compliance gaps, or unhappy users. The right balance drives both stability and growth.

The 50/25/25 Framework

A simple but effective baseline I’ve seen work across organizations:

50% Operations — keeping the lights on: uptime, user support, monitoring, patching, compliance, incident response.
25% Business Process Improvement (BPI) — workflow optimization, automation, cost reduction, service improvements.
25% Large Projects — ERP deployments, cloud migrations, cybersecurity enhancements.

This model ensures IT stays reliable while still investing in efficiency and transformation. It’s not rigid, but it’s a strong starting point.id — priorities shift with business needs, but the model provides a healthy baseline.

Alternative Models for Different Contexts

Every organization is different. These three models adapt the baseline to different business realities:

Balanced Growth (50/30/20): Mid-sized firms with stable ops, tilting more toward projects for growth.
Transformation-Heavy (40/45/15): Organizations in the middle of major digital change where large programs dominate.
Lean Optimization (45/25/30): Companies doubling down on automation, continuous improvement, and reducing technical debt.

The point: time allocation should shift with business maturity and priorities.

Challenges in IT Operations

Even with half the time dedicated, operations remain tough:

Legacy + new systems that don’t integrate cleanly.
Limited visibility into assets and services.
Balancing scalability with security.
Budget pressure — “do more with less.”
Rapid change in tools, practices, and skills.

The best IT leaders anticipate these challenges by building automation, resilience, and visibility into their operating model.

Making Business Process Improvement Real

Improvement work only matters if it’s measurable:

KPIs: response times, error rates, cycle times.
ROI: cost vs. benefit.
Feedback loops: employee and customer insights.
Process metrics: throughput, lead time, defect rates.

If you can’t measure it, it’s not true improvement.

Large IT Projects: Where Time Goes

Typical categories include ERP/CRM deployments, cloud or data center migrations, cybersecurity modernization, and disaster recovery. These demand cross-functional planning and strong governance to deliver.

Digital Transformation Is Continuous

Transformation is no longer “one and done.” It’s baked into every initiative:

IT uplift: modern infrastructure, cloud, security.
Digitized operations: AI, RPA, analytics.
Customer experience: seamless digital touchpoints.
Workforce transformation: training, new tools, new ways of working.

In pharma and biotech, these shifts accelerate R&D, streamline trials, and strengthen patient engagement.

Talent and Culture Matter

Time allocation isn’t just what IT does—it’s how IT works. Smart leaders carve out time for:

Upskilling on cloud, AI, and cybersecurity.
Agile practices and short delivery cycles.
Knowledge sharing across IT and the business.

Without investing in people and culture, even the best time split won’t deliver full value.

Automation and AI Are Changing the Ratios

Automation is steadily reducing the operational burden. Monitoring, patching, and incident response are increasingly automated—freeing teams to invest more in process improvement and strategic projects.
The 50/25/25 balance could become 40/30/30 in the future.

Running IT by the Numbers

Strong IT management requires metrics:

Visibility: inventories, monitoring, dashboards.
KPIs: uptime, MTTR, SLA compliance, cost per incident, project timeliness, user satisfaction.

With these in place, IT shifts from reactive firefighting to proactive strategy.

Conclusion

The 50/25/25 framework provides a healthy starting point, but it’s not a rulebook. The right mix shifts with context—growth, transformation, or optimization. Pair any model with visibility, metrics, culture, and automation, and IT leaders ensure their teams deliver stability today and transformation tomorrow.

Back in the early 2000s, I had the opportunity to visit McLaren F1 and witness firsthand how they were beginning to harness vast, disparate data streams to drive performance. Even then, McLaren was ahead of the curve—integrating telemetry, tire wear, weather conditions, and driver behavior into centralized systems to inform race strategy and car development.

What struck me wasn’t just the volume of data, but how they approached it. They weren’t just collecting information—they were thinking in terms of relationships. They were connecting seemingly unrelated signals to uncover patterns and optimize outcomes. In hindsight, it was an early glimpse into what we now call semantic data modeling and real-time analytics.

At the time, I was working at a large pharmaceutical company, facing many of the same data challenges that still persist today—particularly in High Throughput Screening (HTS) and early-stage drug discovery. We were generating massive datasets from compound libraries, assay results, and lab instrumentation. But much of that data was siloed—scattered across systems, formats, and teams. Integrating and interpreting it was a major hurdle. We often relied on manual processes and intuition to connect the dots, but the complexity and fragmentation made it nearly impossible to see the full picture. We knew the insights were there—but without better tools for correlation and context, they remained locked away.

That visit to McLaren sparked a question that stayed with me: what if we could apply the same principles—centralized data integration, real-time feedback loops, and predictive modeling—to accelerate decision-making in pharma?

That experience fundamentally shaped how I began to think about knowledge management, data architecture, and the future of AI/ML in life sciences. It was a clear example of what becomes possible when data is treated not as a byproduct of operations, but as a strategic asset.

Today, we’re finally seeing that vision come to life. Advances in AI and computing power are helping bridge the gap. Natural Language Processing (NLP) and machine learning models can now extract structure and meaning from unstructured content, making it easier to unify data across formats and sources. Foundation models trained on scientific literature and experimental data can identify patterns and relationships that would be nearly impossible to detect manually. And with scalable cloud infrastructure and high-performance computing, we can process and analyze these massive datasets in real time. Together, these technologies are transforming how we approach discovery—turning once-inaccessible data into actionable insight.

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Category Archives: Digital Transformation & Strategy

Optimal Time Allocation for IT Teams