Solving the mystery of named resource management in life sciences project management for organizational effectiveness

Microsoft Project Online is retiring: What’s next for organizations?

Microsoft Project Online RetirementMicrosoft has officially announced the retirement of Project Online, marking a major shift in how organizations manage projects and portfolios in the Microsoft ecosystem. While this move may seem disruptive, it’s also an opportunity to modernize your project management landscape with more agile, connected, and scalable solutions.What is retiring and what is not within the MS Project Management ecosystem CategoryProduct / Component StatusProject OnlineMicrosoft Project Online (part of Project for Web and Project Online Plans 1–5) Retiring (officially retiring on September 30, 2026)Project ServerProject Server Subscription Edition (on-premises)Not retiring (Microsoft has committed to supporting it through at least July 14, 2031)Project Server 2019 / 2016 / 2013Legacy on-prem versionsRetiring / Out of mainstream supportProject for the WebModern Project experience (built on Power Platform and Dataverse)Active / ExpandingPlannerMicrosoft Planner (and Planner Premium)Active / ExpandingProject Desktop ClientMicrosoft Project Professional (Desktop app)Still available but staticWhat should be your next steps?Our PPM experts identified four key paths forward, some cover Microsoft project alternatives within the Microsoft ecosystems, where are some options go outside Microsoft. Here is a detailed look at their pros, cons, and technical implications to help you make an informed choice. 1. Move to Microsoft Planner with premium capabilities / Power Platform extensionsPlanner has evolved beyond a simple task board. With Planner Premium (built on Microsoft Project for the web) and Power Platform integration, organizations can create scalable, low-code project management environments that automate workflows, connect to data sources, and deliver analytics. They can easily recreate their MS Project plans within Planner Premium or extend them using Power Platform components for automation and reporting.Pros:Modern UI and simplicity: Intuitive, cloud-native experience with integration into Teams and Microsoft 365.Automation and customization: Power Automate, Power Apps, and Dataverse enable custom workflows and reporting.Scalable and future-ready: Microsoft’s strategic focus is clearly on the Power Platform–Planner stack, ensuring continued innovation.Unified data model: Leverages Dataverse for consistent data handling and analytics via Power BI.Cons:Migration complexity: Data structures in Project Online differ from Planner/Dataverse, requiring careful mapping and reconfiguration.Feature gaps: Advanced portfolio-level functions (like EVM or multi-dimensional resource planning) require custom builds or add-ons.Change management effort: End users need to adapt to new workflows and interfaces.Cost implications:Low to moderate initial cost: Most Planner Premium and Power Platform capabilities come under existing Microsoft 365 or Power Platform licenses.Implementation costs vary: Custom app development, workflow setup, and Power BI dashboarding can add moderate consulting expenses.Ongoing savings: Reduced infrastructure costs and seamless integration minimize total cost of ownership (TCO). 2. Move to Project Server Subscription Edition (On-Premises)For organizations not ready to go fully cloud-native, Microsoft Project Professional or Project Server Subscription Edition offers a supported, on-premises continuation of Project Online capabilities.Pros:Continuity with existing processes: Familiar interface, enterprise resource planning, and custom fields remain intact.Control and compliance: Data stays on-premise—ideal for regulated industries with strict data residency requirements.Integration consistency: Existing add-ins, reports, and integrations can often be retained with minimal rework.Cons:Limited innovation: Microsoft’s development focus has shifted to the cloud; few major updates are expected.Higher maintenance overhead: Infrastructure, patching, and scalability remain your responsibility.Scalability constraints: Not ideal for distributed or hybrid teams needing mobile/cloud access.Cost implications:High capital cost: Requires on-prem servers, SQL licensing, and ongoing hardware maintenance.Lower migration cost: Minimal configuration changes compared to cloud migration.Higher long-term cost: IT resource overhead, patching, and version upgrades add recurring expenses. 3. Hybrid or mixed approachMany enterprises choose a hybrid setup, using Planner and Power Platform for agile, team-level project tracking while retaining Project Server for enterprise-level program management.Pros:Balanced modernization: Gradual migration minimizes disruption.Best of both worlds: Agile teams get flexibility while PMOs retain robust governance tools.Phased adoption: Allows time to retrain teams and adjust processes.Cons:Integration complexity: Requires connectors or middleware to keep systems in sync.Dual administration: Managing both environments increases oversight effort.Data consistency risks: Without clear governance, data integrity may be affected.Cost implications:Moderate setup cost: Investment in integration tools and Power Platform customization.Reduced upfront burden: Avoids full migration costs by spreading transformation over phases.Higher operational cost: Running and maintaining two environments can increase ongoing spend. 4. Switch to third-party enterprise PPM toolsFor organizations looking for end-to-end portfolio management with built-in financials, resource planning, and risk management, third-party tools like Planisware, Clarity, Smartsheet, OnePlan or Wrike offer comprehensive alternatives.Pros:Rich PPM functionality: Mature features for scenario planning, capacity management, and financial tracking.Industry-specific capabilities: Tailored solutions for pharma, engineering, or R&D.Dedicated vendor innovation: Regular updates and roadmap-driven enhancements.Cons:High licensing cost: Enterprise-level subscriptions can be significant.Complex migration: Requires data mapping, validation, and process reengineering.Reduced Microsoft integration: Some features may require additional connectors or third-party middleware.Cost implications:High upfront investment: Licensing, implementation, and integration costs can be substantial.Predictable recurring costs: Annual subscriptions and vendor-managed support simplify budgeting.Potential savings in efficiency: Rich automation and portfolio analytics can deliver ROI over time. Make the right choice with i2eAt i2e, we help organizations evaluate their Project Online footprint, assess migration complexity, and select the right modernization path—balancing functionality, cost, and long-term strategy. Our consultants specialize in Microsoft PPM modernization, Power Platform automation, and data integration, ensuring a smooth transition with minimal downtime. Whether your goal is cost optimization, enhanced agility, or future scalability, we design a roadmap that aligns with your business priorities.

Is your data ready for generative AI: a guide for life sciences organizations  

Generative AI (Artificial Intelligence) comes with a promise of offering unparalleled opportunities to life sciences organizations. Yet, the success of the journey grips on how data ready is your company for gen AI. From improving drug discovery to enhancing trials and devising marketing strategies, there is a vast potential to take benefit from the use of gen AI applications. However, the hurdle most Chief Data Officers (CDOs) and data leaders in the life sciences domain are facing is managing data and scaling AI use cases. Now, they need to focus on making changes within the data and the architecture for gen AI to produce meaningful results for the business. In this blog, we explore the importance of making data ready for generative AI and actionable insights for life science companies to navigate the generative AI data with confidence. Importance Of Data Quality for Gen AI Applications Data quality affects the accuracy, dependability, and consistency of algorithmic patterns and results of gen AI applications. To ensure its standards, organizations should build strategies comprising of data validation and data cleansing methods. Data validation refers to authenticating the accuracy of information through different facets. It includes verifying the data for errors, patterns, and inconsistencies and ensuring it runs parallel to the organizations' standards. While the data cleansing process is implemented to fix the errors found during validation, it involves eliminating duplication, correcting errors, and standardizing the data for overall consistency. Data validation is decisive for Gen AI applications as it makes sure the data presented to AI models is reliable, consistent, and precise. Without validation, the input data could have inconsistencies, biases, and errors, leading to variable and unreliable AI-led output. These make sure that AI models are trained to offer reliable and high-quality data for organizations to lay their problem-solving decisions. What Constitutes Data Readiness for Generative AI Data readiness for gen AI involves multi-layered tactics with a few components that are critical for organizations. Next, let us look at the steps involved in preparing data for gen AI usage. Steps to Prepare Data for Gen AI To leverage the power of Gen AI, the data should be prepared well. Here are the four critical steps to prepare life sciences data for gen AI. Data acquisition and creation The fundamental practice of preparing data for gen AI starts with acquiring data from diverse datasets and curating relevant data. The data should consist of all the critical components which are essential to generate the right response. For example, while acquiring data for drug development care should be taken to include chemical structures, target proteins, biological assays, drug reactions, and trials. Data can be obtained from academic literature, internal records, public repositories, and proprietary databases. The next focus should be on the creation of data by cleaning the acquired data and standardizing it to maintain quality and consistency. At the same time, the steps should involve correcting errors, eliminating duplication, and regulating data formats. Additional data including patient demographics, assay conditions, and molecule identifiers should also be analyzed and cleared for further data interpretation and training models. Data cleansing and preprocessing This step involves improving the available data, particularly when it is disorganized or limited. For generating superior results from gen AI cleansing and preprocessing methods must be applied. Data synthesis is the method implemented, which involves creating new data samples based on the available data. A few generative AI techniques at this stage include interpolation and extrapolation, which means creating synthetic data as per the statistic models. Data synthesis is a broad concept that constitutes methods to create new data and is not limited to merely resampling. Gen AI models like generative adversarial networks and variational autoencoders can synthesize data samples from the curated data. Nonetheless, it should be ensured that the data reflects the real-world annotations. Feature engineering and selection This is a critical stage as the data collected must go through sifting, where the raw data transforms into a standard format appropriate for training gen AI models and contribute to visionary performance. For example, the data for drug development should undergo changing biological sequences for numerical embeddings, encode chemical structures, and extract information from clinical data. Some of the techniques involved at this stage are normalization, dimension reduction, and selection for computational efficiency. Moderation and model building Life sciences data for gen AI should be validated and facilitated for model training. This step involves adhering to quality based on accuracy and reliability for AI models. Conducting experiments, validating datasets, and checking for model robustness are a few more steps to assess the performance of gen AI models. The approach begins with a base model and then passes through layers of SFT (Supervised Dine Tuning), RLHF (Reinforcement Learning from Human Feedback), and Proximal Policy Optimizations. Another crucial aspect of model building is moderation, which helps to generate relevant data by eliminating socially irresponsible answers. SME verification Finally, Subject Matter Experts (SMEs) are required to verify the final data samples and ensure it aligns with drug discovery and biological plausibility. Adding a human element is necessary to validate the gen AI responses and test the data quality. Some other measures like implementing control mechanisms and data governance are critical to maintain reliability and integrity. Conclusion In the era of AI-driven world, the potential of data readiness in leveraging pharma organizations should not be overlooked. From enhancing drug discovery processes to clinical trials, and coming up with unparalleled marketing strategies, gen AI applications have the potential to energize the pharma organizations. Adhering to meticulous data preparation through advanced practices and accelerating pharma organizations to use gen AI’s full potential and result in breakthroughs and innovation in the healthcare and drug development industry. The future is gen AI and i2e Consulting can help you prepare for it. Our data scientists are experts in preparing data for gen AI models. We can also advise on implementing control mechanisms and data governance practices.

How technology can fill gaps within resource management for overall PPM success

Resource management for PPM success Resource management is often the most overlooked aspect of R&D portfolio and project management. Despite its crucial role in ensuring project success, many organizations focus predominantly on project selection and prioritization, but neglect or defer addressing the complexities of allocating limited resources across multiple projects. This can lead to bottlenecks, delays and inefficiencies that not only undermine portfolio performance but also significantly impact time-to-market for new therapies. Research highlights that effective resource management and allocation can improve project success rates by up to 40%, underscoring their critical role in project portfolio success.McKinsey's report on strategic financial planning emphasizes that organizations which align their strategic financial plans with their resource allocation processes tend to outperform their peers. Companies that reallocate resources across business units within the year are significantly more likely to achieve superior revenue growth and return on capital In this blog, we will delve into how technical advancements can help resource and project managers have deeper visibility into named resource forecast, allocation, capacity and more. Leveraging technology for efficient resource management in life sciences Effective resource management is crucial, especially considering that large pharmaceutical companies typically manage hundreds of cross-functional and functional projects simultaneously across different therapeutic areas. With R&D spending in the industry averaging 15-20% of revenue, efficient allocation of both financial and human resources is key to portfolio success. Technology plays a vital role in effectively implementing any effective resource management. Life sciences organizations have an established ecosystem of tools and platforms which help to efficiently manage resources across all projects and portfolios. However, decisions on resource management capabilities are made through the lens of the enterprise, and typically do not provide the precision and granularity needed in the functional lines. In addition, data disparity and silos can still exist due to gaps in the tools in terms of visibility into named resource allocation, a clear idea on all auxiliary tasks assigned to the resources, etc. This can lead to insufficient visibility into resource allocation, task management, and coordination across teams. Introducing Alloc8With vast experience in PPM resource management for the life sciences, i2e Consulting developed Alloc8. It is a dedicated resource management tool that seamlessly integrates with your existing PPM ecosystem and focuses specifically on optimizing the use of human resources. Alloc8 is a unique tool which complements and extends existing enterprise tools by giving complete visibility of resource forecasts and allocation to line leads, department heads and product teams that require granular resource management data. It integrates resource data (required to make informed resource allocation) from various systems such as PPM tool (project schedules, resource needs and forecasts at the role level) HR system (employee skills, and name allocations), and Full Time Equivalents (FTEs). Alloc8 then extends visibility into named resource forecasts, allocation, and capacity. With intuitive features like task allocation, real-time progress tracking, and efficient coordination, Alloc8 empowers teams to optimize resource utilization, and enhance project efficiency. Let’s go deeper into how Alloc8 can help life sciences organizations take data-driven decisions when it comes to resource allocation. Features Complete view: The tool integrates and extends information from different sources and offers a comprehensive view of the number of resources, their project schedules, forecasts, full-time equivalents, and HR systems. Spreadsheets are replaced with the compiled data, offering visibility into the skill sets and resource capacity.Automated workflow and optimization: Project managers can manage workloads effectively by utilizing the resources based on their availability and skillset. The advanced automated resource requisition fosters a shared model, thereby reducing internal conflicts and avoiding under- and over-allocation. Improved alerting and issue management: Alloc8 shares alerts and flags, proactively surfacing potential areas of concern. If desired, it can automatically takes meetings-related information from Outlook to streamline the collection of time spent/planned on project related meetings to enhance the identification of capacity constraints.Tracking and interactive visuals: Alloc8 provides detailed reports, interactive visuals, and real-time tracking that enables necessary changes as per the project’s progress. This keeps the project’s stakeholders informed and monitors the workflow as per the demands.Dashboards: The tool displays the forecasted availability of the resources for multiple tasks that lets the managers identify gaps and bridge them to minimize risks. Additionally, it facilitates governance and lets the managers devise mitigation strategies. For more details, check out Alloc8Now that we understand how technology can contribute to improving the efficiency of resource management, let’s see what business outcomes organizations can reap from a streamlined, and visible resource management process. Improves productivity One major advantage of efficient resource allocation is the boost in productivity it brings to an organization. With proper distribution of resources, organizations make sure that every team or project gets the right tools, time, and support that is required to achieve the best outcomes. It also ensures that employees aren't wasting time looking manually for resources or waiting for approvals; instead, they can focus on their main tasks and duties. Furthermore, resource allocation highlights bottlenecks or overburdened areas, allowing for timely corrections.A well-implemented resource allocation strategy significantly enhances productivity and streamlines operations, resulting in greater efficiency and better outcomes for the whole organization. 2. Boosts decision-making Efficient resource allocation provides resource and project managers with a clear understanding of the available resources and their distribution. This transparency allows for informed choices based on accurate data. Strategic resource allocation helps optimize efficiency, productivity, and overall success. With a well-structured plan and efficient decision-making, businesses can achieve their goals more effectively and boost profitability. 3. Enhances financial performance Smart resource allocation can significantly improve a company's financial performance. By distributing resources wisely, organizations can maximize productivity and minimize waste and unnecessary expenses. Proper allocation ensures every department or project receives the support and funding needed to meet its goals efficiently. This leads to smoother operations, better efficiency, and higher profitability. Additionally, it helps identify overused or underused resources, allowing for redirection to areas where they are needed most. Refining resource allocation strategies enhances financial performance and drives sustainable growth. Resource management in life sciences management is crucial to support complex, multi-disciplinary and long duration projects. Life sciences organizations are on a continuous journey to make their processes efficient and automated. Tools like Alloc8 would seamlessly integrate into your existing PPM systems and fill any gaps.