Boosting Value Performance Per Day (VPD) with AI

In project management, timely and effective decisions are critical to success. However, the traditional approach is filled with time-consuming tasks that prevent project managers from focusing on what truly matters—creating value. This is where the concept of Value Performance per Day (VPD) comes into play. VPD measures the amount of value a project manager can deliver in a day, directly impacting the project's success.

Typically, a project manager spends too much time on manual tasks to understand what happened in the previous week. This includes compiling status reports from team members, attending numerous status meetings, updating project plans, and manually entering data into various systems. After gathering all the necessary information, the project manager must then analyze it to identify variances and deviations from the plan. Only then can they begin to make informed decisions on how to realign the project and mitigate risks. As Albert Einstein once said, "The only source of knowledge is experience." In this context, the experience comes from meticulously sifting through data, a process that can consume 75-85% of a typical work week.

The true value of project management lies in the ability to identify issues, anticipate risks, and implement corrective actions swiftly. However, with most of the project manager's time spent gathering and reporting data, only a few hours are left for making meaningful decisions. This imbalance delays critical decisions and limits the project manager's ability to add value proactively. Winston Churchill aptly noted, "To improve is to change; to be perfect is to change often." The current state of project management demands a change that allows project managers to spend more time on decision-making and less on administrative tasks.

This is where Artificial Intelligence (AI) can significantly enhance VPD. By automating routine tasks such as data collection, report generation, and status updates, AI frees up a substantial amount of the project manager's time. For example, AI can integrate data from multiple systems like Jira, time-tracking tools, and project management software, providing a real-time, unified view of the project's status. This automation can reduce the time spent on status and reporting tasks to just 15-25% of the week, allowing project managers to devote 75-85% of their time to anticipating issues, mitigating risks, and creating value for the project.

Consider the story of Sarah, a project manager at a large tech firm. Before implementing AI, Sarah spent 65% of her week gathering data from Jira, updating her project plans in Microsoft Project, and preparing detailed status reports for her team and stakeholders. This left her with only 35% of her time to make critical decisions. After integrating an AI solution, Sarah's project management tools were seamlessly connected, and she received real-time updates. The AI analyzed project data, flagged potential risks, and even suggested corrective actions. With these tasks automated, Sarah now spends only 15% of her week on administrative duties. This allows her to dedicate 85% of her time to strategic decision-making, greatly increasing her VPD. As a result, Sarah identified and mitigated a significant risk early in the project, preventing a major delay and saving her company substantial resources.

Another example is John, a project manager in the healthcare industry. John used to spend 50% of every week manually tracking project progress and consolidating data from different departments, such as patient care, IT, and logistics. This left him with limited time to focus on high-value activities. After implementing AI, John's project management system automatically pulled data from various sources, provided real-time progress updates, and generated comprehensive reports. With these tasks automated, John now spends just 15% of his week on data gathering and reporting. The remaining 85% is spent optimizing patient care processes and improving resource allocation. This increased VPD resulted in faster project delivery and better patient outcomes.

To calculate VPD, consider the total value-added activities completed by the project manager in a day. This can be quantified by evaluating the impact of decisions made, issues resolved, and improvements implemented. For instance, if a project manager resolves three critical issues, makes two strategic decisions, and implements one process improvement in a day, each with a quantifiable value, these can be summed up to measure the total value delivered per day. By tracking this metric over time, organizations can gauge the effectiveness of their project managers and the impact of AI in enhancing their performance.

In conclusion, AI has the potential to revolutionize project management by maximizing VPD. By automating the time-consuming tasks of data gathering and reporting, AI allows project managers to focus on strategic decision-making. This shift not only improves project outcomes but also enables project managers to add greater value through proactive risk management and issue resolution. As we embrace this technology, we move closer to achieving the perfect balance in project management, where value is delivered swiftly and effectively.

 

Resource Management with AI: A Strategic Imperative for Project Success

Effective resource allocation is fundamental to the success of any project management endeavor. The strategic integration of Artificial Intelligence (AI) into resource management not only refines these processes but also maximizes the utility of every project component. By harnessing the power of AI, project managers are equipped to navigate complex project dynamics with unparalleled precision, substantially enhancing team performance and overall project outcomes.

AI significantly augments various facets of resource management:

• Dynamic Skill Matching: AI employs deep learning to meticulously analyze each team member's skills against past project performances, enabling precise alignment of project tasks with the most qualified personnel. For instance, in an intricate engineering project, AI could identify engineers who have demonstrated excellence in specific system integrations or troubleshooting, ensuring that critical project elements are managed by the most adept individuals.
• Predictive Resource Planning: Leveraging predictive analytics, AI forecasts the resource demands of future project stages, based on detailed analysis of project timelines and historical data. This capability allows for proactive adjustments in resource deployment, such as in the rollout of new technology platforms, where AI anticipates the need for additional technical support, facilitating smooth transitions and minimizing disruptions.
• Real-Time Resource Optimization: AI dynamically adjusts resource allocations in real time based on project feedback and external factors. During a major marketing initiative, for example, AI could shift resources among teams in response to real-time performance data, ensuring optimal utilization of personnel and maximizing campaign effectiveness.
• Balanced Workload Distribution: To prevent burnout and ensure equitable task distribution, AI continuously assesses workloads and redistributes tasks where necessary. This feature is crucial during phases of intense project development, where AI ensures that work is evenly distributed, maintaining high productivity and team morale.
• Strategic Team Formation: AI evaluates historical collaboration data to form teams that are likely to achieve high synergy. In global projects, AI might combine personnel from various departments and regions who have historically collaborated successfully, enhancing problem-solving capabilities and project execution.
• Automated Scheduling and Allocation: AI automates the complex task of scheduling, considering multiple variables such as project deadlines, individual availability, and priority, streamlining project logistics and ensuring timely completion of milestones.
• Enhancement of Team Skills Analysis: AI identifies potential skills shortages within teams and recommends targeted training or hiring strategies. This proactive approach ensures that teams are always equipped with the necessary skills to tackle current and future projects effectively.
• Performance-Based Resource Insights: By analyzing the impact of various resource allocation strategies, AI provides valuable insights that help refine future resource planning. This analysis might reveal, for example, optimal team compositions that consistently deliver superior results, guiding more strategic resource allocation in subsequent projects.

The integration of AI into resource management not only simplifies managerial tasks but also enriches strategic decision-making within project management. This innovative approach enables managers to plan more effectively, adapt swiftly to changes, and optimize resource utilization continually. The result is a more agile and responsive project management practice that not only meets but exceeds project goals and expectations.

Furthermore, employing AI for resource management shifts the focus of project management capacity toward higher-value work, moving away from mundane tasks. This shift is accomplished by improving the quality of resources on projects through better matches of skills needed versus availability. AI's role in resource management is pivotal in fostering an environment where project managers can focus on strategic initiatives and innovation, significantly contributing to the broader business objectives. This forward-thinking approach ensures that organizations remain competitive and capable of thriving in an increasingly complex project landscape.

AI Driven PM: Fulfilling the Promise of Lessons Learned

In the realm of project management, the perennial challenge has been not just to navigate the present but to learn from the past in a way that illuminates the path forward. Chapter 12 of my book "Project Management That Works" presents a narrative on risk assessment, a process traditionally encumbered by subjective judgments and cumbersome methodologies.  I presented a way to turn lessons learned into a risk assessment that could provide actionable insights.  It is here, at the intersection of aspiration and reality, that artificial intelligence (AI) and machine learning (ML) emerge not just as tools but as transformative forces, making the ideal of learning from past lessons a tangible, impactful reality.

AI and ML: The Vanguard of Realizing Lessons Learned

The essence of AI and ML in project risk management is their unparalleled ability to digest and synthesize vast datasets, encompassing both the successes and missteps of past projects, to offer actionable insights rather than mere classifications of risks. This marks a paradigm shift from the conventional practice of categorizing risks as high, medium, or low, towards a dynamic model where risks are not just identified but understood in the context of their historical outcomes and mitigated with precision.

Proof of Concept

In my book, I delve into how my project management team undertook the meticulous task of gathering, organizing, and analyzing insights gained from the past three years. The crucial element we sought was the impact of each lesson, be it in terms of time delays, costs, or other significant effects. To leverage these insights, we crafted questions aimed at new project managers embarking on projects, designed to identify potential risks early on. A positive response triggers a report with actionable advice for the project manager.

For example, a common issue is "vaporware," where a vendor offers a not-yet-complete solution, seeking customer investment for development. The risk assessment process includes questions like, "Have you seen a demo of the product?" Followed by, "Was the demo live, recorded, or a PowerPoint?" If "PowerPoint" is chosen, the report suggests the project manager verify the product's completion and user base directly with the vendor. A positive vendor response mitigates the risk; a negative prompts discussion with the project sponsor.

This method illustrates the power of applying past lessons to new projects. However, its effectiveness is tempered by the labor-intensive nature of maintaining and aligning the risk assessment tool with the organization's needs, requiring constant diligence and discipline.

Transforming Lessons Learned into Proactive Risk Management Strategies

1. Automated Compilation of Lessons Learned: Through AI, the exhaustive process of gathering and categorizing lessons from past projects is automated, ensuring a comprehensive repository of knowledge. This database becomes the bedrock upon which AI and ML build to forecast risks and recommend mitigation strategies.
2. Contextual Analysis and Prediction: ML algorithms, trained on historical project data, can predict the likelihood and impact of potential risks with a nuanced understanding of context. This approach transcends the binary nature of traditional risk analysis, offering a spectrum of insights that reflect the complex interplay of various project factors.
3. Customized Risk Mitigation Actions: By integrating lessons learned, AI-driven systems provide tailored risk response strategies that are both specific and actionable. Unlike the generic responses of yesteryear, these strategies are grounded in the empirical evidence of what has worked (or not) in the past.
4. Dynamic Adaptation to New Information: As projects progress, AI and ML continuously refine their predictions and recommendations based on real-time data, ensuring that the risk assessment is not a one-time exercise but a living process that evolves with the project.

Key Takeaways for Embracing AI and ML in Project Risk Management:

• From Reactive to Proactive: Leveraging AI and ML enables a shift from reacting to risks as they arise to anticipating and neutralizing them before they impact the project.
• Precision in Planning: The depth of analysis provided by AI and ML allows for more precise contingency planning, moving beyond arbitrary allocations of time and resources.
• Empirical Foundations for Decision Making: Decisions on risk mitigation are made with the confidence of empirical data, ensuring that the actions taken are proven most effective.

A Vision Realized Through Technology 

The integration of AI and ML into project risk management is not just an upgrade; it's a fulfillment of the long-held vision of truly learning from past projects. By turning the abstract into the actionable, AI and ML actualize the potential of lessons learned, offering a roadmap that is both informed by history and tailored to the unique contours of each new project.

In this new era, the words of George Santayana resonate with renewed significance: "Those who cannot remember the past are condemned to repeat it." With AI and ML, the past is not just remembered but becomes a guiding light, transforming risk management into a strategic advantage that propels projects towards success with the wisdom of experience as its compass.

The Future of PM: Metrics 2.0

Stepping into the future feels like opening a door to a realm where the once labor-intensive tasks of yesteryears transform into seamless, automated processes, thanks to the marvels of modern technology. When I penned "Metrics 2.0 - Data Rules All!" back in 2012, it was with a vision of a world where project management transcended beyond the mundane, manual compilation of data to a more strategic, data-driven approach that not only streamlined operations but significantly bolstered project outcomes and stakeholder satisfaction. The premise was simple yet revolutionary: to meticulously harness the power of project management metrics to unveil insights into resource participation, engagement, and project focus. Yet, the technology of the time posed a significant challenge, making the collection of these insights a daunting, error-prone task. Little did I know that in just over a decade, the advent of AI and the widespread adoption of collaborative platforms like Microsoft Teams would not only catch up to but also spectacularly exceed the aspirations laid out in that paper. Today, we stand on the brink of a new era where AI's prowess in automating the collection and analysis of project metrics is not just a possibility but a vibrant reality, heralding a new chapter in the annals of project management. Here is an excerpt from that paper: 

“The project manager wanted to select metrics that could be easily tracked and could prove or disprove the theory of proper involvement by each department.  Since the previous project managers were removed from the project for not involving the team appropriately, the project manager wanted to track the number of opportunities each team member had to be involved in the project and whether the team member took advantage of the opportunities.  However, with the amount of work already assigned to the project manager, the new metrics had to be clean and quickly accumulated to be effective.  As the project manager sifted through the various PMO forms, the answer became crystal clear.  Resource participation metrics were all around him.  They were always there and were often even filled out; however, they were not filled out in such a way that they could be collected to tell a story.  The metrics were found in the meeting agendas, meeting minutes, issue and risk logs, project plans, and e-mails.  The metrics the project manager decided to track were: 

From the Meeting Agenda and Meeting Minutes: 

• Number of times the resource was invited to a meeting 

• Number of times the resource attended the meeting 

• Meeting Participation Type (In Person, On Phone, Web) 

• Meeting Engagement Level (How engaged was the resource during the meeting.  This can be subjective, but necessary) 

 

From the Issue and Risk Logs: 

• Number of issues assigned to the resource 

• Number of risks assigned to the resource 

• Number of issues resolved by the resource 

• Number of risks resolved by the resource 

• Number of issues introduced by the resource 

• Number of risks introduced by the resource 

 

From the Project Plan 

• Number of tasks assigned to the resource 

• Number of tasks completed on time by the resource 

• Number of tasks past due assigned to the resource 

 

From these statistics, scores can be derived.  Each PMO or project manager may want to put personal touches to these formulas, however, for this case study, the following scores and formulas were used: 

Resource Participation Score (((Number of Meeting Invitations/Meetings Attended) *(Average Engagement Level) + (Issues Resolved-Issues Introduced) + (Risks Resolved-Risks Introduced)-Tasks Past Due) 

Resource Engagement Score (Resource Participation Score + ((Tasks Assigned + Tasks Completed)) 

Resource Project Focus Rating (Resource Participation Score / Resource Engagement Score) 

The scores and ratios did not provide as much value as having the percentage of meetings missed or understanding the number of issues that were being introduced versus being resolved.  What is always interesting in the collection of metrics is the identification of patterns and what is found during the collection phase.  The value of having the scores is the ability to set context or introduce the scores in a manner of factual basis.  “ 

While the metrics outlined were comprehensive, offering insights into resource participation, engagement, and project focus, the reality of the time was that these metrics were manually gathered—a process both time-consuming and prone to inaccuracies. 

Fast forward to today, and the landscape of technology has evolved dramatically. The advancements in AI and the widespread adoption of collaboration platforms like Microsoft Teams have revolutionized how we can approach these metrics, transforming what was once a manually intensive task into an automated, efficient, and precise process. 

AI's role in automating the collection and analysis of project metrics cannot be understated. Microsoft Teams already knows who was invited, who attended, how much he or she participated, and much more.  Connecting the dots to project plans, financial information, and many new possibilities now only relies on the imagination to create the connection.  This automation liberates project managers from the drudgery of data compilation, allowing them to dedicate more time to strategic decision-making and fostering team dynamics. 

Microsoft Teams, augmented with AI, becomes more than just a platform for communication and collaboration; it evolves into a central nervous system for projects. It proactively manages tasks and deadlines, identifies risks based on conversational trends, and even suggests interventions drawing from historical data. Such proactive measures ensure a project environment that is not only efficient but also adaptive to the needs and working styles of individual team members. 

The potential for AI within Microsoft Teams to tailor project management practices to enhance productivity and engagement is immense. For instance, determining optimal meeting times based on productivity patterns, recommending resources for current issues based on past solutions, and predicting project risks from communication patterns are just the tip of the iceberg.  Here are some ideas of where metrics could go: 

• Active Participation Index: This metric could analyze the active contributions of team members in meetings and discussions, distinguishing between mere attendance and meaningful participation. By evaluating the frequency and relevance of contributions in chat discussions, comments on shared documents, and vocal contributions in virtual meetings, AI can provide a nuanced view of each member's engagement. 

• Collaboration Efficiency Score: Leveraging AI to analyze the interaction patterns within Microsoft Teams, this metric assesses how effectively team members collaborate on shared tasks and documents. It considers the time taken from task initiation to completion, frequency of collaborative editing sessions, and synchronicity in task handling. 

• Innovation Quotient: By examining the novelty and diversity of ideas proposed in project chats and documents, AI can score teams on innovation. This metric involves semantic analysis to identify unique solutions and creative problem-solving approaches, emphasizing the quality of contributions over quantity. 

• Consensus Building Efficiency: This metric evaluates how quickly and effectively a team reaches consensus on project decisions. Using AI to analyze meeting transcripts and discussion threads, it identifies key decision points, tracks the evolution of discussion towards consensus, and measures the time taken to resolve disputes. 

• Emotional Intelligence Indicator: Through sentiment analysis of communications within Microsoft Teams, AI can gauge the emotional tone of interactions, providing insights into team morale, stress levels, and overall project atmosphere. This metric helps identify periods of high stress or conflict, allowing for timely interventions. 

• Skill Adaptability Score: By tracking the types of tasks assigned to and completed by team members, AI can assess individuals' ability to adapt to different roles and responsibilities. This metric identifies not only versatility but also the willingness to step outside one's comfort zone, highlighting potential leaders and highly adaptable team members. 

• Network Connectivity Score: This metric analyzes the communication flow between team members, identifying central figures in the project's communication network and potential bottlenecks. It highlights how well information is shared across the team and can indicate isolated members or subgroups within the project. 

• Predictive Project Health Indicator: Combining various data points from project interactions, timelines, and deliverables, AI can forecast potential risks and issues before they become critical. This predictive metric evaluates current project status against historical data to identify patterns that may indicate future project health issues. 

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These advanced metrics, powered by AI's ability to analyze vast amounts of data in real-time, offer project managers a deeper, more actionable understanding of their team's dynamics, efficiency, and overall project trajectory. Integrating such metrics into project management practices can significantly enhance decision-making, project outcomes, and team satisfaction. 

Reflecting on the famous words of Albert Einstein, "The measure of intelligence is the ability to change," it is clear that the project management field is undergoing a significant transformation. The integration of AI and tools like Microsoft Teams embodies this intelligence, automating and refining the process of monitoring resource metrics initially laid out in 2012. This shift is not just about keeping pace with technological advancements but about leading the charge towards a more agile, responsive, and efficient project management future. 

In essence, the vision of "Metrics 2.0 - Data Rules All!" is finally being realized, thanks to the technological leaps in AI and collaborative platforms like Microsoft Teams. The once laborious task of metric collection has been streamlined, making the goal of insightful, data-driven project management not just an aspiration but a practical reality. This evolution from manual to automated processes signifies a pivotal moment in project management, paving the way for a future where focus can shift from data collection to strategic innovation and leadership.