How to Utilize AI in Order to Support Project- and Program Development
Sven R. Bernert
Technology | Strategy | AI Business Integration | New Leadership and Change Advocate | Asset Advisory
Disclaimer: This article series – including the latest postings to my profile – was and is continuously created a) as a learning tool from more than 30 years in leadership- and consulting roles and the many challenges we came across during this time. In many different fields of leadership, technology and business organization, just to name a view. And b) as an outlook into our technological and economic future using emerging technologies (like AI) in order to support and enhance productivity, to speed-up processes and to free (human) resources from standard tasks.
How Can Artificial Intelligence Be Implemented to Support Project or Program Development and What Tools Are Available to Achieve Best Results? Distinguished Between Defining a Project and Continuously Monitoring a Project.
AI can significantly enhance both?project setup?and?project monitoring?in project or program development by streamlining tasks, improving decision-making, and increasing efficiency. The implementation of AI in these areas involves different approaches and tools tailored to the distinct phases of project management. Let's explore how AI can be implemented in both areas, along with the tools available for achieving optimal results.
1. How To Implement AI Tools For Project Definition and Setup
In this phase, AI can be used to assist in planning, resource allocation, risk identification, and generating detailed project timelines. Here's how AI supports the project setup process:
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a. Project Planning and Design
AI tools can analyze large datasets, historical project performance, and other contextual factors to suggest optimal plans. These tools help to:
b. Risk Identification and Mitigation
AI algorithms analyze project scope, schedule, and historical data to identify potential risks early. Machine learning models can flag risks that may not be apparent during initial human review by recognizing patterns that led to past project failures.
c. Budget Estimation and Cost Prediction
AI can predict accurate cost estimations based on market trends, labor costs, and material availability. Tools that leverage AI can generate cost breakdowns and identify budget overruns before they occur.
d. Task Automation and Scheduling
AI can automate repetitive tasks like scheduling meetings, setting up communication workflows, and distributing tasks among team members. Predictive analytics can forecast the duration of tasks based on past projects, optimizing the timeline accordingly.
e. AI Tools for Project Setup (as of September 2024):
Trello (with Butler Automation):?Uses AI to automate workflows, task assignment, and trigger actions based on project conditions.
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Microsoft Project (with AI insights):?Uses machine learning to forecast resource utilization, track deliverables, and estimate timelines. There's a comprehensive article on MS Project and AI right here at LinkedIn, published by Kavitha Udaya Kumar
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Smartsheet:?Offers AI-powered features for project planning and automating repetitive tasks like scheduling and reporting.
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Wrike:?Uses AI to recommend task assignments, predict risk factors, and provide resource allocation insights.
2. How To Implement AI Tools For Project Monitoring
Project monitoring involves tracking the progress of a project, identifying deviations, and ensuring that deliverables meet quality and time standards. AI helps in real-time project monitoring, anomaly detection, and predictive analysis for ongoing projects.
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a. Real-Time Data Analytics and Performance Tracking
AI-powered dashboards can track project metrics in real time, monitoring Key Performance Indicators (KPIs) like budget variance, task completion rates, and team performance. These insights are visualized to offer a comprehensive project health overview.
b. Predictive Analysis for Delays and Bottlenecks
AI can predict project delays by analyzing current progress against the project plan. If certain tasks are lagging, machine learning algorithms flag potential bottlenecks, enabling the project manager to intervene early.
c. Anomaly Detection and Risk Alerts
AI models can detect anomalies in project data, such as sudden budget increases or unexpected task delays. Natural language processing (NLP) can also help monitor communication patterns (emails, chats) to detect miscommunication, low morale, or conflicts that may impact project performance.
d. Resource and Task Optimization
AI algorithms analyze task distribution, work hours, and team availability in real time to ensure resources are used efficiently. It can suggest reassigning tasks or rebalancing workloads if inefficiencies are detected.
e. Feedback Loop and Continuous Improvement
AI can analyze feedback from project stakeholders and team members to identify areas for continuous improvement. Sentiment analysis, for example, can assess project feedback and gauge satisfaction levels, suggesting areas that need adjustment.
f. AI Tools for Project Monitoring (as of September 2024):
Asana (with AI features):?Provides AI-powered analytics to track project progress, visualize dependencies, and predict task completion rates. Especially for corporate size organizations.
Monday.com:?Uses AI for real-time project tracking, alerting project managers about potential risks, and offering performance insights.
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Planview (former Clarizen):?AI-driven features that track project KPIs, forecast completion rates, and automatically adjust resources based on project demands. Value Stream Management for different business setups in general.
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Jira (with AI add-ons like Forecast):?Leverages machine learning to predict sprint completion, track team progress, and identify blockers.
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Microsoft Project w/Copilot
There's a comprehensive article on MS Project and AI right here at LinkedIn, published by Kavitha Udaya Kumar
3. Key Differences Between AI in Project Definition and Monitoring
4. Final Thoughts
AI can vastly improve both project setup and monitoring by providing data-driven insights, automating repetitive tasks, and enabling predictive analytics. By implementing AI effectively, project managers can reduce the likelihood of project failure, improve resource management, and ensure timely project delivery. The tools mentioned offer diverse functionalities that cater to different phases of project management, making them highly adaptable to the specific needs of any project.