- AI will generate 2.3m new jobs, and make 1.8m obsolete (Gartner)
- AI is one of the most important things AI is working on (Google CEO)
- The role of the project manager will likely be more important than ever, though the scope will change: focus will shift from tactical to strategic. (Oracle)
- 80% of PM roles will be eliminated by AI by 2030 (Gartner)
- Robotic Process Automation (RPA) software will threaten the work of 230m knowledge workers – this is 9% of the global workforce (Forrester)
- There is a big difference between data and information, but this is widely misunderstood.
- Intelligent usage is as important, if not more important, as the intelligence in the AI.
What is Artificial Intelligence?
- 4IR – Fourth Industrial Revolution
- AI – Artificial Intelligence
- ANN – Artificial Neural Networks
- IA – Intelligent Automation
- KBE – Knowledge Based Expert Systems
- ML – Machine Learning
- NLP – Natural Language Processing
- RPA – Robotic Process Automation
and provide some definitions, where they are clear to us:
- AI is a field of computer science dedicated to solving problems which otherwise require human intelligence, e.g. pattern recognition, learning, and generalisation.
- ML is a subset of artificial intelligence that uses statistical techniques to give computers the ability to learn from data without being explicitly programmed.
- Intelligence is widely considered to be Sensing, Thinking, Acting and Learning. AI technology now provides an opportunity to disrupt thinking with augmentation, as well as an opportunity to disrupt acting with Robotic Process Automation (RPA). Both are dependent upon data.
The stages of development for artificial intelligence are considered to be
- AI, and finally
AI can generally be implemented with 3 levels of automation:
- augmented, and
As such the evolution of AI in Project Management is most widely considered to follow this path:
- Integration & automation,
- Chatbot assistants,
- ML based project management (expanding project understanding and filling in the data gaps), and ultimately
- autonomous project management.
Artificial Intelligence in P3M (project, programme & portfolio management)
Portfolio management (and to a lesser degree project management) is about balancing risks & reward, but that means good estimates and risk assessments which is inherently hard. Furthermore PM tools are often complex, designed for specialists and don’t do enough to warn about potential problems. As such, there is a huge opportunity for AI to help make sure we pay attention to bad news, earlier.
For project managers, the potential for AI to reduce monotonous and time-consuming tasks that aren’t necessarily high value but show up in every project doesn’t just free up time, it also reduces errors. PMs could think of AI as the intelligent colleague which helps them with the mundane tasks and frees them up to think much more strategically.
2 Key Areas Where AI Can Help Project Management Communities
1. PM Assistance
- Scheduling/Planning (KBE, ANN, Fuzzy Logic)
- Record keeping
- Resource management
- Faster decision making
2. Predictive Analytics
- Project observations
- Performance understanding (avoid hope-based planning)
- Suggestions & recommendations
- Reduce costs and mistakes
- Risk estimation
Managing AI Transformations & Using AI in your organisation Today
We have (so far) found a limited range of tools which actively promote their AI functionality. These include:
- Aptage (Jira plug-in)
- NetSuite from Oracle (enhanced collaboration)
- Planisware (improved planning, chatbots, predictive estimates)
- Planview (collaboration)
- Stratejos (Slack assistants)
The biggest obstacle faced by many is access to sufficient data to make AI valuable. A number of initiatives are under way to gain open access to such data, with the construction industry somewhat leading the way.
Royal Institute Lectures (Chris Bishop on YouTube)
APM Projecting the Future Challenge Paper 4IR/AI
Forbes AI in Project Management