Artificial intelligence (AI) has opened opportunities for greater predictive and historical insights into existing data. The economic value of these insights represents a trove of information to support risk mitigation associated with projects. Artificial intelligence is defined, for purpose of this research, as the ability of computer systems to perform human-like intelligence learning from examples, mimicking perception, learning and problem-solving decision capabilities of the human mind (IBM).
Project management’s ecosystem has seen exploratory research in areas like project selection in project portfolios and project disputes/resolution utilising AI, however, research into project lessons learnt to support decision-making remains limited. An opportunity presented itself to chat with project management professionals regarding the potential role of AI in project lessons learnt based on:
- Current thinking on the role of lessons learnt in project management
- How lessons learnt data are captured/disseminated
- The current challenges faced with lesson learnt implementation
- Insights into various perspective on how lessons learnt can be improved via adoption of AI-tools
By engaging with participants from a wide variety of sectors including transport, defence, higher education and nuclear we were able to draw some preliminary insights.
Collective insights from discussions
Project professionals identified lessons learnt (LL) as a key change activity. They also articulated concerns around poor change management practices and the need for LL to be viewed as a positive change driver informed by a consistent people change (cultural) process for facilitating discussions. Key terms identified across participants included: lip service, tick-boxes and a high degree of data informality regarding capture and structure. Participants also revealed there is a lack of common lessons learnt reporting frameworks.
Time also emerged as a common thread with comments such as: ‘giving people time to reflect’, ‘mandating time as a prerequisite of LL’ and ‘ensuring sufficient time as part of the capture process'. Participants also identified data capture process as an element of the review which was lacking. The need for better change management systems or solutions to support documentation and use of common-place technologies such as databases, excel spreadsheets, templates for capture vs more formal systems was also highlighted. Challenges faced within lessons learnt capture identified time to delve into the insights, people issues (e.g. LL as a blame games) and the need for mandating processes to improve LL robustness as key obstacles to improvement of processes.
Regarding AI’s role in project LL, project professionals highlighted these factors as key contributors to effective integration of AI-tool capabilities into projects:
- The value of AI-based approaches needing to provide core data analytic capabilities to demystify complexity
- Opportunities for removal of biases and less contentious data validation as a “single source of truth.”
- Ensuring a more collective process as it relates to knowledge sharing
- Supporting identification of questions adapted by AI for consideration and continually build on these LL questions sets
- An opportunity for immediate learning (real time insights)
- Supporting project managers interpretation of risk and benefits realisation
Professionals also expressed reservations regarding AI’s capabilities in relation to LL specifically:
- How much technology will help the lessons learnt process
- The need for creating the ‘right environment’ for AI-based approaches to aid lessons learnt
- Impact of AI integration on ways of working, dealing with various sources of data and the skills required to support AI-based project management
- Implications for job security based on potential AI-based project management capabilities
- The required rigour of security to protect valuable sources of data and its effect on the pace of adoption sector-wide.
AI bring significant challenges to existing infrastructure not the least of which centre around collection and analysis of disparate data, storage and employee technology readiness. Lesson learnt processes are no exception given non-standardised practices further stymied by sector heterogeneity limiting common formats. Project professionals articulation of time, people (culture) and processes on projects presents limitations to reflective thinking; a requisite for effective lessons learnt. Moreover, the need to support improved LL capture through training and a redefined managerial imperative to support proper documentation need further consideration to support AI’s potential as a tool within project management. Much is required of the profession to evolve people, processes and data, and inform wholesale adoption. Project professionals need to keep a keen eye both on the economic benefits of AI-based technologies as well as their organisation’s current data collection and analysis capabilities. Moreover, they need a clear baseline to start collective policy discussion regarding an AI way forward.
Further investigation of both the role and requisite capabilities of AI-based technologies is required to inform research and practice. As part of the APM’s contribution to the body of knowledge a forthcoming survey examining AI’s role and more specifically intelligent agents such as chatbots as a starting point is on the horizon. More detail will be announced shortly providing individuals with the opportunity to contribute. If you’re interested to know more, look out for our upcoming survey or contact me via email@example.com.
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