Generative AI is transforming how projects are managed. As project managers, it’s important to understand what generative AI is, how it works, and how it can impact your role. This overview covers the key things project managers should know.
What is Generative AI?
Generative AI refers to AI systems that can generate new content, like text, images, audio, and more that is original, high-quality, and relevant to the given input or prompt. The most popular examples right now are generative text models like ChatGPT that can generate human-like text.
The key abilities of generative AI systems are:
- Producing novel & creative output based on the given input
- Mimicking human style, tone, and knowledge
- Continually learning and adapting from real-world data
Unlike AI assistants designed for narrow tasks, generative AI aims to be multipurpose and capable across different domains and use cases. The potential is vast.
How Does Generative AI Work?
Generative AI leverages machine learning (ML) and especially a technique called neural networks. Very simply, these AI models are first trained on huge datasets like articles, books, conversations, code, and more to discern patterns about how we use language and communicate ideas in the world.
Through this training, the models build an understanding of concepts and context. By learning all these patterns, the models gain the ability to statistically generate their own novel outputs that are remarkably human because they are produced using the same structures and logic patterns we as humans use to communicate. The latest models use an architecture called transformers that are particularly adept at language tasks because they incorporate attention mechanisms to focus on the most relevant parts of their training data. They keep building on previous models’ foundations to grow in scope, accuracy and capability.
Current State of Generative AI
We are currently witnessing explosive growth in generative AI:
- Rapid pace of progress – New models are being introduced and improved at an incredible rate with bigger training datasets and algorithms. 6-18 month improvement cycle.
- Increasing capabilities – Models can write, summarize, translate, code and more across diverse topics and domains. Quality and coherence of outputs increasing dramatically.
- Mass adoption building – Generative AI is transitioning from research labs to the mainstream with easy-to-use applications. Heavy investment pouring into the space.
However, some key limitations still remain around accuracy, factual consistency, harmful bias and more. While early excitement is justified, responsible development is crucial.
Benefits of Generative AI for Project Managers
Here are some of the key ways properly implemented generative AI could benefit project managers:
Streamlining Documentation and Reporting
- Automating status reports – AI can auto-generate update summaries from existing data
- Document drafting – AI can create first drafts of specs, plans, etc. to be refined by PMs
- Meeting note taking – AI can listen into project meetings and produce key takeaways
This allows Project Managers to focus their time on strategy and execution rather than manual documentation.
Augmenting Communication
- Writing assistance – AI can help draft emails, memos, and other communications using key details provided by PMs. This saves time while allowing PMs to closely review anything outward facing.
- Translation – Instantly translate communications and docs across multiple languages to smooth collaboration across global teams.
Enhancing Research & Discovery
- Market research – AI can rapidly analyze & summarize customer needs, trends & feedback from large datasets.
- Tech research – AI can connect PMs to relevant research papers, tools, methods etc. and provide useful summaries and insights to incorporate into planning.
This amplifies PMs ability to deeply understand the domains they operate in.
Aiding Data-Driven Decisions
- Data analysis – AI can process and highlight insights from the growing amount of project data.
- Metrics tracking – AI can automatically track KPIs and flag changes and anomalies to PM attention.
- Risk management – AI can assess costs, dependencies etc. to detect project risks early.
- Recommendations – AI can look at project trajectory and give data-backed recommendations on optimal next steps.
Together this augments PMs abilities to take proactive, data-driven actions to keep projects on track based on hard evidence vs gut instinct alone.
Personal Assistant for Tasks & Follow-ups
- Email triage – AI can provide a summary of the most time sensitive and important emails and meetings to focus on.
- Task follow-ups – AI can track all open tasks and commitments and nudge when pending responses etc.
- Calendar prioritization – AI can automatically shuffle schedules based on priorities if delays or conflicts come up.
This enables PMs to stay effortlessly on top of critical action items amidst the daily chaos.
Risks of Generative AI for Project Managers
Despite the promise, there are risks PMs should mitigate when leveraging generative AI:
Inaccurate or Misleading Outputs
The AI could produce false, biased or nonsensical outputs if not carefully monitored and corrected. This can negatively impact decisions. PMs (Project Managers) must vigilantly review outputs, especially those communicated externally.
Overreliance Leading to Deskilling
If PMs get overdependent on AI for core skills like communication, analysis and planning, it may erode competencies over time. Strike a balance between automation and active skill application.
Confidential Data Exposure
If proprietary data like finances, trade secrets, or customer info is input into public AI models, it poses serious confidentiality issues. Vet security practices of tools used.
Mounting Technical Debt
If AI systems are not well documented and maintained, explaining outputs or improving the tools over time becomes difficult, reducing reliability.
Best Practices for Using Generative AI
To harness generative AI safely and effectively, project managers should:
- Approach with sound goals – Be selective and tactical vs allowing proliferation across all areas. Solve high value problems.
- Phase incrementally – Start small, test impacts, and build internal buy-in before expanding to mission critical uses.
- Establish human review – Rigorously inspect AI outputs with skepticism before use and correct inaccuracies.
- Augment talent judiciously– Keep staff skills sharp for tasks that still require human discretion like leadership and stakeholder interactions.
- Audit for issues – Actively monitor for inaccuracies, bias issues and limitations and retrain models to address them.
- Create transparency – Document when and how AI is used within projects for visibility and to retain organizational knowledge.
The Future of AI in Project Management
Here are two fascinating directions generative AI may head with regards to steering projects:
AI-Assisted Planning & Forecasting
Leveraging computational power for simulations, generative AI could rapidly model and compare multiple project plans and staffing models to predict optimal timelines, budgets resource allocations. This would amplify a PM’s ability to create resilient roadmaps proactively vs through simple guesstimates.
AI Team Members
We may see AI bots added to project teams that can autonomously handle assigned tasks like data collection, documentation, testing based on parameters set by PMs and team. This could allow teams to scale efficiently and provide PMs extra visibility. The feasibility and adoption horizon for this is less clear but intriguing to consider. While enticing, it’s wise to temper expectations, establish ethical foundations for these technologies within teams and organizations, and allow the practical strengths of both human minds and AI systems to harmoniously elevate future project execution.
Conclusion
The recent leaps in generative AI mark the advent of a new era that will transform many fields, including project management. As these models continue evolving in ability, they will open up many opportunities to amplify communication, productivity, insight and foresight for delivering robust initiatives. However, as with any powerful technology, mindful governance and diligent oversight will be key to addressing risks like inaccurate outputs, overreliance leading to deskilled labour, and technical debt accumulating. With prudent guidance, this technology can usher in new heights of efficiency, innovation, and stakeholder delight for PMs. Rather than a threat that replaces human jobs and skills, properly directed AI promises to augment existing talent and priorities. But leaders must proactively ready processes, data pipelines, model integrations and most crucially team mindsets within their organizations today to capitalize on this imminent transformation.
FAQs
Q: How accurate or truthful is generative AI currently?
A: The accuracy varies across models but is rapidly improving. Leading models still have an imperfect factual grounding that requires human editing before external use or high stakes decisions. But AI commentators note integrity is rising with larger training datasets and techniques that improve factual consistency and truthful intent.
Q: Can I use generative AI to replace my current virtual assistants?
A: Not yet directly. Most current digital assistants are narrowly focused while today’s top generative AI models remain relatively broad in capability but shallow in domain expertise. However, pre-trained models can be customized by training on company data to potentially match and enhance human assistants over time.
Q: What ethical precautions should I take when implementing generative AI?
A: Thoroughly audit for biases, inaccuracies and context dropping by analyzing outputs across use cases. Maintain responsible data practices and security. Allow users to identify AI written text and provide feedback. Guide cultural adoption and mitigate labor concerns. Ensure transparency in AI tools’ provenance, training process and limitations.
Q: How can I get started with piloting generative AI safely as a project manager?
A: Start by using vendor-provided models that have ethical safeguards rather than downloading open source models you fully control. Test on low-stakes applications like draft document creation vs client communications. Establish reviews before use and get user feedback. Scope a focused trial before expanding approvals and access.
Q: What should project managers focus on if generative AI causes job impacts down the line?
A: First, proactively lead retraining initiatives for displaced staff to learn complementary skills like user needs analysis, creative ideation, data wrangling, model training and communication where human judgment still delivers value. Second, emphasize experience design, strategy and stakeholder interactions for PMs as AI improves codified task efficiency. Focus on the irreducible human element.
