Recently, I came across a paper on the labour market impacts of large language models (LLMs) by Eloundou et al. (2023). Naturally, my mind wandered to what this means for those of us in research roles within Higher Education Institutions.

Spoiler alert: our jobs will be affected.

The real question is how – through automation, augmentation, or both?

Here are some eye-opening findings from the study:

• 80% of the U.S. workforce could see at least 10% of their tasks impacted by LLMs, with 19% experiencing more significant changes (50% of their tasks).
• About 15% of all worker tasks could be completed significantly faster with LLMs alone. When LLM-powered tools and software are included, this figure jumps to 47–56%.
• Information-processing industries will see the biggest changes, while fields like manufacturing, agriculture, and mining will feel fewer effects.

For research professionals, these stats signal both challenges and opportunities. Let’s explore what automation and augmentation mean for our work and how these forces might shape the skills we need to thrive.

Automation vs. Augmentation: What’s the Difference?

Let’s break it down.

Automation is about delegating tasks entirely to machines or software, with minimal human intervention. The aim? To handle repetitive, rule-based processes with precision and speed, freeing humans from the mundane. Think generating project spending reports or processing invoices – routine, predictable, and perfect for automation.

Augmentation, on the other hand, uses technology to amplify human capabilities. It doesn’t replace us; it empowers us, especially in tasks that require judgment, creativity, and expertise. For example, an AI tool can analyse complex datasets, but it’s still up to us to interpret the findings and make strategic decisions.

Do Automation and Augmentation Work Together?

Absolutely. In fact, they are like peanut butter and jelly – distinct but better together. When used wisely, they complement each other to reshape workflows. Here is how:

1. Automation Frees Time for Augmentation
   Automating repetitive tasks gives us more time for strategic, creative, or high-value work. For instance, AI can handle meeting scheduling or citation management, freeing up to focus on grant writing or hypothesis generation.
2. Tasks Often Have Both Elements
   Many tasks involve a blend of automation and augmentation. Take drafting a research paper: AI can produce the first draft (automation), but the researcher refines the arguments, tone, and accuracy (augmentation).
3. Evolving Roles
   As automation takes over routine tasks, our roles evolve toward higher-order responsibilities like strategic planning, innovation, and leadership.

What Tasks Could Be Automated or Augmented in Research?

Let’s get specific. Here is how automation and augmentation could reshape the research landscape:

Automated Tasks

These involve repetition, clear rules, or data-heavy processes. Examples include:

• Data Processing: Cleaning, organising, and preparing datasets.
• Literature Reviews: Summarising large volumes of research papers.
• Basic Statistical Analysis: Generating summary statistics.
• Writing Drafts: Creating initial drafts of reports or proposals.
• Compliance Checks: Ensuring adherence to regulations.
• Document Management: Automating archiving and retrieval.

Augmented Tasks

These require human expertise, but AI can enhance their efficiency and quality. Examples include:

• Hypothesis Generation: Using AI to identify research gaps or suggest novel approaches.
• Complex Data Analysis: Leveraging AI for advanced statistical modelling.
• Collaborative Writing: Refining clarity, coherence, and style in manuscripts with AI support.
• Strategic Planning: Analysing funding trends to prioritise resources effectively.
• Stakeholder Engagement: Personalising communication using AI insights.
• Risk Management: Predicting project risks based on historical data and trends.

How Do We Stay Relevant in This New Landscape?

The reality is clear: resisting change isn’t an option. Here is how to future-proof your career and thrive:

1. Master the Art of AI Collaboration
   Learn to work alongside AI. The GenAI tools are here to stay. Embrace them and make them part of your daily workflow.
2. Cultivate Uniquely Human Skills
   Hone your critical thinking, creativity, and problem-solving abilities – skills that AI can’t replicate. Don’t forget the power of empathy, leadership, and collaboration.
3. Commit to Lifelong Learning
   The pace of change is relentless. Stay ahead by taking courses on AI, data science, or research project management. Platforms like Coursera and LinkedIn Learning are great places to start.
4. Focus on Strategic Roles
   Transition from routine tasks to roles that emphasise planning, innovation, and oversight. Become the go-to person for big-picture thinking.
5. Adopt a Growth Mindset
   Instead of fearing AI, view it as a tool to expand your potential. Ask yourself: How can AI help me work smarter, not harder?

The Future of Jobs in Research

Automation and augmentation aren’t just buzzwords – they are transformative forces reshaping the research landscape. By automating routine tasks and augmenting creative and complex work, we can achieve unprecedented levels of efficiency and innovation.

The key is adaptability. Embrace AI, nurture human-centric skills, and keep learning. The future isn’t about competing with AI – it is about leveraging it to redefine what is possible.

So, sharpen your saw, step up your game, and get ready to thrive. The future is here, and it’s time to make it work for us.