Developing an Approach to Using AI

Research by Dell’Acqua et al. (2023) on AI integration in professional settings reveals a fascinating phenomenon they call the “jagged technological frontier”, where AI’s capabilities vary dramatically across different tasks in sometimes surprising ways. Their study of 758 consultants at Boston Consulting Group demonstrated that thoughtful AI integration could increase productivity by 12.2% and improve work quality by over 40%, while misapplied AI could decrease performance by 19%. These findings are powerfully illustrated through the story of three interns, Sarah, Wei, and Priya, whose different approaches to AI integration mirror the patterns observed in the research. Just as Priya discovered through careful experimentation what the researchers would later quantify, the key to successful AI integration lies not in wholesale adoption or rejection, but in developing the wisdom to understand which tasks benefit from AI assistance and which require more direct human oversight. Like the high-performing consultants in the study, Priya’s “centaur” approach of strategically combining human and machine capabilities demonstrates how the future of professional work will likely depend on our ability to thoughtfully navigate this new technological frontier.

The concept of the “jagged technological frontier” provides a useful framework for understanding how to integrate AI effectively in professional settings. As we know from the research, this uneven distribution of AI capabilities across different task types creates both opportunities and challenges.
The Boston Consulting Group study’s findings about the 40% quality improvement and 12.2% productivity increase among consultants who used AI thoughtfully reinforce why strategic integration matters. These metrics, combined with the concerning 19% decrease in performance when AI was misapplied, highlight the importance of developing nuanced judgment about AI tool usage.

Consider how these findings manifest in our three interns’ approaches. Sarah’s indiscriminate AI use exemplifies the pitfalls identified in the research when professionals operate beyond AI’s capabilities. Wei’s complete avoidance of AI illustrates the opportunity cost of missing the documented productivity gains. Priya’s approach aligns with the successful integration patterns identified in the research, demonstrating how understanding AI’s capability boundaries leads to optimal outcomes.

The research’s identification of “Centaur” and “Cyborg” patterns provides valuable models for professional AI integration. Priya’s strategic task division exemplifies the Centaur approach, while the Cyborg model offers an alternative path for deeper AI integration. Both patterns, as we know, require thoughtful consideration of AI’s evolving capabilities.

The study’s finding that below-average performers improved by 43% while top performers gained 17% has significant implications for workforce development. This differential impact suggests that thoughtful AI integration can serve as a powerful levelling mechanism while still benefiting high performers.

These insights fundamentally reshape our understanding of professional AI integration. Rather than viewing AI adoption as a binary choice, the research demonstrates the importance of developing what we might call “AI literacy” or, the ability to navigate this jagged frontier effectively. Success, as both the research and Priya’s example show, comes from understanding AI as a tool for augmenting human capabilities rather than replacing them.

The Three Types of AI Workers

Think of choosing your AI integration approach like choosing a transportation method. A non-user is like someone who only walks everywhere. They maintain complete control and independence, but might struggle to keep pace in situations where faster travel is expected. The research shows that while non-users avoid certain ethical complications, they often fall behind productivity benchmarks that assume some level of technological integration.

A Centaur approach is like someone who drives for specific trips but walks for others, making deliberate choices about when each mode serves them best. The Harvard and Boston Consulting Group study highlights both the benefits and risks here. While Centaurs can effectively leverage AI for specific tasks, their confidence in their AI usage sometimes exceeds their actual skill, leading to that concerning 19% error rate Dell’Acqua et. al. (2023) identified.

The Cyborg approach is like someone who uses various vehicles for every journey, a car, bike, scooter, depending on the specific need. While this comprehensive integration can significantly boost efficiency, it comes with higher stakes. Just as relying entirely on vehicles might leave you stranded if they break down, Cyborgs can experience heightened stress when AI tools don’t perform as expected.

To make this decision, consider asking yourself:

• What does your work primarily involve? Different fields may benefit more from different approaches.
• How comfortable are you with technological change? Cyborg approaches require constant adaptation.
• What are the consequences of errors in your work? The Harvard/BCG study’s findings about Centaur error rates might be more concerning in some fields than others.
• How important is independent skill maintenance in your profession? Some fields require maintaining strong non-AI capabilities.

Using Artificial Intelligence Ethically in the Workplace

In today’s rapidly evolving workplace, the integration of artificial intelligence tools like ChatGPT has created both unprecedented opportunities and significant ethical challenges. While AI can dramatically enhance your efficiency and innovation, the manner in which organisations choose to implement and use these tools profoundly impacts their effectiveness and ethical implications. Their research identifies several key workplace applications where AI tools can provide meaningful support, including:

• data mining and analysis for research and operational insights
• language editing and enhancement of written communications
• generation of preliminary ideas and content drafts
• technical calculations and problem-solving in fields like engineering
• administrative task automation and streamlining
• real-time translation and cross-cultural communication support
• personalised learning content development for workplace training.

However, Griszbacher (2024) emphasises that the decision of how to approach AI implementation extends far beyond simply selecting appropriate software. It requires careful consideration of data privacy, bias mitigation, transparency, and the maintenance of human agency in decision-making processes. As AI becomes increasingly embedded in workplace operations, organisations must thoughtfully evaluate not just what AI tools to adopt, but how to implement them in ways that uphold professional integrity, protect employee wellbeing, and ensure responsible innovation. This makes the development of ethical AI usage frameworks a crucial priority for modern workplaces seeking to harness AI’s benefits while proactively addressing its risks.

As an intern entering a workplace, you’ll likely encounter various systems and policies governing the use of AI. Understanding these frameworks within your workplace is crucial for navigating your early career effectively. Most organisations now have structured approaches to AI implementation that directly affect how you’ll interact with these technologies in your daily work. For example, you might find that while AI helps analyse data or generate initial reports, your supervisor will always review AI-generated outputs with you, providing context and helping you understand how to interpret and use these insights effectively. This human oversight isn’t just a formality, it’s part of a carefully designed system ensuring that AI remains a tool to enhance your work rather than replace your professional judgment.

You’ll also discover that many organisations have what’s known as “social contracts” regarding AI usage, essentially, clear guidelines about how AI should and shouldn’t be used in the workplace (Wright & Schultz, 2018). These policies are particularly important to you because they protect your interests while you’re developing your professional expertise. For instance, if AI tools are used to track your early performance or suggest training opportunities, you’ll have the opportunity to discuss these assessments with your managers and provide input. Most organisations will provide training about their AI systems during your onboarding, explaining not just how to use these tools but also the ethical considerations behind them. You might even be invited to participate in AI ethics committees or feedback sessions, giving you a voice in how these technologies are implemented in your workplace. This involvement can be a valuable opportunity to develop both your technical and leadership skills early in your career.

Not every organisation has embraced artificial intelligence with open arms, and many organisations maintain restrictive policies around AI usage due to legitimate concerns about data security, privacy, and regulatory compliance. For interns entering these environments, understanding that these restrictions aren’t merely bureaucratic obstacles but often reflect serious considerations about protecting sensitive information, maintaining client confidentiality, and ensuring consistent quality control is crucial. Security concerns are particularly relevant in fields handling personal, financial, or proprietary data, where even seemingly harmless AI tools could potentially expose the organisation to significant risks (Jackson & Allen, 2024). Rather than viewing these policies as limitations, interns benefit from approaching them as important guardrails that protect both the organisation and its stakeholders.

Within these constraints, interns can still engage with AI ethically and professionally by focusing on approved tools and processes whilst maintaining transparent communication about their technology use. This involves being upfront with supervisors about AI tool preferences, clearly documenting any approved AI assistance usage, and ensuring all AI-supported work goes through appropriate review channels. For example, if a workplace allows AI for specific tasks like data analysis but prohibits it for client communications, respecting these boundaries absolutely whilst seeking opportunities to demonstrate the value of permitted AI use through careful documentation and quality results is essential. When uncertainty arises about whether an AI tool is appropriate for a particular task, seeking explicit guidance from supervisors rather than making assumptions represents the best approach. This commitment to transparency and respect for organisational policies helps build trust whilst positioning interns as professionals who can thoughtfully balance innovation with security requirements.

Working with Artificial Intelligence on Internship

The story of Priya, Wei, and Sarah at the beginning of the chapter illustrates a key insight that aligns with Industry 5.0’s emphasis on human-machine collaboration. Just as Industry 5.0 seeks to create “symbiotic relationships between humans and machines” (Gamberini & Pluchino, 2024, p. 6), successful interns need to develop human-centric connected systems. During your internship, you will have a unique opportunity to experiment with finding this balance in a supported environment. Gamberini & Pluchino emphasise that Industry 5.0 focuses on “merging the creativity and expertise of human experts with efficient and intelligent machines” (2024, p. 5) rather than replacing humans with automation. This is exactly what Priya demonstrated in her approach. She didn’t just use AI tools blindly like Sarah or reject them entirely like Wei, but rather developed a nuanced understanding of how to leverage AI’s strengths while applying her own critical thinking and professional judgment.

An internship provides an ideal testing ground for developing these crucial capabilities. When interns like Priya experiment thoughtfully with AI tools, they aren’t just learning to use specific technologies, they’re developing skills such as curiosity, resilience, flexibility, agility, and systems thinking. These are precisely the attributes that are essential for adapting to changes brought about by automation and digitalisation. By practising this balanced approach during your internships, you can begin building the professional capabilities you’ll need throughout your career in an increasingly AI-enabled workplace.

Selecting AI Platforms for Professional Development

When beginning your journey with AI tools, the first step is selecting appropriate platforms that align with your professional needs. While numerous AI platforms exist, it’s important to approach platform selection strategically rather than attempting to master every available tool. Each platform has distinct strengths and capabilities that make it suitable for different professional tasks.

Currently, several major platforms dominate the professional landscape. OpenAI’s ChatGPT provides broad capabilities in text generation and analysis. Microsoft’s Copilot excels in both text and image generation tasks, making it particularly valuable for creative and visual projects. Google’s Bard offers strong capabilities in current events analysis and data interpretation. Anthropic’s Claude demonstrates strength in detailed analysis and precise writing tasks, especially valuable for professional documentation and research.

Rather than attempting to become proficient with every available platform, consider developing expertise with two or three that complement each other and align with your professional needs. For instance, you might pair a platform strong in visual generation (like Microsoft Copilot) with one specialising in detailed writing and analysis (like Claude). This approach allows you to develop deep proficiency with specific tools rather than surface-level familiarity with many.

When selecting your platforms, consider these factors:

• the specific requirements of your professional field
• the types of tasks you most commonly need to accomplish
• the platform’s reliability and accuracy for your use cases
• the platform’s ability to integrate with your existing workflow
• the platform’s approach to data privacy and security.

Remember that platform selection is not a permanent decision. As you develop experience and as the technology evolves, you may adjust your choices based on changing needs and capabilities. The goal is to build a toolkit that enhances your professional capabilities while maintaining your judgment and expertise as the guiding force in your work.

Bringing AI into Your Daily Professional Practice

Think of learning to work with AI like learning a new language. Immersion is key to developing fluency. Instead of reserving AI tools only for complex or challenging tasks, incorporate them into your regular workflow for routine activities. This approach serves two important purposes: it helps you develop comfort and competency with the tools while also helping the AI learn to match your professional communication style and standards.

Consider starting with straightforward professional communications. When booking travel for conferences or meetings, use AI to draft your initial emails to hotels and airlines. While you could certainly write these emails yourself, having AI generate a first draft gives you opportunities to refine its output and teach it your preferred tone and style. For instance, if the AI’s draft is too formal or casual, you can adjust it and use similar prompts in the future with notes about your preferred formality level.

Professional documentation provides another excellent opportunity for AI collaboration. When working with spreadsheets, instead of immediately searching online for formula solutions, engage with AI to help construct them. This process not only helps you complete the immediate task but also builds your understanding of formula logic through the AI’s explanations. You might ask the AI to explain its thought process or suggest alternative approaches, turning each interaction into a learning opportunity.

Discussion forums and professional networking platforms offer particularly valuable opportunities to develop your AI collaboration skills. Rather than simply using AI to generate responses, engage with it as a thought partner. Share your initial ideas and ask the AI to help you refine them or consider different perspectives. This approach helps you maintain your authentic voice while leveraging AI’s ability to suggest enhancements or identify areas that might benefit from clarification.

The key is to view these everyday interactions as training opportunities, both for yourself and the AI. Each time you refine an AI-generated draft or adjust its tone to better match your professional style, you’re developing your ability to effectively collaborate with these tools while teaching them to better serve your needs. Think of it as developing a professional partnership rather than simply using a tool.

Building an Effective Partnership with AI

The key to productive AI collaboration lies in understanding that these systems are designed to engage in natural, conversational interactions. Just as clear communication and professional courtesy enhance human workplace relationships, they also improve the quality of AI interactions. When we frame our requests with clarity, context, and courtesy, we create conditions for more precise and useful AI responses.

Consider how you would approach a knowledgeable colleague for assistance. You wouldn’t simply demand information, you would provide context, explain your needs, and frame your request respectfully. The same principle applies to AI interactions. Instead of issuing abrupt commands like “Write hotel email,” take a moment to set the stage with context and specific parameters. For example: “I need to write a professional email to confirm my hotel booking for a business conference. The booking is for three nights starting July 15th at the Marriott downtown. Could you help me draft an email that’s courteous and includes all essential details?” This approach gives the AI the context it needs to generate more relevant and useful responses.

This principle extends beyond simple task requests. When working on complex projects, treat the AI as a collaborative partner by sharing your thought process and objectives. For example, when starting a data analysis project, you might say: “I’m analysing customer feedback data to identify trends in product satisfaction. Could you help me think through effective approaches to categorising this data? I’d particularly like to focus on identifying recurring themes in the comments.” By providing this level of context and engaging in a more collaborative dialogue, you enable the AI to provide more targeted and valuable assistance while maintaining your role as the decision-maker guiding the process.

Learning Together: The Feedback Loop in AI Collaboration

Effective AI collaboration requires developing a two-way learning relationship where feedback plays a crucial role in improving outcomes. Just as you would help a new colleague understand your preferences and standards, providing constructive feedback to AI helps refine its responses to better match your professional needs. This feedback process isn’t just about correcting mistakes, it’s about guiding the AI to better understand the nuances of your requirements and professional context.

Consider how feedback works in professional mentoring relationships: specific, constructive comments help people understand not just what to change, but why the change matters. The same principle applies when working with AI. Instead of simply accepting or rejecting outputs, engage in a dialogue about refinements. For example, if an AI generates a client report that’s technically accurate but too informal, you might say: “The analysis is thorough, but could you revise this using more professional language appropriate for senior executives? In particular, I’d like to see more industry-specific terminology and a more formal tone.” This specific guidance helps the AI understand the professional standards you’re aiming to meet.

Remember that feedback works best when it’s:

• specific about what works and what needs improvement
• constructive rather than simply critical
• focused on the desired outcome
• clear about the professional context and standards
• building toward better future interactions.

For instance, you might say: “The structure of this analysis is excellent, particularly how you’ve organised the key findings. However, could you enhance the recommendations section by including more specific, actionable steps? Our clients typically expect detailed implementation guidance.” This type of feedback not only improves the current output but helps the AI better understand your professional needs for future interactions.

By consistently providing thoughtful feedback, you help create a more effective working relationship with AI tools while developing your own skills in professional communication and quality assessment. This feedback loop becomes an integral part of your professional development, helping you refine both your use of AI tools and your ability to articulate professional standards and expectations.