How to Balance AI Automation with Developer Skills

I have been thinking about the co-pilot vs autonomous agent branding of AI capabilities lately and finally had critical thought mass to put my ramblings into words. As AI capabilities have grown, there are two contrasting emerging perspectives on how it can impact the future of work. One view is the "auto-pilot" model where AI increasingly automates and replaces human tasks(eg. Devin). The other is the "co-pilot" model where AI acts as an intelligent assistant, supporting and enhancing human efforts. Personally, the co-pilot approach seems more promising, at least with AI's current level of development & intelligence. While highly capable, today's AI still lacks the nuanced judgment, high-level reasoning, and rich context that humans possess. Fully automating complex knowledge work could mean losing those valuable human strengths. On a Psychological level, the co-pilot model keeps humans involved. It allows us to focus on aspects of our work that require creativity, strategic thinking, emotional intelligence and other distinctly human skills. It also preserves the key psychological needs derived from work - autonomy, mastery and purpose. The co-pilot model maintains human agency while providing efficiency gains at the same time. I have been observing products that are taking this co-pilot centric approach. One key and contrarian observation from these is that from a design perspective, AI assistance works better when users can opt out of specific automations, rather than being forced to automate everything. Rather than asking "what do you want automated?", ask: "what do you NOT want automated?" This puts control in the hands of the human for how AI lends a hand. At this point, this co-pilot approach of combining human and AI capabilities is not just an abstract concept - it is being operationalized into the foundations of AI developer frameworks and tooling. For example, Langchain has an "agentic" component called Langgraph that includes an "interrupt_before" functionality. This allows the AI agent to defer back to the human when it is unable to fully accomplish a task on its own. The developers recognize that AI agents can be unreliable, so enabling this hand-off to a human co-pilot is critical. Similarly, Langgraph provides functionality to require human approval before executing certain actions. This oversight allows humans to verify that the AI's activities are running as intended before they take effect. By building in these human-in-the-loop capabilities at the foundational level, developer frameworks are acknowledging the importance of the co-pilot model. I seem to use more products that assist me using embedded AI layers rather than promise me completely autonomous task completion, only to massively under-perform and lead to incorrect outcomes - What about you?

“The robots are great at processing text, but they’re terrible at having coffee with a nervous author.” I shared that line in a recent talk with editors, and it stuck. Because it’s true. AI is changing how we work, but it can’t replicate what makes us human. In 2025, AI is a regular part of publishing and content production. It’s helping teams brainstorm, draft, edit, illustrate. The shift isn’t coming—it’s already here. So what does that mean for editors? It means our role is evolving. For some of us, editing now looks like guiding an AI through a task and checking its work, rather than manually pushing commas around a document. And yes, many editors are being asked to use AI tools daily. To move faster. To do more. But here’s what I reminded that room of editors: Change is not new. We’ve been automating editing for decades. Spell check went mainstream in the ‘80s. Grammar checkers and Word macros followed in the ‘90s. AI is just the next step in that evolution. So how do we stay relevant? We lean into the thing AI can’t do: be human. 📍 Make humanity your asset. Focus on your “people skills" like empathy, coaching, and face-to-face communication. Look for ways to increase human connection in your work. 📍 Become the person who knows AI. Be the one who teaches your team how to use it well. Test tools, improve workflows, and share what you learn. If AI saves your team time and money, you may have just covered your own salary. 📍 Expand your range. The editor who also understands AI search, is an SME, or leads a team? That person is harder to replace than someone who only knows style guides. 📍 Stay in the loop. Wharton Professor Ethan Mollick calls it “Human in the Loop.” At every stage of AI use, humans need to be involved—reviewing, guiding, checking for accuracy. (We’ve all seen The Terminator. We know what happens if we skip that step.) AI can help us move faster and do more, but it still needs us. Your judgment. Your people skills. Your coffee chats with nervous authors. Our humanity is the future of editing. Let’s lean into it.

With the advent of generative AI there’s been a lot of discussion about the role of “human in the loop” (HITL) models. At Mineral, we’ve been doing work in this area, and I’m often asked how long we think HITL will be relevant. So I thought I’d share a few thoughts here. HITL is not a new concept. It was originally coined in the field of military aviation and aerospace, and referred to the integration of human decision-making into automated systems. Today, it’s expanded to be a cornerstone in the AI discussion, particularly in fields like ours — HR & Compliance — where trust and accuracy matters. At its core, HITL is a design philosophy that involves human intelligence at critical stages of AI operations. It's not just about having a person oversee AI; it's about creating a collaborative environment where human expertise and AI's computational power work in tandem. HITL is a key part of our AI strategy at Mineral, and as we think about the value and longevity of HITL, we think about two distinct purposes it serves. The first is technical. Our domain is a complex arena – federal, state, and local regulation and compliance. As good as AI has become, our tests have shown that it’s still not capable of fully navigating this landscape, and is unlikely to get there soon. HITL plays a critical role in catching and correcting errors and ambiguities, and ensuring the accuracy of the output, so clients can rely on the guidance we give. The second is cultural. This aspect of HITL is both more intuitive and less understood. Even if AI is capable of providing correct information, HITL plays a critical role in establishing trust in a cultural sense. Think about the last time you went on an amusement park ride. Odds are a human operator tugged on your seatbelt to ensure it was fastened. There’s no technical reason why a human needs to do this work — a machine could do it better. But culturally we feel better knowing a human has confirmed we’re safe. The same is true in HR and compliance. Whether they’re starting from scratch or already have an instinct on how to proceed, clients often want confirmation from a human expert that they’re on the right track. In the world of AI, this cultural value of having a human in the loop is likely to extend beyond the technical value. So how long will HITL will be relevant? For a long time, and probably even past the point at which AI’s capabilities equal or surpass our own. As we continue to innovate, the importance of #HITL in areas like this is more evident than ever. It represents a balanced approach to AI, acknowledging that while AI can process data at an unprecedented scale, human insight, empathy, and ethics are irreplaceable. In this partnership, #AI amplifies our capabilities, and we guide it to make sure it serves the greater good. That’s a recipe for long-term success. I’d love to hear from you: how do you see human in the loop systems evolving?

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗲𝘅𝗽𝗲𝗿𝘁𝘀; 𝗶𝘁 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 𝘁𝗵𝗲𝗶𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲! 👉 It’s about harnessing AI to enhance our human capabilities, not replace them. 🙇♂️ Let me walk you through my realization. As a healthcare practitioner deeply involved in integrating AI into our systems, I've learned it's not about tech for tech's sake. It's about the synergy between human intelligence and artificial intelligence. Here’s how my perspective evolved after deploying Generative AI in various sectors: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞: "I 𝐧𝐞𝐞𝐝 AI to analyze complex patient data for personalized care." - But first, we must understand the unique healthcare challenges and data intricacies. 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧: "I 𝐧𝐞𝐞𝐝 AI to tailor learning to each student's needs." - Yet, identifying those needs requires human insight and empathy that AI alone can't provide. 𝐀𝐫𝐭 & 𝐃𝐞𝐬𝐢𝐠𝐧: "I 𝐧𝐞𝐞𝐝 AI to push creative boundaries." - And yet, the creative spark starts with a human idea. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: "I 𝐧𝐞𝐞𝐝 AI for precise market predictions." - But truly understanding market nuances comes from human experience and intuition. The Jobs-to-be-Done are complex, and time is precious. We must focus on: ✅ Integrating AI into human-led processes. ☑ Using AI to complement, not replace, human expertise. ✅ Combining AI-generated data with human understanding for decision-making. ☑ Ensuring AI tools are user-friendly for non-tech experts. Finding the right balance is key: A. AI tools must be intuitive and supportive. B. They require human expertise to interpret and apply their output effectively. C. They must fit into the existing culture and workflows. For instance, using AI to enhance patient care requires clinicians to interpret data with a human touch. Or in education, where AI informs, but teachers inspire. 𝐌𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐫𝐨𝐥𝐞𝐬 is critical. And that’s where I come in. 👋 I'm Umer kHan, here to help you navigate the integration of Generative AI into your world, ensuring it's done with human insight at the forefront. Let's collaborate to create solutions where technology meets humanity. 👇 Feel free to reach out for a human-AI strategy session. #GenerativeAI #HealthcareInnovation #PersonalizedEducation #CreativeSynergy #BusinessIntelligence

AI Automation is killing human creativity. A recent study by Gartner shows a significant drop in innovative output in companies heavily reliant on AI-driven automation. But only if you let it... The Gartner report highlights decreased employee engagement and a stifling of novel ideas in organizations that have fully automated key creative processes.  However, the study also revealed that strategic integration of AI tools, focusing on augmentation rather than replacement, led to significant productivity increases and enhanced creative problem-solving. I fundamentally believe AI automation is a powerful tool for accelerating progress, but only when human ingenuity remains central to the process. And it would be a mistake to simply replace humans completely. So, here are my thoughts and takeaways from the Gartner study: ✅ Focus on augmentation, not replacement.  ↳ Leverage AI for repetitive tasks, freeing humans for strategic thinking. ✅ Invest in employee training and development.  ↳ Equip your team with the skills to collaborate effectively with AI. ✅ Foster a culture of experimentation and innovation.  ↳ Encourage employees to explore new ideas, even if they seem unconventional. ✅ Regularly evaluate and adjust your AI implementation.  ↳ Monitor its impact on employee creativity and make necessary changes. AI automation can be a game-changer, but it shouldn't be at the cost of human creativity. The key is to find the right balance between automation and human ingenuity. For more insights and strategies for leveraging AI in your business, follow my page for regular updates!