Tips for Preparing Teams for AI Integration

Teams will increasingly include both humans and AI agents. We need to learn how best to configure them. A new Stanford University paper "ChatCollab: Exploring Collaboration Between Humans and AI Agents in Software Teams" reveals a range of useful insights. A few highlights: 💡 Human-AI Role Differentiation Fosters Collaboration. Assigning distinct roles to AI agents and humans in teams, such as CEO, Product Manager, and Developer, mirrors traditional team dynamics. This structure helps define responsibilities, ensures alignment with workflows, and allows humans to seamlessly integrate by adopting any role. This fosters a peer-like collaboration environment where humans can both guide and learn from AI agents. 🎯 Prompts Shape Team Interaction Styles. The configuration of AI agent prompts significantly influences collaboration dynamics. For example, emphasizing "asking for opinions" in prompts increased such interactions by 600%. This demonstrates that thoughtfully designed role-specific and behavioral prompts can fine-tune team dynamics, enabling targeted improvements in communication and decision-making efficiency. 🔄 Iterative Feedback Mechanisms Improve Team Performance. Human team members in roles such as clients or supervisors can provide real-time feedback to AI agents. This iterative process ensures agents refine their output, ask pertinent questions, and follow expected workflows. Such interaction not only improves project outcomes but also builds trust and adaptability in mixed teams. 🌟 Autonomy Balances Initiative and Dependence. ChatCollab’s AI agents exhibit autonomy by independently deciding when to act or wait based on their roles. For example, developers wait for PRDs before coding, avoiding redundant work. Ensuring that agents understand role-specific dependencies and workflows optimizes productivity while maintaining alignment with human expectations. 📊 Tailored Role Assignments Enhance Human Learning. Humans in teams can act as coaches, mentors, or peers to AI agents. This dynamic enables human participants to refine leadership and communication skills, while AI agents serve as practice partners or mentees. Configuring teams to simulate these dynamics provides dual benefits: skill development for humans and improved agent outputs through feedback. 🔍 Measurable Dynamics Enable Continuous Improvement. Collaboration analysis using frameworks like Bales’ Interaction Process reveals actionable patterns in human-AI interactions. For example, tracking increases in opinion-sharing and other key metrics allows iterative configuration and optimization of combined teams. 💬 Transparent Communication Channels Empower Humans. Using shared platforms like Slack for all human and AI interactions ensures transparency and inclusivity. Humans can easily observe agent reasoning and intervene when necessary, while agents remain responsive to human queries. Link to paper in comments.

I speak to 100's of HR team's a year. Here's 5 steps the best ones are doing to embed AI.   Too many HR teams are either intimidated or overwhelmed when it comes to AI, and they're falling behind.   Instead, embrace the below steps and you'll be in the top 10% of People teams using AI.   1. Create dedicated, protected time   You can't just slap AI on top of your day job, or squeeze it in between meetings (duh).   The work you're doing today will always trump the deep focus time you need to be successful.   So carve it out, choose 1 of these: - Commit 1-2 hours a day to AI, or - Give 1 day a week to AI.   If you need to drop something to make this happen, do it. This must be higher priority than at least one thing you're team does or it won't get the attention it needs.   Protect the time, remove distractions — go into deep focus and spend it doing one of the things in the next step.   2. Time box it   If you're just starting out, commit to doing a short term blitz and then work out how to embed it longer term.   Here's a format you can steal:   6x week sprint:   Weeks 1-2: Focus only on learning/immersing yourself in what's possible Weeks 3-4: Focus on testing the tools you saw in weeks 1-2 Weeks 5-6: Move from testing to actually building (what to build is covered in step 5)   3. Curate learning, don't create   There's so much free content available on using AI that you'd be crazy to spend money on it.   - Greg Isenberg - Ruben Hassid - Jeff Su These are just three (from dozens of) creators I love, who share genuinely actionable insights on embedding AI.   Spend time watching and seeing what can be done - this will get your brain subconsciously thinking about AI solutions for your team.   4. Empower your team   The key message here is: 'remove friction'   Time is a friction (tackled in step 1), but the second biggest one I see, is money.   Don't let budget be a blocker here.   If you're CEO wants you to embed AI practices (and you should to quit if they don't) — then securing a small budget is a no brainer.   There's so many tools - give your people a chance to trial a bunch of them and see what works.   Then: - Keep the ones that work - Remove the ones that don't   Don't make a big procurement process block game changing improvements.   5. Start small when building   Don't try to boil the ocean.   You won't be able to replace a HRBP in a week.   Instead, start small.   Pick the smallest workflow/process/interface under your remit, and start there.   Use your success here to build momentum and move up the value chain.   Don't let overwhelm and intimidation stop you from becoming a more effective People team.   Tell me, which step do *you* think is most important to embedding AI in People teams?

AI adoption isn't just a nice-to-have anymore, it's becoming a performance criterion (at least for my team at e27 (Optimatic)) I recently told my team that I'll be checking in on how they're using AI tools, and meaningful integration of AI into their work matters. Not because I want to police them, but because I want to see them thrive. Here's what I'm learning about building an AI-enabled organization: 1. Tool diversity matters Just like you wouldn't use a spoon to eat noodles, you shouldn't use ChatGPT for everything. I used Claude for content centric work. Manus AI for research workflows. And I'm starting to play around with Perplexity for discovery. The right tool for the right job unlocks exponential value. 2. Create space for experimentation Employees need permission, and a framework, to openly test AI tools without fear. Here's my favorite trick: When trying a new tool, I tell it my role, challenges, and current AI stack, then ask, "How can you help me specifically? How do you compare to alternatives?" This one prompt has saved me countless hours. 3. Accountability drives adoption Performance reviews are evolving. The question isn't just "What did you deliver?" but "How did you leverage AI to deliver more strategic value?" If someone on my team is still doing repetitive work manually when AI could handle it, we need to have that conversation. 4. Balance empowerment with governance Organizations need frameworks that encourage exploration while ensuring security and protecting sensitive data. It's not about restricting access, it's about enabling smart, safe usage. The goal? Free our people from low-value tasks so they can focus on high-value work only humans can do: strategic thinking, creative problem-solving, meaningful relationships. AI fluency is becoming as fundamental as digital literacy was 20 years ago. Leaders who ignore this aren't just missing a productivity opportunity, they're failing to prepare their teams for the future of work. How is your organization approaching AI adoption? Are you creating the conditions for your team to experiment and excel?

Stop Treating AI Like a Tool, Start Onboarding It Like a Teammate! 🚀 Are you struggling to get real value from AI in your team? The problem might not be the technology, but how you're integrating it. Just like a new hire, AI needs clear roles, training, and ongoing feedback to truly thrive. : * Define clear responsibilities: What specific tasks will the AI handle? * Invest in "AI literacy": Everyone on the team needs to understand AI's capabilities and limitations. * Establish communication protocols: How will the AI share its insights and when will it need help? * Provide continuous training and feedback: Help the AI learn and improve, just like you would with any team member. * Foster collaboration and trust: Encourage teamwork between humans and AI. * Iterate and adapt: Be flexible and adjust your approach as the AI evolves. * Address ethical considerations: Be mindful of bias and ensure fairness. The key takeaway? Treat AI as a partner, not just a tool. Build a collaborative environment where AI can flourish, and you'll unlock its true potential.

Somebody has to say it: some AI tools are causing more harm than good. Not because the technology is bad. Not because people are resisting change. But because we keep rolling out tools without guidance, training, or context and calling it “innovation.” When employees are expected to figure it out on their own, confusion replaces confidence. Work slows down. Trust erodes. AI at work doesn’t fail loudly. It quietly creates friction when enablement is missing. If we want better outcomes, we have to design for adoption, not just deployment. If you’re rolling out AI at work and want it to actually help, here’s a simple place to start: 1. Start with the “why,” not the tool ✅ Be clear about the problem AI is meant to solve. Productivity, quality, speed, decision-making. If people don’t understand the purpose, they won’t trust the tool. 2. Define when and when not to use it ✅ Ambiguity creates hesitation. Give real examples of appropriate use cases and clear boundaries so employees aren’t guessing. 3. Train for workflows, not features ✅ Skip the generic demos. Show how the tool fits into existing day-to-day work, step by step. 4. Equip managers first ✅ If managers can’t explain or model usage, adoption stalls. Enable leaders before expecting teams to follow. 5. Build feedback loops early ✅ Create space for questions, friction, and adjustments. Early feedback prevents quiet frustration from turning into resistance. 6. Treat adoption as ongoing, not a launch event ✅ AI enablement isn’t a one-time rollout. It’s reinforcement, iteration, and support over time. AI works best when people feel prepared, not pressured. ——— ✦ ——— 🌱 More on AI + Workforce Development → Janet Perez

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

We rolled out AI across our team in 60 days. No chaos. No confusion. Just clear wins and real results. I've seen marketing departments jump into tools like ChatGPT and Claude without a plan, only to end up with inconsistent usage, security risks, and wasted time. So here’s a reality check: Giving your team access to AI tools is not the same as making them AI-ready. What works? A clear, structured rollout that builds confidence, protects your brand, and drives performance. Here’s the 7-step sequence I recommend getting your marketing team fully ready to use AI: 🔹 1. Leadership Alignment Before anyone writes a prompt, you need to answer this: → What are we actually trying to improve with AI? → Clarify your goals: content speed? campaign performance? lead quality? 💡Assign an internal AI Champion to lead adoption and make this someone’s job, not everyone’s maybe. 🔹 2. Create Your AI Usage Policy Yes, before the first prompt. Set ground rules: → No client data or credentials in tools → Human review before anything goes public → Approved tools only → A go-to person for AI questions 💡Keep it simple. A 1-page doc is better than a 20-page one no one reads. 🔹 3. Train the Team Don’t assume “digital native” means “AI fluent.” Run a short onboarding: → Demo real-world prompts for their roles → Share a centralized prompt library → Walk through how to use your company’s Custom GPT (if you have one) 💡Make it practical. Confidence creates momentum. 🔹 4. Start With Small Pilots Want to build trust in AI fast? Deliver small wins early. Assign 1–2 people per function to test real use cases: → AI for email writing → Content repurposing → Campaign briefs 💡Document results. Share what worked and build internal buy-in. 🔹 5. Bake AI Into Daily Workflows AI should enhance what already works. → Add AI to your content creation SOPs → Use it for meeting note summaries → Integrate it into campaign planning templates 💡The more friction you remove, the faster usage scales. 🔹 6. Build a Feedback Loop Set a bi-weekly or monthly check-in: → What’s saving time? → What’s confusing? → What should we expand next? 💡Refine as you go. This isn't a one-and-done rollout. It's a capability you're building. 🔹 7. Enable Long-Term Growth This isn’t just about productivity. It’s about transformation. → Encourage ongoing experimentation → Recognize team AI wins → Offer certifications or incentives to deepen adoption 💡You’re not just introducing a tool. You’re building a smarter, faster, more strategic team. ✅ Final Thought If you're leading a marketing team, you don’t need to rush into every AI trend. But you do need a clear path for AI readiness. Because the biggest risk today isn’t overusing AI. It’s being the last team in your category that doesn’t know how to use it well. ____________ ♻️ Repost if your network needs to see this. DM me if you need help creating an AI rollout plan for your team.

The ORCHESTRATE framework gives non-technical managers a simple, repeatable way to design hybrid human + AI teams. Use ORCHESTRATE as a thinking scaffold, not a diagnostic. It is shared clarity on: - Who does what - Where judgment lives - How responsibility stays human. Each element maps directly to work they already manage today, just applied to AI as labor. Here is how to teach it with one concrete workflow in mind. Start with: Outcome Definition. Ask one question only. What must be true at the end for this to be considered done and acceptable by the company. This keeps teams from designing AI activity instead of business results. Role Mapping. Go step by step through the workflow and label each step as AI, human, or shared. Stress that AI drafts, checks, or prepares. Humans decide, approve, and take responsibility. This reinforces accountability early. Crossovers and Handoff Points. Ask where work changes hands. What exactly gets passed. In what format. With what confidence level. Poor handoffs are where most risk appears, not the AI itself. Human Value. Ask how AI reduces load, not replaces thinking. Less searching. Fewer reworks. Clearer first drafts. Faster visibility of issues. This keeps the focus on time and attention, not headcount. Escalation Triggers. This is critical for Managers. Define when AI must stop and ask. In which situations should AI never continue on its own? Missing data. Conflicting rules. High-risk cases. Policy conflict. Ambiguity. Make it explicit that stopping is a success behavior, not a failure. Success Metrics. What gets better if this works well? Avoid vanity metrics. Focus on time to first draft, number of reworks, error reduction, and decision cycle time. Clearer decisions. These are familiar and defensible. Training Needs. Ask what managers & teams must learn to work well with AI. Reviewing drafts. Giving feedback. Spotting weak logic. Updating instructions. This reframes AI adoption as a skill issue, not a tech issue. Risk Mitigation. Use a simple lens. What could go wrong operationally, reputationally, or legally. Then tie each risk to a control. Rules. Reviews. Limits. Sign-offs. Adaptation Cycles. Make it clear that workflows are living systems. Decide upfront how often they are reviewed. Monthly for high-risk. Quarterly for stable flows. This keeps AI aligned with reality. Tech Integration. Keep this light. What systems provide inputs. Where outputs go. Who owns access. Avoid tool debates. Focus on boundaries. Ethics and Compliance. Close the loop. Ask how this workflow respects company values, customer trust, and regulatory expectations. Reinforce that responsibility never transfers to AI. The power of ORCHESTRATE is that it feels like management. It turns AI into something leaders already know how to govern. ---------------- Follow Madison Bonovich for more on the SME AI journey.

AI isn't just for tech giants anymore. It's a gamechanger for businesses of all sizes. But where do you start? I've got you covered with these 5 actionable steps to integrate AI into your business strategy today. Identify the Problem You Want to Solve → AI is a tool, not a magic wand. ↳ Start by pinpointing specific challenges or inefficiencies in your operations. Whether it's customer service bottlenecks or inventory management, clarity is key. Gather and Organise Data → AI thrives on data. Ensure you have access to clean, relevant data. ↳ This might involve integrating different data sources or implementing new data collection methods. Remember, quality over quantity. Choose the Right AI Tools → Not all AI solutions are created equal. Research and select tools that align with your identified problem. Consider userfriendliness, scalability, and support. ↳ Platforms like TensorFlow or Azure AI might be a good starting point. Pilot and Iterate → Start with a pilot project to test the AI solution. Measure outcomes and gather feedback. ↳ Use this data to refine and iterate. The goal? Make informed decisions before a full rollout. Train Your Team → AI might be new territory for your team. Invest in training to ensure everyone is on board and confident. ↳ This fosters a culture of innovation and ensures smooth integration. Starting with AI doesn't have to be overwhelming. By following these steps, you'll be well on your way to leveraging AI for tangible business success. What's your biggest barrier to adopting AI? Let's discuss in the comments!

Adopting the latest technology alone won’t build an effective AI roadmap. Leaders need a thoughtful approach—one that empowers their teams and stays true to their values. Over the past few years, we’ve seen AI’s incredible potential, but also its complexity. Crafting effective AI strategies can challenge even the most seasoned tech leaders. To truly unlock AI’s value, we need to put people at the core of our roadmap. At RingCentral, we’ve made it a priority to envision AI in ways that benefit our teams, partners, and customers. Here are a few strategies my team has found essential for building human-centered AI: 1. Emphasize the “why” behind AI adoption: Start by identifying the specific needs AI will address. Help your team see the value of AI as a tool to enhance their work—not replace it. 2. Start with small, targeted wins: Choose use cases that tackle real challenges and show early success. These wins build trust in AI’s potential and create momentum for further adoption. 3. Prioritize transparency and ethics: Set clear guidelines around data privacy and responsible AI use, ensuring that team members feel they’re part of an ethical and trusted process. Guiding AI adoption with a clear, people-first approach enables us to create a workplace where innovation truly serves the people behind it, paving the way for meaningful growth. 💡 How are you approaching AI within your teams?