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Ronnie Parsons shared thisIf your team is busy and AI hasn’t made the difference you expected, I’d start with a piece of work that keeps slowing people down. A report waiting for review. A request passed between three people. An update that takes longer to check than to write. Ask the person doing that work to walk you through a recent example: ❓️ Where did they get stuck? ❓️ What information did they have to chase? ❓️ Which decisions needed someone else? You may find a useful role for AI. You may also find that nobody agrees on what a good result looks like, or who can approve it. Those details shape whether AI will actually help. For example, if a report sits waiting because only one person knows what to check, generating it faster may leave the team with the same delay. That’s how I think about Operating Design: Start with what the business needs to improve, then work out how people, information and AI should fit together. If you’d like help deciding what to tackle first, I have 3 AI Assessment spots available in October. You’ll leave with clear priorities: 1️⃣ recommended tools 2️⃣ estimates of costs and time savings 3️⃣ and a 30–60 day implementation plan DM me and tell me where the work gets stuck.
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Ronnie Parsons shared thisHere's how to find your team's first useful AI project. Free resource linked below. Take proposals, client updates and incoming requests. You might see room to improve all three. But one has reliable information and someone ready to test it. Another depends on records the team can't yet find. I'd want to know that before choosing. That's the choice my free playbook helps you work through. It asks six questions about the value of an idea, what it needs and how you'll judge whether it helped. You also get: → A blank canvas to compare three pieces of recurring work. → A completed example showing why one idea is ready to test and others need preparation. Use it with the person who knows the work. Their view of what takes time, what goes wrong and what needs checking belongs in the decision. You may finish with a first test. You may finish knowing that someone needs to sort out the information first. Either gives you a next step you can explain. Get the free playbook and canvas when you sign up for Further, my newsletter: https://buff.ly/uCsT4HXFind Your Team’s First Useful AI Project | Mode LabFind Your Team’s First Useful AI Project | Mode Lab
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Ronnie Parsons shared thisWhat would you like to offer clients that your team doesn't have time to deliver today? That's one of the questions I'd bring to a conversation about AI. Take a team that reviews a client's work once a month. Between meetings, things change. An earlier update might help the client act, but someone would have to keep looking for what matters. AI could help with that. It could gather changes from agreed sources and prepare a draft for someone who knows the client. The interesting decisions come next. What is worth interrupting the client for? Who checks the update? What happens when the sources disagree? How much time would the team need to keep this going? Those answers shape the service. I'd test it on a small set of information the team is allowed to use. Then ask whether the update would help the client do anything differently, and what it took to get it ready. This is one reason Operating Design interests me. It gives us a way to work out how people and AI could deliver something the business couldn't reliably offer before. A useful first test should tell us whether that possibility is worth pursuing. --- I just launched my newsletter, Further. Every Saturday, I'll share practical ways to put AI to work in your business. Pure signal. No noise. If that sounds useful, you can join here: https://lnkd.in/gKuZRGTp
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Ronnie Parsons shared thisIf you keep fixing the same process, it’s worth asking where the fix goes after the work is done. Take a proposal. You clarify a confusing paragraph, add a question you should have asked earlier, and explain a deliverable the buyer didn’t understand. The proposal improves. But if the next one starts from the old template, you have the same work waiting for you. That’s a useful place to design a continuous improvement loop into your operations. Before you close the work, capture what you learned. Decide whether it was specific to that client or something worth changing for everyone. Then carry the agreed change into the template, instructions or process your team will actually use next time. The loop is simple: Observe → identify an improvement → review → update → verify → reuse. Someone needs to make sure the change carries through. They need to know where the shared version lives, which changes they can make, and which decisions need someone else’s judgement. Clarifying a sentence might be theirs to handle. Changing what you promise a client may need your approval. AI can help identify improvements and prepare updates. A person still needs to decide what becomes part of how the business works. Then check the next use. Did the new question get you the information you needed? Was the explanation clearer? Did the change create another problem? Make that check part of finishing the next job, and you have a way for everyday work to keep improving the business.
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Ronnie Parsons shared thisI’m starting a newsletter called Further. The first issue goes out this Saturday. I want a place to explore a question that runs through my work: how can people and AI work together to improve a business and make new things possible? That includes the everyday work, like preparing a proposal or keeping a client informed. It also includes the ideas you haven’t had the time, capacity or tools to pursue. If you run a business or help a team get its work done, I’m writing this for you. The first issue looks at a familiar moment: AI has written a client update that reads well, but would you send it? The answer depends on whether it has the latest information, whether the next steps are actually agreed, and who knows enough about the client to catch what’s missing. Those are the details I want to explore in Further, because they shape whether AI becomes useful in the work your team does every day. I’ll share practical examples, illustrations and questions from my work and our community workshops, with a prompt or template when it helps put an idea to use. If that sounds useful, subscribe below to get the first issue on Saturday morning. https://lnkd.in/gKuZRGTp
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Ronnie Parsons shared thisYou changed the offer. Why is the old price still turning up in proposals? I'd check how that decision reached the people and AI preparing the work. Suppose one colleague uses the current offer and another has an older version saved in a project. Both can produce a polished proposal. Only one starts with the terms you now want to sell. Telling the team about a change is one part of the job. Updating the material they work from is another. I'd follow the last change through three places: → The record of what was agreed. → The instructions and examples used to prepare the work. → The next proposal that reaches a client. If those don't agree, there's a gap someone needs to close. Keep the old version if it helps explain the history, but make its status clear. And decide who is responsible for updating the working material. This is part of what I mean by Operating Design. A business decision needs a way to reach the work, including the work AI helps produce. Otherwise, the person doing the final review has to keep remembering what everyone else should already know.
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Ronnie Parsons shared thisI spent the last two weeks prototyping different ways of working across my consulting business. Those experiments helped shape how I’m working for the rest of the year. Choosing which changes to keep meant answering the same question each time: Is this actually better, or just new? AI can help me stand up a test quickly. I still have to judge whether the result improves the work for the client and meets the standards I’m responsible for. I put the approach into this cheat sheet: 1️⃣ Pick one stage of work you repeat and describe the old way. 2️⃣ Write down what you expect to improve before you start. 3️⃣ Give yourself two hours to test the smallest useful change. 4️⃣ Compare the result with the old way, then decide what deserves to stay. If it holds up, save the instructions, a worked example and the checks needed to run it again. Make that part of finishing the experiment, while you still remember what mattered. Before making it the team’s way of working, let someone else try it. A teammate or client will find gaps you miss because you already know how the test is supposed to work. That gives you something concrete to improve before others start relying on it. If you use the sheet this week, start with one repeated task. Define what “better” would mean, test a change and keep a record of what you learn. Two weeks of experiments can shape months of work. The part that matters is deciding what earns a place in how you work next.
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Ronnie Parsons shared thisToo many AI ideas. No clear place to start. I made a free playbook to help you choose. Get the free playbook and canvas here: https://lnkd.in/derr4WjQ You might be considering client updates, proposals or incoming requests. Each could benefit from AI, but which would make a meaningful difference, and which is ready for a test? The playbook helps you work through that choice. Inside, you’ll find: → Six questions to assess the value and readiness of an idea. → A canvas to compare three pieces of recurring work side by side. → A completed example showing why one project is ready to test and others need preparation. → Guidance for naming an owner, defining a useful result and deciding what a person needs to check. Use it with the person responsible for the work. Together, you can identify where AI could help, what information it needs and how you’ll judge whether the result is useful. That includes the effort spent reviewing and correcting it. If none of your ideas is ready, the exercise still gives you a next step: identify the gap and agree who will resolve it. The six-page playbook, blank canvas and completed example are all free. Bring your preferred AI (I'm feeling Codex these days). Download the playbook and canvas https://lnkd.in/derr4WjQ
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Ronnie Parsons shared thisI’ve put six AI agents to work in my business. Each has a clear job, shared context, and rules about what stays with me. I’ve organized them six areas of recurring work: -> Marketing: audience research & content drafts. -> Sales: reviewing enquiries and drafting proposals & follow-ups. -> Delivery: assessment drafts, session preparation, & community support. -> Research & Development: tracking changes & testing new ideas. -> Operations: reviewing my inbox, organizing tasks, & summarizing progress. -> Learning: capturing lessons, improving instructions, & testing changes. Across those roles are 13 defined skills. Some run daily or weekly. Others run when an enquiry arrives or I request the work. Two choices shape the setup. First, every agent uses the same business context: who we serve, what we offer, how we communicate, and the standards the work needs to meet. I maintain that context so each task starts with a clear account of the business. Second, each agent has a specific responsibility, with clear limits. For a proposal, the agent prepares a draft with scope and pricing inputs. I decide what to offer, what to charge, and whether it’s work we should take on. I set direction, manage relationships, and review the work. What I learn from that review feeds back into the instructions. That’s the operating design behind this setup: clear responsibilities, shared context, and a way to improve the work over time. The graphic shows how I’ve divided the work across all six agents. Which recurring part of your business could you define this clearly?
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Ronnie Parsons liked thisHere's how to find your team's first useful AI project. Free resource linked below. Take proposals, client updates and incoming requests. You might see room to improve all three. But one has reliable information and someone ready to test it. Another depends on records the team can't yet find. I'd want to know that before choosing. That's the choice my free playbook helps you work through. It asks six questions about the value of an idea, what it needs and how you'll judge whether it helped. You also get: → A blank canvas to compare three pieces of recurring work. → A completed example showing why one idea is ready to test and others need preparation. Use it with the person who knows the work. Their view of what takes time, what goes wrong and what needs checking belongs in the decision. You may finish with a first test. You may finish knowing that someone needs to sort out the information first. Either gives you a next step you can explain. Get the free playbook and canvas when you sign up for Further, my newsletter: https://buff.ly/uCsT4HXFind Your Team’s First Useful AI Project | Mode LabFind Your Team’s First Useful AI Project | Mode Lab
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Ronnie Parsons reacted on thisIf you are looking for some solid, actionable and practical information on AI in business, check out Ronnie Parsons new newsletter:
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Ronnie Parsons reacted on thisCelebrating 25 years of Trust for America's Health and its rigorous, nonpartisan research and tireless advocacy for policies that build a stronger, healthier nation. Thank you to J. Nadine Gracia and the entire team for your leadership and partnership. Grateful to work alongside you!Ronnie Parsons reacted on thisNavigating complex, compounding public health challenges requires actionable, evidence-based guidance that successfully bridges the gap between public policy and corporate practice. In our next Voices of Trust segment, Steven Levine and Stephen Massey, Co-Founders & Co-CEOs of Health Action Alliance, discuss how partnering with TFAH helps employers move from recognizing public health challenges to implementing practical, cross-sector solutions. Tracing their partnership from the height of the COVID-19 pandemic to current efforts on the National Commission on Climate and Workforce Health, they explore how TFAH’s independent, science-backed guidance enables corporate leaders to safeguard vulnerable worker populations, connect workforce health with economic stability, and champion robust public health infrastructure. Watch to hear how aligning corporate influence with independent public health expertise drives holistic workforce resilience – addressing everything from environmental threats to mental well-being – and why a thriving business sector relies on a well-funded, robust public health ecosystem. Extreme Weather + Work initiative: https://lnkd.in/exWi2ytf
Experience & Education
Licenses & Certifications
Publications
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Design X at ICFF 2014
BAKU, Art.Culture.Wild.
See publicationCoverage of our installation/exhibition at the International Contemporary Furniture Fair in New York. The 3-day event was the first major design fair to base its programming around 3-D printing. Our talks covered everything from parametric design to printing 3-D fabrics. Participants included Nervous Systems, Shapeways, Francis Bitonti, and Skylar Tibbits, to name a few.
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Parsing Populations & RK4 Tiles
Scripting Cultures, Architectural Design and Programming
See publicationTwo projects (Parsing Populations, RK4 Tiles) contributed to this publication. Description of the book from Wiley:
With scripting, computer programming becomes integral to the digital design process. It provides unique opportunities for innovation, enabling the designer to customise the software around their own predilections and modes of working. It liberates the designer by automating many routine aspects and repetitive activities of the design process, freeing-up the designer to…Two projects (Parsing Populations, RK4 Tiles) contributed to this publication. Description of the book from Wiley:
With scripting, computer programming becomes integral to the digital design process. It provides unique opportunities for innovation, enabling the designer to customise the software around their own predilections and modes of working. It liberates the designer by automating many routine aspects and repetitive activities of the design process, freeing-up the designer to spend more time on design thinking. Software that is modified through scripting offers a range of speculations that are not possible using the software only as the manufacturers intended it to be used. There are also significant economic benefits to automating routines and coupling them with emerging digital fabrication technologies, as time is saved at the front-end and new file-to-factory protocols can be taken advantage of. Most significantly perhaps, scripting as a computing program overlay enables the tool user (designer) to become the new tool maker (software engineer). Though scripting is not new to design, it is only recently that it has started to be regarded as integral to the designer's skill set rather than a technical speciality. Many designers are now aware of its potential, but remain hesitant. This book treats scripting not only as a technical challenge, requiring clear description, guidance and training, but also, and more crucially, answers the question as to why designers should script in the first place, and what the cultural and theoretical implications are. -
Fleshless Floor, Commonwealth
Creative Review
See publicationInterior design project completed during my time at Commonwealth. Description pulled from Creative Review.
Using animation software to create ceramics or ‘rule-driven scripts’ to design furniture, New York-based studio Commonwealth has made a name for itself in employing familiar technological processes in unlikely ways...
Its latest project, Fleshless, is very much a case in point – a flooring system designed using a programme more commonly used to create liquids in movie…Interior design project completed during my time at Commonwealth. Description pulled from Creative Review.
Using animation software to create ceramics or ‘rule-driven scripts’ to design furniture, New York-based studio Commonwealth has made a name for itself in employing familiar technological processes in unlikely ways...
Its latest project, Fleshless, is very much a case in point – a flooring system designed using a programme more commonly used to create liquids in movie special effects sequences.
Organizations
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Consumer Technology Association
Member
- PresentA proponent of innovation, CTA advocates for the entrepreneurs, technologists and innovators who mold the future of the consumer technology industry. CTA provides a platform that unites technology leaders to connect and collaborate, and it avidly supports members who push the boundaries to propel consumer technology forward.
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Association for Computing Machinery
Member
- PresentACM SIGGRAPH is a global non-profit, volunteer organization defined by SIGGRAPH people and their attributes: EXCELLENCE, INTEGRITY, PASSION, VOLUNTEERISM and CROSS-DISCIPLINARY INTERACTION. Our programs serve the various parts of the SIGGRAPH community, reach out to other communities, and benefit all of us by promoting contact and information exchange.
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Association for Computer Aided Design in Architecture
Member
- PresentThe Association for Computer Aided Design in Architecture is an international network of digital design researchers and professionals. We facilitate critical investigations into the role of computation in architecture, planning, and building science, encouraging innovation in design creativity, sustainability, and education
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