In a talk I gave a couple of weeks ago, I highlighted that in order to scale, leaders need to realize the importance of creating and replicating processes, and embrace the potential of disseminating autonomy. A sharp-minded member of the audience (whose name I didn’t catch!) asked this: how can you resolve the contradiction between following a process and having autonomy? My initial answer to the group was: “use 300gm flour, 3 eggs, 200gm butter, 300gm sugar, whisk, and bake” and “use flour, eggs, butter, sugar” are both examples of a process to make cake. The earlier allows little autonomy but high repeatability/predictability, the latter more autonomy but higher variability/unpredictability. In the corporate world, deciding on the specificity of actions and variability of outcomes is a balancing act; critical processes must be rigid and fluid ones need to be flexible. While I still stand by that, I did have a further personal insight that I shared with the gentleman 1:1, and sharing below. I am personally not a good rule follower. I want to understand the why before I follow any rule/process. This has caused untold misery to my parents, teachers, bosses, and my wife. But it’s also allowed me to reach a desired outcome by doing things my way – the process is not the point, it’s the repeatable outcome that’s the point. The additional layer of nuance that I want add to my answer is this: even in most restrictive process, asking why allows you to understand the desired outcome, and combining a clear understanding of the process and the why behind it gives you the autonomy to reach the outcome your way. Just don’t mess with legal along the way 😊

Lessons from Founders Scaling Sales In the early stages of a startup, founders often do everything, but scaling a business requires a shift in mindset. Here are four lessons I've learned working with scale-ups that made the leap: Invert the Pyramid – you can't be the solver of all problems. Problems shouldn't flow from the field to leadership, it's the other way around. Identify high-potential leaders, coach them, and empower them to take ownership. Ask questions, don’t provide solutions. Processes Allow Repeatability – sales success isn’t magic; it’s methodical. No one can read your mind. Document your process, introduce qualification and forecasting principles, and run on structured cadences. Compounding Change > Silver Bullets – big transformations rarely work. Instead, drive consistent, incremental change. Focus on one improvement per month or quarter and reinforce the changes by celebrating success. Accountability Requires Autonomy – if your business depends on you for every decision, you’re the bottleneck. Introduce clear accountability structures (like RACI), break your role into specific jobs, and free your time to lead. Accountability requires autonomy, autonomy requires trust, and trust requires letting go. At scale, leadership isn’t about being the superhero – it’s about building superleaders. What shifts have helped you scale effectively? Let’s discuss.

Successful startups will inevitably need to answer the question: are we now ready to scale, to start putting resources in sales structure and repeatability to achieve high growth? I would argue that there is no “correct” answer, that’s it’s as much an art as it is a science, and that luck and market conditions will play a bigger role than many founders believe. Having said that, there are a few areas that will help move the needle from the art to the science side: Annual revenue: from two angles, first, it’s proof that people are willing to pay for what you’re selling, and second, scaling means hiring salespeople, and knowing how to pay them starts with how you make money. The exact amount will depend on the market and funding, but the $1-5 million range is a good rule of thumb Product market fit: are all your customers using your products in a similar way? Is your product solving the same problem? Are your commercial offers to different clients consistent/within range of each other? Repeatability: do your deals close following a similar pattern? Is it documentable? Do you have the assets to support a sales process? Customer success: are your customers using the majority of your features? Technical readiness: can your solution support rapid growth (back-end systems, ticketing & troubleshooting, setup & provisioning)? Operational readiness: are your company systems ready to support rapid growth (billing & collection, customer onboarding, etc.)? A final note here: VC pressure notwithstanding, I would always recommend being too late than too early when deciding to scale. Scaling too early is a drain on resources, it’ll distract from the core business, and potentially derail focused growth. As in all things startup, agility and experimentation is your friend here, and starting small is the way to go. What are other metrics you look at when you consider scaling? Let me know in the comments.

To help dispel the fog of the holiday season, I’ve been attending a few networking and learning events. When I introduce myself as a fractional sales leader, I often get asked “What’s that exactly? Are you a consultant? An advisor? Isn’t that just a fancy name for part-time?” Yes and no – read on! A fractional leader is an experienced individual who works for a company in a senior role on a “fit for purpose” basis. Let’s break it down! The purpose it is fit for is providing expertise and leadership for companies that can’t afford/don’t need that individual on a full-time basis. Your typical start-tup, for example, doesn’t really need a full-time CxO, so going fractional is getting only what they need and not more Fractional leaders are not consultants – they behave and should be treated as employees of the company they work for. They don’t just point to problems and build blueprints (sorry my consultant friends, I know you do more!), they lead teams and affect change. They are employees, they’re just not around all the time Fractional leaders are not part-timers – yes they spend a faction of their time at any one company, but they’re different than part-time workers in that their scope is not as well-defined. Fractional work applies when the problems being tackled are complex/ill-defined Fractional leaders are not contractors – contract work tends to be limited in time and scope. Fractional work is more fluid, with fractional leaders tackling whatever problems thrown at them, and it and tends to be long-term, usually a year or longer, and are often are called in for critical events Does this make sense? What issues do you foresee in using such a model?

This post was hosted by a company I'm partnering with: Scopic. Their blog post is linked here . In this blog post I’ll attempt to cover some of the nuances in using AI in a contact center (CC) environment. Contact centers are the ever increasingly default touchpoint between organizations and their customers. Due to their reliance on people to serve customers, even if remotely, CCs are often viewed as cost centers with expenses heavily scrutinized. Using AI in the contact center isn’t new, but the recent advances in developing AI solutions using large language models (LLMs) has changed what’s possible and how it’s done. Classic AI solutions have been improved, and a new generation of generative AI (genAI) solutions has introduced whole new use cases. Are they magical silver bullets to solve all challenges? Not yet. But there’s enough new and interesting to warrant a revisit. Before delving into some use cases, it’s important to note that the more automation is introduced into a CC operation, the more the performance metrics need to be tweaked to match the new environment. As the more mundane issues get automated out, agents will naturally start receiving the more complicated issues, which will generally take longer to resolve and might need multiple touch points. Traditional contact center metrics of average handing time (AHT), cost per call (CPC) should be de-emphasized and replaced with more meaningful customer satisfaction metrics such as first call resolution (FCR) and net promoter score (NPS). So what areas of the contact center could benefit from AI? Agent Assistance The highest cost in a CC is the human one, so any efficiencies there have the best ROI. The first step of interaction between customers and an organization these days is usually chat. It might be tempting to give genAI chatbots free reign to serve customers in that channel, but the risks with current gen are too high (hallucination, misunderstood intention, malicious users). Instead of going full genAI automation, chatbot vendors have started introducing genAI elements into their classic AI solutions, making them sound more human and responsive. They’re better at knowing at customer intent, they serve more comprehensive answers, and when they can’t solve a problem, know when to hand over to an agent. Note that this automation can be done for chat and voice calls, but in my experience the accuracy and responsiveness of voicebots is not ready for wide deployment. After that handover happens, chatbots can stay “on the call” to continue providing real-time assistance: both in terms of a soft handover to the agent (for example, offering a summary of the chat and potential resolutions), and staying on to assist the agent with responses and tools in real time. These bots will surface relevant data to the live agent, provide unique pre-built responses, and even provide feedback to the agent. A chatbot will also assist with after-call work: summarizing the call, customer issue, and resolution, and displaying that information so the human agent will only need to review the summary and save it. A note about voice-bots: I’ve seen several vendors promise good voice-bots for customer service, but I’ve only seen limited successful deployments. Issues with cost, accuracy (especially with accents and dialects), customer frustration, and length of deployment have made them niche offerings. However, things are developing quickly in that space so this paragraph might not be accurate in the coming months. Agent Onboarding & Training One of the challenges in contact centers is on-boarding new agents and having supervisors shadow them for their first few calls. Whether to train new agents or retrain current ones on new products, there are now genAI-based solutions that will allow you to create a persona that agents can train with: happy customer, irate customer, etc., and allow agents to practice through either with chat or voice. These tools will provide summaries on how well an agent does and suggest actions to improve. And because these use cases are internal, a full genAI chatbot can be used, freeing up supervisors and team leaders. Analytics CCs generate a lot of data: call metrics, call recordings, agent performance, etc. The challenge with that data is the quantity: generating reports is a complicated process, and except for a small percentage, most recorded calls are not reviewed at all. The most recent advances in applying GPTs to large quantities of data has opened a whole new world of possibilities. First, analyzing chats and ACW notes from agents. This will help contact center managers uncover trends and sentiment: how well are customers liking a certain product? What’s the most common complaint about another one? What’s a feature everyone is asking about? What’s a competitor doing? These can all be summarized by training an LLM on the CC’s data. Second, analyzing call recording: this is a bit more computationally demanding but is a natural extension of the analyzing text. Similar insights can be had, but these can also analyze agent calls: how did they perform, where can they improve, where do they need additional training. Third, analyzing statistical data using human language: what’s our call trend, where can we improve, can you compare these numbers to last week’s, etc. This offloads some of the demands of the analytics team, allowing them to spend less time generating reports and more time analyzing and making decisions. Adopting AI Tools A common question posed by many companies is: with the apparently limitless possibilities of AI, where do we start? I’m providing a rough guide here to help generate some ideas. Start with the most pressing problems: every CC has its challenges, and some will be higher on the radar than others. It might be tempting to solve the easier problems first, but the time and effort put into an AI project will be similar regardless of the problem being solved, thus a higher ROI will be achieved with the more complicated problems. Get comfortable with an experimentation mindset: some classic AI solutions are proven, but the outcomes of more modern genAI-infused solutions are still being defined. Vendors are rushing to introduce genAI into their solutions, but the use cases are still niche and don’t have clear ROI. When experimenting with genAI solutions, it’s better to start small (subset of users, subset of customers, subset of data, etc.), define a narrow scope of project, including success criteria, and be comfortable with declaring failure if things don’t work. It’s better to succeed in 1 of 5 smaller experiments than force one l project through. A large project shouldn’t be attempted unless there was a successful smaller scale experiment. Beware the vaporware: vendors have different levels of maturity when it comes to genAI adoption and expertise. When dealing with vendors, adopt a “benefit of the doubt” approach. After the marketechture presentations, insist on live demos and use cases that apply to your organization. Instead of going full project deployment, use a proof of concept (POC) or pilot approach, keeping the scope and investment to a minimum. Define success criteria clearly and insist and involvement by the vendor throughout. Engage the right partnerships: the genAI wave is only two years old, so anyone insisting that they’re “experts” in genAI is being optimistic. That shouldn’t mean everything needs to be done in-house – many companies that have been doing the behind-the-scenes work around building AI solutions for years, and thus will have experience in AI in general. Ask for specific examples of projects or deployments, seek detailed explanations for how that partner would address a specific problem, look for lessons learned or other indications of experience. Watch for unintended consequences: you might successfully solve one problem and create another. In the CC space, resolving the smaller requests via chatbots will make the typical problems live agents handle more complicated, thus increasing their handling time. Making generating reports easier might lead to too many reports, overwhelming the BI team. Involve governance, at the right time: genAI solutions are somewhat of a black box in terms of how they work. They have built-in biases, assumptions, understandings, and definitions that have been tweaked by their developers. Many genAI solutions involve uploading user data for model training and improvement. Many insist on connecting over the internet to a cloud provider. All these variables are things worth considering and assessing before rolling out full projects, but trying to resolve them all before experimenting is not practical. Engaging the governance team at the right time is important. This is a taste of what’s possible. The genAI market is developing rapidly, and instead of focusing on specific solutions, the approach of experimenting with genAI is what companies should focus on.

Qualification is a key sales discipline and missing it or making assumptions while working on deals makes forecasting tricky. A sales cycle that has weak or undisciplined qualification will show some or all the following symptoms: Deals constantly missing committed close dates, no matter how much you “review” them The deal size changes drastically throughout its lifecycle The deal “story” changes every other review There are many unknown basics about a deal: why the customer is moving forward with it, what their alternatives are, what the key decision criteria are Deals are committed based on responding to an unsolicited RFP/RFQ Predictable cash flow is key for any organization, and accurate sales forecasts are the backbone of cash flow, and a strong qualification culture would ensure that. Now the question is: what is qualification exactly? Qualification is the process that assesses the certainty of a deal closing at a certain time with a certain value. A strictly-enforced, clearly-documented, widely-communicated sales qualification process improves the predictability of a sales funnel. There are many qualification methodologies available in the market, each claiming they’re the best for xyz, and while they have their nuances, they all try to answer the following five questions for a company selling to a customer: Is there value that the company can deliver to this customer? Does the customer have a quantifiable problem that the company can fix? Value is unique per customer and depends on their challenges Does the customer have any sense of urgency to address their problem? What’s happened recently that is driving this deal from the customer’s side? Is the company dealing with the correct person from the customer? Does that person own/influence/oversee the procurement process? Can they open doors internally, and guide the company down the best path? Are they giving the company a fair chance? Does the customer have a budget set, or can they secure the budget, for procuring the solution? Does the company understand the customer’s buying process? What’s the sequence of steps needed to get to the order: documents needed, demos, response to RFP, signatories, etc., and the time this process typically needs? If the answer to any of these questions is a no, the deal is not well-qualified. In the next post I’ll talk a bit more about how to get answers to these questions. An important note about qualification: no outlook is 100% certain, even a well-qualified deal might still slip or change in value. The aim of qualification is not to guarantee all deals, but to improve the close rate and predictability on average. Qualification is near and dear to my heart, and at kyadah I spend a lot of time to understand a company’s business and help them adopt or build their own qualification process. A final point to to stress: qualification is not a box-ticking exercise, it’s an effort by the sales team to move a deal from partially or weakly qualified to fully qualified. A less than full qualification only means that the deal might not close at a specific time and value; it doesn’t necessarily mean it should be dropped without considering improving the qualification, as follows: If there is no immediate problem to fix, no value to address, is there a way to go deeper with the customer? Talk to other stakeholders? Uncover challenges the customer didn’t know about? If there is no apparent sense of urgency, can it be uncovered? Sharing use cases that stress the risk/reward balance? Highlighting competitive threats? Stressing upcoming regulatory changes? If the current champion isn’t the correct person, can the sales team ask for internal referrals? Can they help this champion to become an internal champion by acting as value creator for other users? Can other stakeholders be accessed? If there is no budget, can the sales team help build the business case? Can the commercial offer better align with the budget cycle? Can other financial models be presented? If the buying process isn’t clear, it can be clarified. Persistently asking the right questions will build a more robust understanding of the process and how long things will take In short, an unqualified deal doesn’t mean an instant drop. At kyadah we consider qualification the roadmap to ensure that a deal (a) will close (b) at a specific time (c) at a specific value. A sales team’s efforts should be focused on improving all the points above: more value, more urgency, a wider network in the account, more financial impact, and tighter control of the buying process. Only when the efforts to improve the above fail should the deal be qualified out.

I like to think of corporate leadership as having three levels: manage, influence, and inspire, corresponding roughly to the relationship a leader has with the people they work with: managing is for the leader’s team, influencing is for those the leader works with but in different areas of the organization, and inspiration is for those the leader doesn’t interact with personally – the wider audience who might listen to the leader, read their writing, or follow their strategies. As a leader rises in an organization, their leadership level must rise with them to succeed. As the reporting structure under a leader becomes larger, and their relationships become more complex, the tools that worked in a previous levels become less effective. Rising in an organization doesn’t mean abandoning the previous levels, but rather adding layers to them. Managing is telling people what to do. Managing is performing the corporate function: getting a team to deliver accomplish their goals. Being a good manager requires being able to set clear objectives and deliverables, prioritizing regular 1:1 coaching, mediating and resolving conflict as it arises, guiding career planning, and protecting their team from distractions. Influencing is getting people to understand why need to do what they need to do. Influencing is affecting those a leader works with the intent of getting work done. Being a good corporate influencer requires a leader to be clear communicator, developing a strong sense of empathy, operating with strong integrity, focusing on building relationships vs. transactions, and an acceptance to compromise. Inspiring is getting people to want to do what they need to do. Inspiring affects everyone the leader interacts with: the wider team, other corporate functions, external stakeholders, and even outside the business context. Inspiration requires self-mastery, knowing one’s strengths and weaknesses and acting accordingly, it needs a strong sense of authenticity and honesty, even vulnerability, and it communicates via storytelling, where people can find common values. Above all inspiration needs aspiration: an inspirational leader needs to adopt a grand vision; puny goals have never inspired anyone

Teams do not spontaneously innovate. Apple famously designs their offices to encourage employees to mingle, with the hope that people will interact and spark new ideas. The reality of most individuals, unfortunately, is that they just want to get their work done, and these “sparked” interactions rarely happen. Hope is a poor strategy to build on. A better strategy is to design for team effectiveness, whether it’s being productive, efficient, agile, innovative, or whatever metric a company decides is important. Instead of hope, build a team on the following three principles. Strong norms: this is the foundation of any team (or company, really), and it just means setting clear and enforced expectations of behaviors. The following norms tend to come up often in strongly performing teams: transparency, being clear and explaining why things are the way they are; accountability, owning actions: who will do what by when; integrity, if commitments are missed, how will that be resolved; respect, separating facts from judgements; psychological safety, the freedom to make, report, and learn from mistakes Diverse inputs: building a team for “culture fit” will not generate new ways of thinking, but inviting and encouraging diversity will. How to encourage diversity? Ask for inputs before a meeting starts to reduce groupthink; ask for a devil’s advocate in every meeting to upset echo-chambers; allow everyone to contribute, even if non-verbally; perform pre-mortems; ask for “wrong” ideas/ones that won’t work; only permit the leader to speak last Deliberate, Design, and Deliver: team actions should be categorized into those categories and performed in that order. Actions categorized in Deliberate, the crux of why teams exist, attempt to solve non-trivial problems: invent a new product, overcome a delivery issue, add critical features, any problem that requires original thought. Actions in Design come after Deliberate, and they’re meant to plan how to achieve the goals outlined in Deliberate. Finally in Deliver are the individual actions that team members need to deliver A team missing one of these three will not perform well. Poor norms lead to discomfort and disengagement, mono-inputs lead to groupthink and echo chambers, and unplanned actions lead to waste and chaos.

If you're a leader who has team members who are not doing what they're supposed to be doing, they're not lazy or apathetic. There are six sources of (in)action at work, summarized in the graphic. Humans take action based on personal, social, and structural drivers. Personal drivers are the ones in our heads, social ones are what we see in our communities or teams, and the structural ones are the ones imposed on us by our employer. There are two dimensions to each driver: motivation and ability, which gives us the six causes of (in)action. If you see someone not getting things done, one of these drivers is either missing or even undermining the work getting done, and addressing it usually resolves the problem: Personal motivation: why should I do this? If you hear “I don’t care” then this one is the issue. The fix: find a connection or meaning from the personal values to the work being done, or overcome any objections based on value Personal ability: how do I do this? If you hear “I agree with you, I just don’t know how” this might be your root. The fix: training. Social motivation: is everyone around me doing this? Going against the grain is hard, so asking someone to do a thing one way while everyone else is doing it another will probably not work. The fix: pave the way; find the boldest, loudest disruptors, and ask them to publicly do the thing first. Social ability: am I getting the support I need? Most jobs are team efforts, and not getting the help you need is demotivating AND embarrassing. The fix: setting clear expectations, asking for help publicly, offering “buddy” systems and help. Structural motivation: am I being paid for this? Or more accurately, am I measured on this? If the measures are not clear, or there is no incentive to meet the measures, then there will be little motivation. The fix: make sure incentives align with expected KPIs/outcomes/behaviors. Structural ability: does the company structure support what I need to get done? Companies might have the best intentions: training, incentives, information, everything an employee needs, but have their teams or processes structured in a way to make hitting KPIs difficult. The fix: look at teams and processes and make sure everyone can get their work done. The ideas above are adapted from the book Influencer https://www.goodreads.com/book/show/914211.Influencer Have you had experience dealing with “stuck” employees? Share in the comments.

You’re a startup that’s successfully built an MVP and has a few customers, when’s the right time to start hiring salespeople (i.e. become a scale-up)? There is no straightforward answer, and the timing is a matter of gut as much as data; however, the following scorecard can help you sharpen your thinking: Market fit & readiness: are the market conditions right for scaling up your business? Is your ideal customer profile clear to you? After a sales call, do you spend more time tweaking the product or working on a quote? Do your customers use the product similarly or does each have a unique use case? Do your customers have elements in common (size, industry, GTM model)? Repeatable sales process: is your sales process documented anywhere? Can you walk a new hire through how to close a lead? Do you have a sales playbook? Is your pricing/delivery model well-defined? How quickly can you get a quote out? How quickly can you process an order? Do you have brochures, a product overview, RFP support material? Can your sales team rely on marketing/lead gen support? Scale-up readiness: is your product technically ready to scale up? How easily can your product be implemented at scale? Can you support and troubleshoot customer issues at scale? Is there a customer hotline/support channel? Do you have a customer success team/model? Do you have a partner network in place to support your customers? Revenue & incentives: can you afford salespeople? Do you have enough recurring revenue/cash reserves to cover your expenses? How long is your sales process? What are your assumptions on when your salespeople can start bringing in revenue? Do you have an incentive model to ensure your salespeople are motivated without breaking the bank? Score each area 1-10 on how ready you are. Total 30+ and not lower than 6 on any area, you’re probably ready for sales. An area of 5 or lower needs attention. What areas have I missed? Let me know in the comments.

I've created this graphic to summarize my experience in the APAC, and explain why the APAC market is so hard to penetrate. In short, the concept of APAC itself is misguiding, and the countries should be viewed individually. I like to think of the market along three dimensions: size of opportunity, ease of doing business, and appetite for innovation for technology products. Appetite for innovation: despite external appearances, Asian cultures in are conservative and collective. In an overgeneralizing sentence: risk avoidance is more important than successful experimentation. Taking time to assess, evaluate, experiment, and establish trust and dependability are core parts of doing business. Companies will not change to a “superior” vendor because they’ve had years of successful support from the current one. Ease of doing business: there is no ease, because you will need to consider the following. First, (if at all possible) mastering the cultural and language aspects: operating within the nuances, understanding what yes and no mean, accepting the pace of meetings. Second, successfully navigating the complex co-dependencies and unspoken rules that need to be followed. Third, accepting and overcoming the (to the external eye) unreasonable legal and regulatory frameworks in each country. China: its GDP alone is almost half of Asia’s, so the APAC’s GDP is heavily skewed by it; its growth influences many reports and optimism related to the APAC. It’s also a peculiar market to enter, where in addition to the above geopolitics play a big role. Finally, while it’s large overall, it’s almost impossible to sell external tech there because the locally developed stuff is so good. Australia is only linked to the APAC by its geographic proximity. In its culture, economy, political alignment, and way of doing business, it belongs in Europe. Note: Taiwan and Hong Kong are missing. I didn't include them in the bubbles because of their elevated exposure to geopolitical trends, and the lack of my personal experience. What's your experience in this market? What have I missed?

Climbing out of Silos We all know silos – not the ones that hold grain, but the ones that emerge when there are different lines of business or stakeholders in an operation and each is solely focused on its own objectives. Silos tend to emerge as an unintended consequence of narrow KPIs. If you have a team, how do you measure their performance? Is it the immediate measure (how many invoices are closed in a month?) or the wider corporate outcome (the balance of accounts receivable at the end of the year)? Most organizations tend to measure the more immediate impact, as it's easier to capture, directly motivates an employee (they see exactly what they need to do to "succeed") and allows for departmental Profit and Loss (P&L) measurements. A silo is an unintended consequence of a well-defined job description. Ironically, individual silo success might lead to overall organizational decline. Working in silos is a problem so prevalent in large organizations that most leaders will have to tackle it at one point or another. I certainly have. The following tactics have helped me and will hopefully help you too: 1. Encourage individuals to climb out of the silo. If we take the metaphor further and think of the organization as a field of silos, the first step is to climb out and see the whole field. Climbing out of the silo means understanding not just how your team is measured, but how it contributes to the overall success of the organization. Helping your team understand where they fit in the overall picture helps them see how sticking to small details or insisting on "doing their job" might actually hurt the overall organizational outcome. That big picture is often missing, and it needs to be explained over and over, with emphasis at every step of success. 2. Allow teams to peer into other silos. While every team contributes to organizational success, no team is solely responsible for it. Seeing what other teams do, how they do it, how they're measured, etc. brings a sense of camaraderie and understanding that no amount of KPI setting can. It converts the conversation from "that team always does this" to "in this situation, person abc did that because x". It changes relationships and interactions from blackbox input/output flows to more human understanding. 3. If you're wheat, try thinking like barley. This is about inviting teams to think about how they can make the lives of other teams easier. In most corporate environments, work tends to happen in steps/deliverables/outcomes that are handed over from one team to another. If we can imagine what the other team needs to succeed and how they do their jobs, we can start to imagine the steps we need to take to make it easier for them. Conversely, what small change can the other team make to make our lives easier? Better yet: xix wheat and barley: simply allow people from one team to shadow/observe other teams. I've found that official "job exchanges" are most effective, but even a shadow/”day in the life” exercise will help teammates open their eyes to how other people work. 4. Setup red phones between silos. At one point in the cold war, to avert accidental nuclear war, an emergency "red phone" communication channel between the US & USSR leadership was set up where they could immediately and directly speak to each other during a crisis*. No matter how well intentioned we all are, miscommunications and conflicts will arise, and instead of the typical escalation (going up the management tree or putting people on CC), having individuals sort it out 1:1 really helps sort out some of these problems. These "red phones" are trusted individuals in each team/silo who are not necessarily the most senior but tend to understand the processes best and are good communicators. Modern, large organizations can't operate without some level of specialization. Focusing on one task allows an individual (and by extension, their team) to become better at what they do and is a definite competitive advantage. With this in mind, I don't think we need to do away with specialization altogether, but we do need to be vigilant of the side effects; in this instance, silos. If the vision is clear and communication is encouraged, working in silos doesn’t have to be the default way organizations operate. How are you helping your team climb our of silos in your org?