10x Productivity and Engagement with AI-Generated Visuals and Voices

In this short blog post, I want to share my experience using two remarkable AI technologies: AI Automatic 1111 GUI and Eleven Labs speech tech. These tools can help you create stunning visual and audio content with minimal effort and cost.

Automatic 1111 GUI is a browser interface for Stable Diffusion, a deep learning model that can generate realistic images from text descriptions. You can use it to create anything from surreal paintings to photorealistic portraits, just by typing what you want to see. You can also edit existing images by sketching, inpainting, outpainting or upscaling them. AI Automatic 1111 GUI lets you choose from different models, adjust various parameters, clone or design new voices, and merge checkpoints. I have been using it to create all my recent blog post images.

Eleven Labs speech tech is a voice technology research company that develops the most compelling AI speech software for publishers and creators. Their Prime Voice AI platform can convert any text to speech in any voice and any emotion with unprecedented fidelity and context awareness. You can use it to voice news articles, newsletters, blogs, audiobooks, videos or games. You can also clone voices from surprisingly small samples or create entirely new synthetic voices from scratch. The authentic storytelling potential for this is significant and although It does not yet supports the nuance of Stephen Fry’s reading say Harry Potter. However, with a little effort and the creation of voices, I would image we are not that far away.

It’s truly amazing how these technologies can work together to create uber-personalization for an audience. I used AI Automatic 111 GUI to generate these images on my blog. It’s crazy what you can do with it for example these images are based on me having trained the model on some pictures of myself and if I ever wanted to know what I might look like as a woman; well know I know! The important thing to realize here is that they are not “photoshopped” they are totally computer generated.

I used Eleven Labs speech tech to clone my own voice from a 15-second recording it’s a bit bazar to hear yourself say something you have never said! But the potential for this is enormous. As a Dyslexic person who does not find reading a pleasure, I can see a host of user cases. For example, in the games industry, when playing a computer game, you could have the narrate spoken out loud in your own voice. Many AAA game have voice actors for large amounts of text, but the quests are still written and require the payer to read.  I have also experimented having my blog read in my own voice the result was a personalized audio experience that sounded surprisingly just like me. It is of course, possible to combine  Automatic 111 GUI and Eleven Labs tech along with some animation to combine the two.  

Eleven Labs and Automatic 1111 GUI have the potential to be game-changers in the B2B, B2C, and B2B4C spaces, transforming industries in ways we’ve yet to fully imagine. As someone who began my career around the same time as the internet was emerging, I’ve witnessed first-hand the transformative power of technology. AI is already revolutionizing the way we work and create, with machine learning driving ever-faster improvements.

Businesses of all sizes need to embrace these new technologies and transition to an AI-powered future. Just as everyone was building a website in the early days of the internet, I believe that soon everyone will be shifting to an AI-powered agent. These agents can work 24/7, delivering a 10x productivity boost for businesses of all sizes. And for those Woking in marketing looking to create engaging content without spending too much time or money on production, these technologies are a must-have.

At Curious Cognition, we help businesses understand how AI technologies can be applied to create game-changing products and services and support a 10x productivity increase. If you’re interested in trying them out, visit their websites or contact me to schedule a call for an overview of what’s possible.

In my next article, I’ll be sharing my experimentation with an open-source project called Auto GTP, as well as two products: LangChain and Pinecone, which make the creation of intelligent agents possible today. Stay tuned for more insights on how AI is changing the game!  

Your Roadmap is Outdated: Time to Take Charge and Adapt

Digital Roadmap

As I sipped my morning coffee and scrolled through my LinkedIn feed, I couldn’t help but think about the future of B2B software. We’ve come a long way from the days of desktop applications to web-based solutions, mobile, and now, we’re entering a new era – the age of AI-powered software.
The B2B software landscape is on the verge of a significant shift, and business leaders must be ready to embrace this change to stay competitive. I smile when I hear people at the gym talk about companies banning the use of OpenAI tools. It’s like the shift to the cloud all over again. Those that embraced it got the advantage, and those that were laggards are now being forced into adopting it to remain competitive., Let’s dive into the implications of AI-driven transformation in B2B software and what it means for organisations and their roadmaps.

The Shift to AI-Powered Solutions: More Than Just Productivity
While it’s true that AI-driven solutions can provide significant increases in productivity and cost savings, it’s essential to recognise that the benefits go far beyond that. Integrating AI into software solutions leads to more open-ended capabilities, enabling creative problem-solving and fostering innovation.
AI-powered software can analyse vast amounts of data quickly, support unbiased decision-making, and continuously improve through machine learning. The result? More efficient and effective business processes and an enhanced ability to adapt to rapidly changing market conditions.

Redefining Software: The Emergence of AI-Driven Smart Agents
The rise of AI-powered smart agents will redefine how we interact with B2B and B2C software. Chat-based interfaces and intelligent assistants will replace traditional menus and buttons, allowing users to communicate their needs more naturally and intuitively. 
Smart agents will learn from users’ behaviour, adapting and personalising the experience to better suit individual needs. This transition to AI-driven smart agents will make business software more agile, flexible, and user-friendly. This open-ended nature of ‘products’ is one of the things that product managers need to consider in their roadmaps.

Adapting Roadmaps: Preparing for the AI Revolution
Business leaders, especially CEOs and heads of product; must revisit their roadmaps and consider the impact of AI-driven technologies. As we witness the rapid adoption of AI-powered solutions, it’s crucial to identify where AI can provide a competitive advantage and integrate it into product offerings and internal processes. Traditional products will become outdated almost overnight, and new plays will inevitably win big. Salesforce, in its early days, embraced web-based technology and pushed the SaaS model with its No Software marketing campaign to become the powerhouse it is today.
In the coming years, AI-driven solutions will become a necessity rather than a luxury. Companies that fail to adapt risk falling behind in the race for market dominance. This 10x productivity increase is too significant to ignore, yet I hear clients and prospects banning its use rather than educating themselves.

Final Thoughts: Embrace the Change
The software landscape is evolving, and integrating AI-powered solutions will bring about a new era of innovation and growth. By embracing AI, organisations can gain a competitive edge and redefine employee productivity.
Have your teams reviewed their roadmaps and started to adapt to the AI-driven software landscape? Let’s continue the conversation in the comments below.

#AI #B2BSoftware #Innovation #MachineLearning #DataAnalytics#DecisionMaking #SmartAgents #UserExperience #AgileSoftware #Roadmap#BusinessStrategy #DigitalTransformation #FutureOfWork #10xRevolution

How to deliver 10X productivity (Part III) for Knowledge workers?

Part 3 of a 3 part essay on the future of work in a post COVID-19 World?

A well communicated message can make the difference between inspiring a workforce and making it sigh whenever Monday morning comes around. Take one small example, how do you name a document? Everyone has their own way. Some people use personal conventions, some randomly pick a name, while others might follow a standardised procedure they’ve brought in from another organisation. It’s a small thing but what a document is called can make all the difference. Who hasn’t tried seven different ways of searching for that one important document needed for the Board meeting that starts in five minutes? But here’s where it gets interesting. What if there was technology smart enough to name documents for us based on criteria we define, and therefore automatically standardising the process across your whole organisation? AI has the potential to structure the unstructured without the work becoming reductively homogenised.

I have spent much of my working career building software products, marketing them or taking them to market. I worked in the USA helping to build an automotive electronic document management (EDM) solution as a SaaS product before the term SaaS was a common term. It was designed to manage certain types of documents (APQP & ISO 9000 to be precise, Source Quality-One) that the automotive supply chain shared. It was at this stage I got my first introduction to lean and agile thinking. For example, when a tier 4 supplier, such as a steal producer would update a document, this would then update a tier 3 producer, like an engine maker, and so on up the chain. The documents were independent but connected through a backend database, and this idea and the advantages this interconnection had, always stayed with me. As someone who has worked in many different industries (CRM, EDM, Automotive, Banking, Travel, API’s, Video Transcoding), it’s this cross-industry perspective that gives me a solid purview that often allows me to see opportunities where others might not. It’s from this background that my 10x productivity hypothesis was born.

Tomorrow’s Technology Today

Many people don’t realise just how much AI technology is already in our daily lives. Just take one of the commonest uses of AI today, rough planning. For the smartphone generation who have always had maps built into their phones, they might be unaware just how differently we used to do things. Getting from A to B in a car without the use of a disembodied voice telling us to turn around, used to be a semi-painful/humorous activity that could (in my experience anyway) result in arguments, accusations and general grumpiness. But then came along companies like TomTom who utilised AI to plan your journey for you; goodbye maps. You still had autonomy over your journey, but also a tool that responded to and updated your plan accordingly. And then as smartphone technology improved, the need for a separate tool decreased; goodbye TomTom (TomTom still trades today in fairness). This streamlined the process further, but the initial premise of AI doing the work still remained. 

People Supported with AI

I recently got to see a knowledge worker-based AI tool in action. It was an expert system (a form of AI popular in the 1980s that has become more powerful due to recent AI advances) called AIC from a consulting company called Rialto Consultancy. This AIC carried out sentiment analysis and degree of alignment analysis between leadership teams, which then went on to create a set of deliverables that helped to overcome the usual 70% failure rate seen in change programs in larger organisations (Source: McKinsey).  This system was built as a result of research undertaken by Harvard professors, Thomas Schelling and Chris Argyris, one of whom received a Nobel prize for his work. 

In effect, the expert system drives increased performance and the likelihood of a successful change programme through better orchestration of the individuals involved (Source: Bain & Co.), lining up the detractors and getting them on side. Because change relies on human beings, therefore the human condition can drive success and/or failure in equal measures. However, a tool like AIC shows how having all the data and insight overseen by AI, can increase the chance of success significantly. By establishing a protocol that ensures every contributor is receiving the same information, this information therefore becomes accessible to everyone, leaving any egos in the room free to concentrate on other things. One thing is for sure, in the future more decisions will be made by algorithms and humans working together which will aid leaderships teams and Boards to make better strategies and/or successful growth approaches. But if after the recent A Level fiasco in the UK has left you wary about the use of algorithms, don’t be; the problems occur when algorithms are left without human oversight. Properly managed, AI working with knowledge workers can free them up to concentrate on problem solving work and thus increase productivity.

The Future of AI

Anyone who has spent any time looking at the open AI API GPT-3 will have seen first-hand how powerful this technology has become. GPT-3 however, has been built mostly using open source data sets that have plagued the AI space by introducing bias and a number of high profile and embarrassing bunders.  Nevertheless, as demonstrated above, AI can successfully be used for a number of knowledge working tasks, with the breadth of these tasks increasing with every new innovation. And if applied the to the challenge of 10x productivity, I believe we could see a significant step change in productivity. 

However, in order for people to interact effectively with the technology, we will need AI that is explainable (XAI) so decision making processes are accessible and transparent. We need to be able to look into the ‘black box’ (the algorithm) and understand how and why a classification was made. The range of algorithms and applications for AI is staggering. If you’re interested in keeping up to date with what is going on with AI, I would encourage you to check out AI Forum which is a useful resource for the latest research and news.   

From More of the Same… To a New Breed of Solution

We are starting to see a new breed of productivity apps but in some cases, they are taking us way from the prize of increased productivity rather than helping to deliver it. Instant message-based tools like Slack and MS Teams hold great promise but that they take us out of our state of flow and do nothing to help structure the real work. They facilitate the communication, yes; they help support the connections and the messages I discussed in the last essay, yes; but they are yet another distraction from the real work. 

So, to help with all these interruptions, a huge range of easy to use project management tools have grown up in recent years. These move away from the MS Project style (mostly waterfall based) bloatware, to tools with superior user experiences like Jira,  Monday and Asana. However, in many cases they are little more than shared task lists with the ability to attach the documents (the work). Essentially, the project management tools are gathering all the information together in folders (tasks) and files (subtasks), but the method of transmission of the work, the document, is still the limiting factor as it remains unconnected and therefore open to duplication, wasted time etc. 

I am interested to see how documents and modern machine learning algorithms might forge a whole new type of productivity application. We are starting to see hints of the future with Microsoft 365 PowerPoint with its ability to auto design slides. If you’ve not seen it, I recommend you should because it demonstrates a small glimpse of the future. Another example is Google mail with its auto complete capability that can finish sentences for you.

So, if your appetite has been whetted, here are some productivity tools that are starting down this road, all addressing small discreet elements of the 10x productivity challenge. You may have heard of companies like Grammarly, an AI based spell checker that can help you create different types of writing. But actually, the companies closest to delivering 10x productivity are probably companies you have not heard of, like Notion who pitch themselves as an all in one workspace for notes, tasks and wikis, or Coda who pitch themselves as a new document for teams, and who recently secured $80M from famous Google venture capitalist, Kleiner Perkins. And AirTable who claim you can organise anything with a spreadsheet-like database, who also recently secured a $185M Series D round of VC investment. Then there are other companies like Monkey Learn and Agolo who offer tools for language classification and text summarisation, all of which hold promise for the development of self-organising documents. Even Microsoft with Microsoft Fluid are starting to recognise the traditional paradigm is broken and finally for good mesaure dont forget or overlook Miro great API and innovative visaul work tool.    

The Future of Productivity

Every organisation wants to be successful and maximise productivity. And every person in every organisation wants to work on things that matter, that make a difference, and that one day might just help to get them promoted. Yet so much work inside organisations are non-value-adding, and some are even value destroying. The law of diminishing returns means as an organisation gets bigger, it becomes harder for everyone to start on the same page, meaning 10x productivity slips further and further away. For start-ups who are focused on delivering the founder vision this transaction can be particularly difficult as teams grow and grow. 

So how is 10x productivity actually achievable? It’s finding the correct balance between the four kinds of knowledge work, Reactionary, Planning, Procedural and Problem Solving Work, that demands our focus; and making sure everyone understands the Purpose, People and Connections, Process and the Message.  That’s where tangible and accessible 10x productivity lies when combined with the right technology tools and training.  everyone’s ability to understand how they contribute to the big picture is challenged along with their ability to be truly productive. And with working from home, these challenges have never been more significant. Keeping a team motivated, focused and well informed is not easy, keeping a team motivate and focused and well informed is not easy. 

So unfortunately, at the moment you can’t buy an off the shelf solution to deliver true 10x productivity, but I believe in the not too distant future we will see this kind of innovation more and more. I hope my observations can help you address productivity in your own work environments, and maybe by using some of the tools and ideas I have shared in the previously essays, you can begin to organise your own 10x productivity workspace. I’d love to hear your views and how you address productivity in your own organisation. Let’s keep the conversation going, J. 

How to deliver 10X productivity (Part I) for Knowledge workers?

Essay (1 of 3) on the future of work in a post COVID-19 World.

Even before Covid 19 changed our working lives, for a long time the way the majority of companies work has been buckling under numerous pressures. For example, how many times have you felt your heart sink on opening your inbox and seeing all your productive time vanish until after lunch? So with remote working the new normal for the foreseeable future, now more than ever, the way we work desperately needs to evolve to keep us on track and in doing so, boost our productivity. Businesses need new tools and processes that support asynchronous working. But how will these work, and what will they achieve?

How about 10x productivity? Sounds like a dream, right? Sounds like unattainable marketing speak you’ve heard 100 times before. Stand up meetings! Do emails in bulk, not as they come in! Never eat lunch at your desk! But actually, 10x productivity is very attainable, if you look at it from a holistic organisation-wide perspective. Which is easy, once you understand what it is you need to achieve.

An organisation that is highly productive is greater than the sum of its parts. Such businesses have a clear understanding of their purpose, their mission. They have an inspiring vision of the future, and a strong constructive culture that empowers and motivates employees to do great work. Finally, highly productive companies have an understanding of the core competencies that make up the foundation for a high performance work environment. They focus on productivity because its though productivity business can increase profitability, lower operating costs, reduce waste and environmental impact, improve competitiveness and increase engagement, to name just a few reasons. In the new world of work where growth for growth’s sake needs to be questioned, productivity might be the obvious replacement that organisations should be fixating over.  

Another critical element to becoming a high performance organisation is a clear understanding of waste. If you haven’t already read it, I would strongly recommend getting a hold of a copy of The Toyota Way by Jeffrey Liker which sets out Toyota’s strategy in the 1980s. Toyota demonstrated the power of total quality management, just in time, and work in progress (WIP) limits, all based around constraints theory with roots in ideas proposed by American engineer, statistician, and management consultant, W. E. Deming. By interrogating every component of their organisation and aligning them, they were able to visualise what they needed to achieve in order to increase productivity. This strategy ultimately delivered a transformation in the automotive industry. 

So 10x productivity need not be a pipe dream; it’s something that with game changing strategic thinking can be made real. At least that is my hypothesis, but let me share my observations on why I have made this educated guess.

Jason Nash

Let’s look at the building blocks of the most critical aspect of today’s workplace: knowledge work.

To start, we need to understand what knowledge work is and how can we measure productivity of knowledge workers? The definition of knowledge work from Wikipedia is ‘Knowledge work can be differentiated from other forms of work by its emphasis on “non-routine” problem solving that requires a combination of convergent and divergent thinking’. Knowledge workers are therefore the people whose jobs involve handling or using information, often using a computer. And they are at the very heart of how one might deliver on 10x productivity ambitions, especially as this kind of role is undergoing significant change as technology and global pandemics continue to redefine how and where we work. 

We can measure performance as the amount of work (or output) that an employee completes during a period of time (their input). In organisations that produce physical work a simple example might be the amount of bricks a bricklayer lays in the course of a working day. However knowledge work in the service business sector can be harder to measure, although a simple example here might be the number of lines of code a software engineer produces, or the number of words a copywriter has produced.

All knowledge work falls into four different blocks: reactionary, planning, procedural, and problem-solving. Let’s go through them and highlight some pitfalls.

Reactionary Work: 

This makes up a large and growing proportion of our daily work. Reactionary work is replying to an email, or instant message. It’s often minimal value-adding and, in many cases, not particularly well-governed. For example, requests for information or tasks often go unanswered. Why? Because they disappear into inboxes with over 121 other requests on average every day (source: Radicati). Products like Slack have effectively become the equivalent of the virtual water cooler conversation, but have not necessarily increased the productivity they promised, in part because they have increased the amount of reactionary work.  The average organisation can lose up to 20% or 1 day a working week of its productive capacity (source: HBR). Now, you may think of this as naturally occurring organisational drag, but I hypothesise that much of this is down to time spent on reactionary work, and it will have increased due to new internal communication channels and distractions such as Slack and MS Teams. 

Planning work:

The next block of work is planning, relating to both near or long term plans. A lot of this work, especially long term planning, can be a form of waste. A now somewhat dated 2014 article (source: Bain) said as much as 97% of strategic planning is a waste of time in multinational companies. Yes, 97%! It’s easy for businesses to get caught in a planning loop where last year’s plan has not yet been fully executed before the new plan needs to be worked on. I know first hand how dysfunctional this can be, especially when the ideal environment for 10x productivity highlighted at the start of this 3 part essay is missing. And it’s not just the wasted hours and energy that needs to be taken into account, but the wasted employee goodwill when they see their hard work superseded before implementation.

Procedural Work: 

Procedural work can be as simple as planning out your day, ensuring you are working on the right things at the right time, or as complicated as building detailed operating procedures to ensure consistency across a team or department. It is as varied as the work demands. But as any manager knows, localised procedural work can only be effective up to a point. If clear communication strategies are not implemented business-wide, productivity decreases as time is lost due to reasons such as competing egos, misfiled documentation, and duplication of work. And procedural work can be high value, especially if it’s focused on new processes that help a business define new distinctive competencies. 

Problem Solving Work:

And the final block, problem solving work. Problem solving work, which includes training, adds the most value to our work experience by engaging and motivating us. It’s when we find new and creative pathways, which impacts on an organisation’s overall output and innovation. And yet despite how important this work is, it is often the work we spend the least amount of time doing. Why? Because it requires us to focus and ideally enter a state of flow. But how can we do that if we keep being interrupted by the above? This is why you need to focus your attention, and not just your time to truly increase personal and business productivity (source: Forbes).

So those are the types of knowledge work you and your employees wrestle with on a daily basis. In my next post I going to cover the six intersecting blocks that are fundamental to actually getting the work done and driving 10x productivity. Organisations who have found how to align these have successfully gone on to improve productivity at every level.

Product managment can feel like trying to eating an Elephant!

Elephant carpaccio has nothing to do with cruelty to elephants, it’s the process where software people practice and learn how to break user stories into thin vertical slices. It came from Elephant carpaccio Alistair Cockburn. I want to use the concept to challenge your thinking. Should we as product managers be working with elephants? One of the many roles of product management can be described as creating elephant carpaccio; however, you’re still trying to eat an elephant, and I would argue most of us can’t realistically hope to eat an elephant… however big your business appetite. A better approach to eating elephants is to shrink it down to something more bite-sized, let’s think burger. You need to move away from managing demand, in this case the elephant, to managing supply. But to get there, you need to manage your stakeholders and reach an agreement.

At its core, the job of product management is one of balancing the needs of all the many business stakeholders’: customers, prospects, the market, sales, support, executives… the list goes on and on. Yet if we think customer first, a lot of these sometimes-conflicting needs can be addressed quite simply. What do customers want? They want good quality solutions that address their pains or jobs to be done, it’s that easy. However, it’s easy to forget this in the never-ending product release cycle that product managers live within.

What’s your target? Get, Keep or Grow?

First, if you buy into the concept that keeping and retaining customers is more valuable than getting new ones, this starts to define a set of variables you can use to determine how you prioritise. Of course, you need all of your business stakeholders to buy into this. This includes making sure your sales teams are not outselling and promising things that you’re unlikely to deliver in the short term or medium term.

It’s should be obvious, but too often this simple rule is not followed. You must sell what you have today, not what is coming tomorrow (its common sense right?) but as I have discussed in other articles, common sense is not so prevalent especially in business were strategy and goals are not well aligned. I hear a question, I thought selling technology was about selling a vision? Yes, it is. You can sell the vision and its important to do that. However, the requirements need / have to be managed by someone and that someone should be the product manager. Having the right product narrative comes in to play here – what’s the big problem you’re trying to fix (the villain)? What the vision for solving the problem (the hero) then you get to them and at this point, you sell what you have, not the future.

With this foundation, you can start to create a set of variables, a matrix and some metrics to help you prioritise all of the requirements, but you can’t hope to do this unless you have got control of the new requests coming in. So often I see businesses drowning in a sea of requirements – features requested by customers through sales without a clear understanding of the problem or the job to be done. We all understand the pressure of selling a solution however feature bloat is a real issue and has hidden costs across the business, more code to manage, more support calls, more sales complexity, more marketing… the bigger the elephant is, the harder the problem you’re trying to solve.

Line up your stakeholders

When we think about stakeholders in business, we don’t think about the kind we see in the classic vampire films but maybe we should. Departmental egos, unrealistic expectations and conflicting strategic priorities can soon make the product manager feel like they just can’t win. The best way to address this is to take stakeholders to one side and walk through the variables and the prioritisation matrix you have defined. Getting everyone at different levels within the organisation onside is critical. In smaller start-ups, this is less of an issue as everyone is focused on the single product vision, but in more significant business, this can be complex and time-consuming. To help you manage them, you need to be equipped with the facts, and you need to work across the business with all your internal and external stakeholders in order to get agreement on what matters. If you work in a business where everything matters, then it might be time to start looking for a new organisation. The definition of insanity is doing the same thing repeatedly and expecting a different outcome. If a business is trying to do everything then they are trying to do nothing and if your strategy is not clear, or is trying to fight on too many fronts, then you can’t win. I will cover strategy in a future article but what fascinates me is that there are only a few key strategies (market expansion, diversification, market penetration, product expansion) yet business go wrong in trying to pursue too many of them at the same time and not funding them at a level that can lead to real success.

Ratios and ranges can be your friend

Ratios are good things to use to help you get a handle on the health of your products and your product management process, in the same way, ranges are a good thing to use when you’re estimating. Building software is not like building a car where you design the components and assemble them; the goal to make each car the same as it rolls off the production line. Anyone that has built software knows there are many ways of solving development problems. Should I use an Array, Dictionary, Set or roll my own collection type? Because of this, a lot of general management thinking, much of which originated with General Motors in the 1950’s, does not help you manage digital products or development teams. You need to be equipped with the facts and you need to work across the business with all your internal and external stakeholders to get agreement on what matters. So, many CEO’s and even CTO’s or CIO’s don’t understand concepts like constraints theory or the real value of time i.e. opportunity cost. So, product managers end up at the sharp end of all of the conflicting priorities.

a lot of general management thinking, much of which originated with General Motors in the 1950’s, does not help you manage digital products or development teams

If your ratio of new defects to existing is increasing, then you have a quality problem and your most likely overloading the system with too many stories per sprint. The same holds for new support incidents to existing ones. Its’ ‘working as designed’ comments, demonstrate a breakdown in the requirements management process and / or a lack of design input. Adding new customers, if existing ones are leaving in droves, is counterproductive and especially if the new customer brings with them new requirements. This can make the problems even worse. This can be a real problem when moving to new countries with new market requirements. Using the bowling alley approach highlighted in ‘Crossing the chasm’ can be useful when making these kinds of decisions and doing them at the right time.

Herding Elephants

It’s just common sense, but if sales have been tasked with selling, then you can understand how these conflicts emerge. It’s also where looking at ratios of departments within a business can be useful, what’s the ratio of Development staff to the ratio of sales? The traditional business needed large sales teams but in modern platform business, these ratios are changing. With improved customer experience comes self-service, onboarding, social support and a range of cost-effective ways of serving customer needs. Much of which can be supported by product and product managers, but you need to make space for them in the backlog. Also, the company needs to have a focus on improved customer experience at a fundamental level, not just lip service, or it just one more thing to be prioritised with everything else i.e. a bigger elephant.

How to prioritise will have to wait for another time, the different mathematical approaches to prioritising have real value: MoSCoW, Class of service, Weighted look-ahead approach, Incremental funding model, Cost-benefit analysis, HiPPO decisions, Equity, Weighted shortest job first or CD3. Don’t get me wrong these all have a place, and I have used many different types in my past. However, the maths you use to prioritise does not get around the human aspects of managing expectations. It can’t magically make your development teams 50% more productive or increase your development budget by 100%. So, they will help you make smarter prioritisation calls, but they can’t fix management bad practice and unrealistic expectations. For those, you need to win over the hearts and minds of your colleges, and for that, you need to be able to explain why you have prioritised in the way you have and told them: what they will get, what they won’t, when they would get it and why. Get your people to start thinking burgers, not elephants.