The AI Hierarchy: crafting a successful AI strategy from the ground up

Part II: Crafting a Compelling AI Product Vision and Narrative

Illustration of birds working on a whiteboard to plan out data strategyIllustration of birds working on a whiteboard to plan out data strategy
Table of Contents


The First AI Product Vision

Let’s first head back - way back. It’s the summer of 1956, and it’s the Dartmouth College Summer Conference. Our story begins with a gentleman by the name of John McCarthy. McCarthy was an exceptionally bright, young mathematician who, along with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, were responsible for proposing the first AI workshop. McCarthy is also credited with coining the name ‘Artificial Intelligence’, and what is especially interesting for our purposes, is the proposal this group put together. Here is a brief excerpt:

“We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve the kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

I love this proposal. It’s incredible how prescient and ambitious it was. Living now in the 21st century, we know that these gentlemen did not succeed in making ‘significant advances’ over the summer of 1956. In fact, we are the first to be living through what they proposed all those years ago. I know I’ve been blown away and captivated by the incredible powers of GPT-4 and other large language models widely available today. But this is not why I appreciate the proposal and why I bring this to your attention.

These men had a clear vision. They communicated it well and painted a picture of the future they wanted to build. This is the key foundational piece of every successful AI project. A vision, a narrative, a description of what could be in the future. Although these gentlemen lacked the tools and technologies that allowed that vision to become reality, we have those tools available today. To apply these tools and technologies successfully, we need to take the first page out of their proposal. We must first start with what we want to achieve. 

The Amazon Approach

Though the Dartmouth group put together a single paragraph proposal, Amazon and other Silicon Valley companies have more formally applied this approach and tried to standardize the process of writing down and describing the future vision before executing. In their book Working Backwards, Colin Bryar and Bill Carr talk about Amazon’s approach to product management and how they use writing to more rigorously think about projects before they are formally proposed. 

Here are the key points that Bryar and Carr make about the effectiveness of a written narrative vs. the typical powerpoint presentation:

  • Greater Information Density: Narratives contain much more information than a typical PPT slide, allowing for a deeper dive into topics.
  • Reading vs. Listening: People can read faster than they can listen, so narratives enable audiences to absorb more information in less time.
  • Nonlinear, Interconnected Arguments: Narratives are better suited for complex, interconnected discussions that don't fit well into the linear format of PPT slides.
  • Forces Clarity of Thought: Writing a narrative requires deeper thinking and anticipation of audience questions and objections. It helps in forming a more comprehensive argument.
  • Bonus: Writing is even more accessible than before with the assistance of LLMs and can help those of different backgrounds and writing abilities to communicate their ideas clearly. 

How do you write an Amazon-Style Narrative

There are many articles on the web that give examples and instructions on how to create an Amazon 6-pager, but even Bryar and Carr admit, there is no single way. Depending on the purpose and desired outcome, there can be many ways to structure an Amazon 6-pager. Here are their top tips:

  • Six-Page Maximum: Aim for a functional length of about six pages to respect meeting times and maintain focus.
  • Anticipate Questions and Objections: Address potential concerns and alternate viewpoints within the document.
  • Clarity and Cohesion: Ensure the narrative is clear, persuasive, and logically structured. It should demonstrate how different points are interconnected.

How do we apply this to an AI Strategic Narrative?

The most important piece of the Amazon approach must be retained. Describe the problem, why it’s worth solving, and who has the problem. Start here - every time. Don’t worry about proceeding to the next section until you have enough clarity in your proposal that you could explain it to your grandmother. 

Problem Validation

This section tells us why we are all here reading the narrative in the first place. There is a problem out in the world and we believe that we can solve it. To get everybody aligned and introduce them to the rest of the document, think about answering the follow questions:

  • Who has this problem? Can we talk to them and ask them about it?
  • Can we distil our research into easy to understand problem statements (eg. When taking action x, I want to do something I can’t yet do, so that I can achieve a goal.)
  • Can we verify that this problem is something they would pay to have solved for them? How much would they pay?


Marty Cagan says that product teams spend way too much time on the problem space and not enough time on the solution space. The solution is where the magic is and if you get this wrong, you’ll never be successful. In this section you need to take a stand. Given the problem you’ve identified, what is your hypothesis? A good starting point is to use the following statement:

“We believe that by implementing this strategy and executing on our product vision, we will successfully move these key metrics in this specific direction.”

Things to avoid:

  1. Technical implementation details: this is a section where we describe what the solution is, not how it works. Let’s not get bogged down by technical details and ensure that a clear picture of our idea emerges and is digestible by a wide audience.
  2. Thinking small: later in the narrative you’ll take time to describe how you can incrementally get to the grand vision. In this section, however, it’s important to paint a picture of the fantastic future you imagine if the solution is built.

Target Outcomes

Layout the objectives

John Doerr, the famous Google investor, introduced Google's founders to the concept of OKRs early in the company's life. He explained them as a way to get organizational alignment and ensure that the company was all moving in the same direction. As part of your AI strategy you need to articulate the objectives - which is just a fancy way of saying the goals/outcomes you’d like to achieve. A good heuristic is 3 high level objectives and 3 key results (tangible metrics) that can be used to measure your progress against the goals you’ve set. Make sure that the goals are for no more than 6 months so that you can come back and re-evaluate consistently and modify if needed. Keep them clear, measurable, and ambitious. 

Set a target

Which layer of the AI Strategy Framework Pyramid will this vision take us to? Does the use case end with data visuals? Is it a predictive program or recommendation software? Does it use Generative AI or Deep Learning to achieve our vision? By establishing the layer our vision takes us to, we can then work our way back down each layer of the pyramid and begin to detail the components we need to reach our target and fulfil our future vision. Make sure to mark which step your journey takes you to, whether it’s the data analytics, machine learning, or AI step. Then go back down to the bottom and begin uncovering how you will execute on data collection, and move step-by-step up the pyramid establishing tools, technologies, and strategy along each layer of the pyramid. 

Minimum Viable Robot (MVR) - Delivering value at each step

No more thinking big, this section is where you get very granular and have a tangible plan to get to value as quickly as possible. The MVR - a play on the overused Minimum Viable Product acronym - is how we move fast. We need a way to deliver measurable value to the organization well before leadership gets tired of waiting for the promised vision to be realized and pulls the plug. 

To do this, we can take a modified approach to working back up the pyramid in our AI Strategic Narrative document. Instead of focusing on the long-term vision, and all of the data, infrastructure, and data scientists we’ll need, we can instead focus on our quickest path to value. This smaller vision should not divert us from the path that leads to our bigger vision, we’re building the model airplane before we build the jet. 

By focussing on a first smaller use-case, we get the benefit of a small target. Something that allows us to collect less data, introduce less complex technology, and lays the foundation for the future. We are taking a slice out of the AI Strategy Pyramid, building for the future while delivering value today.

Working Back Up - How we build the solution

Here is the section where some technical detail will be important. It is important to think through the tools, technologies, and processes that are going to allow you to reach your MVR target. Think your way through from the bottom up to your target.

Data Collection

Given the target you’ve set and the vision for the MVR, what data will you need to get there. By starting at the top and then moving back to the bottom, you should have a strong sense of what data will be required. If you are struggling, go back to the last step and get more specific about what you’re delivering. 

Questions to think about:

  1. What types of data do we already have?
  2. How is this data collected (sensors, manual input, cookies, third party data)?
  3. How often is this data collected and what is our data size at each interval?
  4. Are there any real-time data streams?
  5. What file formats are we dealing with?

Data Storage

Next, you need a place to put the data that you’ve collected. Depending on the frequency, size, and file formats you’ll need to make a decision on the data storage location that is best for you. You may need many intermediate databases, or different fit-for-purpose databases and this is okay. Just make sure that you understand the purpose and function of each and how they fit into achieving your vision. 

Questions to think about:

  1. Where is the collected data stored (cloud, on-premises servers)?
  2. What is the current data storage capacity?
  3. How is data security managed?
  4. Is there a system for data backup and recovery?

Data Transformation

Real world data is messy. It’s never in the format you need and often doesn’t have the associations to other data points you need to perform strong data analysis. Taking the raw data and cleaning up the values and structuring it in a way that data analysis can be performed is a key step on your journey.

Questions to think about:

  1. What processes are currently in place for cleaning and preprocessing the data?
  2. How is data normalized or standardized?
  3. Are there tools or software used for data transformation?
  4. How is the quality of transformed data ensured?

Data Analysis

Now you have a somewhat structured dataset with obvious errors cleaned up and a queryable structure. It’s time to dive in and see what the data has to say. Once again based on the data you expect to see, you can make decisions on how much compute and storage you will need to perform adequate data analysis. If you have a larger dataset you may select a different toolset than if you have a smaller data set. 

Questions to think about:

  1. What tools or software are currently used for data analysis?
  2. Are there any automated processes for routine analysis?
  3. How are analysis results documented and reported?
  4. What challenges exist in the current data analysis processes?

Machine Learning

You’re almost there. The top of the pyramid is in sight. WIth a strong understanding of the data and a clean set ready for training you can begin experimenting with machine learning. Sometimes you’ll need different toolsets for analysis and machine learning so a decision will have to be made. More importantly, an engineering strategy for getting these models to work with the rest of your solution - MLOPs. This is often the most difficult part, so I highly recommend keeping the model simple and focus on getting the model out in the world. 

Questions to think about:

  1. Are there existing machine learning models in use?
  2. How are these models trained and tested?
  3. What platforms are used for deploying these models?
  4. How is model performance monitored and evaluated?
  5. Are there processes for updating or retraining models?

AI & Deep Learning

You’ve done it! You’ve reached the top. Some of the concepts used in Machine Learning will be the same, you may be able to reuse much of your model infrastructure for deep learning, but also may need to make some key changes depending on your data volume and data types. Applying deep learning methods is more complex than machine learning so you may also need to hire. Training deep learning models can require more expensive hardware. Addressing all of these complexities from the start is daunting, no wonder this step is at the top. 

Questions to consider.  

  1. What specific deep learning applications are being considered?
  2. What is the current computational infrastructure available for deep learning (e.g., GPUs, cloud computing resources)?
  3. Are there datasets large and complex enough to warrant the use of deep learning?
  4. What expertise in deep learning exists within the team, and is additional training or hiring needed?
  5. How will the implementation of deep learning models integrate with existing systems and workflows?


Wrap it all up with a bow and get the team motivated to execute!


Want to see an example? Leave us your email and we’ll send you a copy!

Wrapping Up

Think before you act. That is the essence of this step. Business plans get a bad rep, and maybe they should. But if you instead embrace Amazon's forward-thinking practices and the pioneering spirit of Dartmouth's early AI visionaries, you can propel your business into the future.

Next, in Part III, we'll dive deep into the essence of AI projects: data collection. We'll explore its pivotal role and introduce 'Data-centric AI'. See you next week!

Need Help?

If you're seeking to unlock the full potential of AI within your organization but need help, we’re here for you. Our AI strategies are a no-nonsense way to derive value from AI technology. Reach out. Together we can turn your AI vision into reality.

No items found.
No items found.

Want to stay in the loop?

Subscribe below to get updates as they happen!
You have subscribed! Keep an eye on your emails for future updates.
Oops! Something went wrong while submitting the form.

Mitchell Johnstone

Director of Strategy

Mitch is a Strategic AI leader with 7+ years of transforming businesses through high-impact AI/ML projects. He combines deep technical acumen with business strategy, exemplified in roles spanning AI product management to entrepreneurial ventures. His portfolio includes proven success in driving product development, leading cross-functional teams, and navigating complex enterprise software landscapes.

Next post
There is no next post
Back to all posts
Previous post
There is no previous post
Back to all posts
Depiction of a person sitting working at a computer with birds in the background (illustration)
Leveraging RapidAPI for Startup Growth: A Strategic Perspective for Founders

Developing utilizing Rapid API (and similar tools) can be helpful, but it may not always be the most ideal scenario. There are always tradeoffs - are you looking for quick go-to-market and low development costs, or long-term growth potential?

Read More
Illustration of a baseball player amongst a flock of birds
Part VII: Machine Learning

Explore the journey of machine learning in baseball, from Billy Beane's OBP algorithm to modern MLOps, highlighting supervised and unsupervised learning, deployment, and monitoring.

Read More
Depiction of a person in the oval office with birds flying around - illustration only.
Part VI: Analytics and Insights

Netflix’s data strategy, using structured data and SQL, led to "House of Cards." Effective dashboards and feature engineering drive actionable insights for AI success.

Read More
Image with glasses and computer screen.
AI, Ethics, and the Importance of Diversity in Machine Learning

Discover the ethical implications of AI and the critical role of diversity in building unbiased algorithms. Learn from real-world examples where AI systems failed due to lack of representation and understand why diversity isn't just an option, but a requirement in AI.

Read More
Illustration of birds in flight around the Kepler Satellite
Part V: Data Exploration & Transformation

NASA's Kepler mission used innovative data strategies and AI frameworks to collect, process, and analyze vast amounts of astronomical data, leading to significant discoveries about planets and the universe.

Read More
Illustration of birds flying around Earth
Part IV: Ensuring Reliable and Accessible Storage

Discover how Amazon Web Services (AWS) transformed from a strategic insight at Jeff Bezos' home into a pivotal cloud solution for businesses, enabling innovative digital infrastructure management and strategic growth.

Read More
Ai for management
AI implementation requires a new type of manager

Explore the evolving role of management in the AI era. Discover why traditional management strategies falter with AI implementation and the rise of the AI translator - a new kind of manager bridging the gap between tech and business

Read More
Illustration of birds flying over a graph
Part III: Data Collection - The Essence of AI

Google's founders used camera tech and a van in 2007 to validate image stitching, evolving to Street View and enhancing Maps with AI-driven data insights, setting a foundation for data-centric AI strategies.

Read More
Illustration of birds working on a whiteboard to plan out data strategy
Part II: Crafting a Compelling AI Product Vision and Narrative

Part II discusses crafting a compelling AI product vision, leveraging historical insights and modern management techniques for effective AI projects.

Read More
AI Investments
AI in Decision Making: Lessons from the Investment World

Explore how leading investment firms like Georgian Partners and Bridgewater Associates leverage AI and radical transparency to enhance decision-making. Discover the importance of combining AI insights with human expertise for optimal outcomes.

Read More
An illustration of birds sitting on a tree, a server is in the background.
Streamlining Website Management with Headless WordPress

Tired of endless CMS changes disrupting your marketing flow? Headless WordPress offers consistency, power, and ease of use.

Read More
Ai in PropTech
AI in PropTech: Transforming Real Estate with Intelligent Solutions

Explore how AI is revolutionizing the real estate industry with applications like property value estimation, AI-driven matchmaking, and conversational AI for customer profiling. Dive into the potential of PropTech and how managers can harness AI to boost profitability and enhance customer experiences.

Read More
Illustrated depiction of birds trying to put together a machine
Part I: Introducing the AI Strategy Framework

Get a proven AI Strategy Framework to take your project from idea to value-driven AI implementation. Actionable steps included.

Read More
AI Transformations: The Value of Steady Progress Over Rapid Innovation

Discover the importance of pacing in AI transformations. Drawing from real-world examples, this article highlights the pitfalls of rushing into AI projects and the benefits of steady, incremental advancements in AI applications.

Read More
Can Someone Please Tell Me What AI Actually Is?

Dive deep into what AI truly means, beyond common misconceptions. Explore the fundamental differences between human cognition and machine problem-solving, and why certain tasks remain a challenge for AI

Read More
Illustration of birds sitting on a stack of automation gear.
Cutting Costs with Automation: A Small Business Guide

Discover effective strategies for leveraging automation to cut operational costs and boost profitability in small businesses. This guide provides insights into selecting and implementing the right automation tools to streamline processes, reduce manual labor, and enhance efficiency.

Read More
Crafting Data-Driven Strategies: The Power of Understanding Machine Learning

Discover how a deep understanding of machine learning can empower managers to craft effective strategies. Learn the difference between supervised and unsupervised machine learning and their real-world applications in business decision-making.

Read More
Data-Driven Decision Making: The Foundation of AI Innovation

Explore the significance of data in the AI era. Understand the challenges faced by legacy systems and the importance of strategic data collection. Learn from past mistakes and the wisdom of industry leaders to harness the power of data for future success.

Read More
Data Processing in AI: A Manager's Guide to Ensuring Quality and Effective Training

Delve into the crucial steps of data processing and model training in AI. Learn how managers can ensure data quality, handle outliers, and understand the significance of training and test sets for successful AI project outcomes.

Read More
Illustration of birds on a servers
AI in Business: Revolutionizing the Corporate Landscape

How AI is reshaping various aspects of business operations, from decision-making processes to customer experiences.

Read More
Deep Learning Demystified: From Jigsaw Puzzles to AI Breakthroughs

Dive into the world of Deep Learning and its transformative impact on AI applications. Understand the layers of neural networks, the history of Deep Learning, and its real-world applications from voice assistants to medical diagnostics.

Read More
Illlustration of a bird on a desk
Harnessing AI for Efficient Inspiration Curation

We streamlined our inspiration curation by using GPT-4.0 to transform a disorganized Slack thread into a well-structured, easily navigable database, saving hours and enhancing our creative workflow efficiency.

Read More
Demystifying AI in the Workplace: 6 Key Disciplines and Their Real-World Applications

Uncover the six major disciplines of AI and their practical examples. Learn how understanding these areas can help align employees with AI initiatives, dispel myths, and foster a positive outlook on AI-driven transformation.

Read More
Demystifying Robotic Process Automation: Impact on Workforce and Business Efficiency

Explore the transformative power of Robotic Process Automation (RPA) in businesses. Understand its role in automating mundane tasks, its impact on jobs, and the surprising benefits it brings to employee engagement and operational efficiency

Read More
Open source code
Embracing Open-Source in Digital Transformation: Unlocking Collective Innovation

Discover the power of an open-source approach in digital transformations. Learn from Kaggle's success and historical examples to harness the collective intelligence of your organization and drive innovative AI solutions.

Read More
weighing benefits and ethics of AI
Ethical Considerations in AI: The Singularity and Its Implications

Delving into the concept of the 'Singularity' by Ray Kurzweil, this article highlights the ethical challenges posed by AI technologies. Using Amazon's Echo Dot for kids as a case study, it underscores the importance of evaluating not just the feasibility but also the ethical implications of AI implementations.

Read More
Football and AI: Tackling the Challenges of Bad Algorithms

Using football as an analogy, delve into the pitfalls of bad algorithms in AI. Understand the concepts of overfitting, underfitting, and the importance of regularization in crafting effective machine learning models.

Read More
toilet paper hoarding teaches us about AI
From Toilet Paper to AI: Lessons from the Pandemic on Rational Decision Making

Drawing insights from the Covid-19 toilet paper frenzy, this article delves into human decision-making processes and how AI can be harnessed to elevate our cognitive abilities. By understanding our inherent biases and needs, we can design AI systems that empower humans to make more rational choices and focus on higher-order aspirations.

Read More
Connection between money, data, and AI
Guarding Digital Gold: The Imperative of Data Security in the AI Era

Drawing parallels between money and data, this article emphasizes the urgency for companies to prioritize data security. As data becomes increasingly valuable, businesses must adopt stringent measures akin to banks, ensuring the safety and privacy of their users' information in the rapidly evolving digital landscape.

Read More
Futuristic boardroom showcasing AI-driven strategies with Canadian city skyline in the background.
Harnessing AI for Post-Series B Growth: A Guide for Canadian Enterprises

Unlock exponential growth for post-Series B Canadian enterprises with tailored AI strategies. Dive into a roadmap that bridges vision with implementation, ensuring your company harnesses the transformative power of AI effectively.

Read More
Implementing AI in Business: A 4-Step Guide for Managers

Discover a structured approach for managers to harness the power of AI in business. From challenging assumptions to choosing the right algorithm, ensure your AI projects align with customer needs and business goals.

Read More
Interpreting Machine Learning for Business Strategy: The Role of Managers

Discover the importance of managers in interpreting ML results for actionable business strategies. Dive into a real-world scenario to understand how industry expertise can transform raw data into actionable insights, driving business success with ML.

Read More
Marshmallows, Kindergartners, and Reinforcement Learning: A Tale of Strategy and AI

Discover the link between the Marshmallow Experiment and reinforcement machine learning. Learn how feedback-driven approaches, similar to kindergartners' strategies, can lead to optimal solutions in AI and business scenarios.

Read More
Measuring Success in Machine Learning: Beyond Accuracy

Explore the nuances of measuring success in ML models. Understand why accuracy isn't the only metric and how different scenarios demand varied evaluation criteria. Dive into real-world examples to grasp the importance of tailored metrics in ML projects

Read More
ModelOps: The Key to Accelerating AI Transformation and Maximizing ROI

Explore the challenges companies face in AI adoption and how ModelOps can streamline the process from data collection to deployment. Learn the importance of data structuring, empowering data science teams, and leveraging expert firms to establish a robust ModelOps framework.

Read More
navy seal strategy
Strategic AI Implementation: Navy SEAL Principles in Business

Discover how Navy SEAL strategies can guide businesses in their AI transformation journey. Embrace a measured approach, from planning to scaling, to ensure successful AI integration and stay ahead of competitors.

Read More
Strategic AI Integration: A Step-by-Step Guide to Successful Innovation

Discover a structured approach to AI integration in businesses. From leveraging internal expertise to identifying key opportunities and setting up innovation labs, learn how to strategically wade into the world of AI without diving headfirst.

Read More
Understanding Data Biases in Machine Learning: Lessons from Politics

Explore the pitfalls of 'bad data' in ML strategies, drawing parallels from political missteps. Learn the importance of critical thinking in data collection and the role of managers in identifying and mitigating biases in ML models.

Read More
Black Swan concept in AI
Understanding the Black Swan in AI: Managing Risks in Big Data

Drawing from Nassem Taleb's concept of the Black Swan, this article emphasizes the importance of recognizing the limitations of big data in AI. By distinguishing between different types of datasets, managers can make informed decisions and mitigate unforeseen risks in their AI strategies.

Read More
unmasking AI ethics
Unmasking Bias with AI: The Ethical Dilemma of Algorithmic Transparency

Exploring the ethical challenges of AI, this article delves into how algorithms can inadvertently expose inherent biases in decision-making. By examining Amazon's hiring algorithm case, we learn that confronting these biases head-on, rather than reverting to old practices, can pave the way for a more equitable future.

Read More
Venture Capital and AI: Navigating Investments in a Complex Landscape

Explore the surge in AI-related startup investments and the potential risks of a speculative bubble. Understand the importance of domain expertise in AI for making informed investment decisions and discover resources to gain insights into the technology.

Read More
Why AI and Machine Learning Aren’t the Same Thing

Dive into the differences between AI and machine learning. Discover how machine learning, a subset of AI, uses data patterns for predictions and the significance of quality data in driving real-time business insights.

Read More
Image of hands on a keyboard in oil painting style
Ecommerce and how it has changed the retail market

The retail industry has changed dramatically over the past decade. From the rise of online shopping and increased competition, to evolving consumer priorities and automation in retail – ecommerce is reshaping how we shop. In this post we'll explore some of these changes, along with their impact on consumers and retailers alike.

Read More
Oil painting of cows in a sunset
Project Launch: Ventec Website

Leading the charge in agricultural tech, Ventec needed a new site to better represent their industry. Today, we're proud to announce the launch of Ventec's new online platform!

Read More
Chess pieces painted in oil
Start With Strategy: The Key To A Successful Project

Without a defining strategy, projects can fall apart at any point in the process. It's important to start on strong footing to ensure success as a result.

Read More
Image of results of SEO, stylized into an oil painting
How long does it take for SEO to start working?

The time factor of SEO is often longer than many companies expect. Here's what to expect when it comes to launching an SEO strategy.

Read More
Painting of an automated arm moving
How to use automation to save you time and money

The key to success for many businesses today is automating tasks to ensure that costs are low, consistency is high, and less time is wasted overall.

Read More
Painting of a man climbing
Top Growth Tools To Expand Your Business

With the world of software evolving at a breakneck pace, here are a few tools that we use to help our clients' businesses grow.

Read More
oil painting of brain above a table representing AI
How Is AI Going To Change Graphic Design?

Artificial intelligence is changing the way every business operates, even in the creative fields that may once have been deemed safe from machine intelligence.

Read More
Sketch of a crane flying
Cranes, Trains, & Automobiles: Evolving Work Culture In The Digital World

With the COVID-forced digital shift, we have created a new benefit initiative to help improve quality of life for team members .

Read More
Watercolor of Paris
Company Trip Report: France 2022

A summary of our first official Cranes, Trains & Automobiles work away, and what's coming down the pipeline!

Read More
Watercolor background with the word "Branding" in the bottom left corner
A guide to creating a brand that works for your audience

It's time to step back and think about your brand in terms of what makes sense for your audience.

Read More
Orange painting with the word "Hybrid" in it
Hybrid Work: The Future Of The Office

The days of the office-bound worker are numbered. Organizations that have been slow to adapt will struggle to compete with those that embrace hybrid work, as employees seek more flexibility in their careers.

Read More
Logo of TIlt Five in a wheat field painting
Tilt Five Announcement

Congratulations to Tilt Five on partnering up with Asmodee to launch Catan in AR!

Read More
Watercolor image with a link icon in the middle of it.
Backlinks and Search Engine Optimization

When it comes to SEO, backlinks hold a lot of power. In fact, they’ve been shown to have a huge effect on how well your site performs in the search engines.

Read More
painting with the word, "Story" in the background
How do you build a brand story?

If you're looking to build your company into something more than just another commodity offering among many others on the market, here are some steps to get started:

Read More
Computer icon on a watercolor backsplash
How Owning A Great Website Impacts Your Business

Metrics on good websites vs. poor ones can be difficult to assess. With that being said, there are some important reasons to ensure your website is helping your company grow.

Read More
Plane moving around world icon, on a purple and green water color background
How Travel Breeds Creativity And Happiness For Our Team

We've found that traveling with our team has made them happier and more creative in the process.

Read More
Watercolor image background with "On-Page SEO" as wording in the middle
On-Page SEO: Questions To Ask Your 'Expert'

SEO is a convoluted field that can be difficult to understand as a non-expert. We have some tips on things to ask your developer or SEO expert as they change your site.

Read More
Our Paper Crane logo against a black and white watercolor splash
Legitimizing Your Brand

Brand legitimacy is a powerful tool for businesses, but many small businesses don't think that way. In this article, we'll discuss the concept of brand legitimacy and how it can help your business grow.

Read More
WordPress logo on a pink watercolor splashed background
How to speed up your Wordpress site

With recent search engine algorithm updates, page speed is more important than ever. Learn about how you can speed up a WordPress website!

Read More
Water color blue background with a Webflow logo
Webflow: When To Use It

Webflow can be a powerful tool in the right hands and perfect situations. In others, it is used to lesser effect when better tools may fit the bill better.

Read More
Oil painting of mountains
Vault 44.01 Lands $150M in Capital Commitment

With a significant fiscal investment, Grey Rock Investments showed their trust in Vault 44.01

Read More
Virtual Gurus logo over a colorful painted backdrop
Virtual Gurus Closes 8.4 Million

The Virtual Gurus were successful in closing 8.4 million dollars in funding after showing incredible year-over-year revenue growth on a consistent basis.

Read More
Board room painting
Kudos Lands $10M In Funding

With employee engagement trending internationally, Kudos leads the way with their unique software.

Read More
Next.js and Headless CMS: Revolutionizing Enterprise Web Development

Read More