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Digital twins and generative AI (GenAI) are two transformative technologies reshaping industries by enhancing decision-making and operational efficiency. When combined, they offer synergistic benefits that surpass their individual capabilities.

Understanding Digital Twins and Generative AI

• Digital Twins: These are virtual replicas of physical assets, processes, or systems.
• Generative AI: This branch of artificial intelligence creates new content—such as text, images, or simulations—by learning patterns from existing data.

Digital Twins and Generative AI: Unlocking the Future of Geospatial 2.0

Digital twins and generative AI are fundamental components of the Geospatial 2.0 era. A digital twin in isolation lacks context, but when placed in a 3D digital world like that generated by Cesium or similar platforms, it gains the spatial and temporal context needed to model real-world interactions effectively. Generative AI enhances this by synthesizing complex data inputs and outputs, enabling the digital twin to simulate dynamic scenarios, predict outcomes, and provide actionable insights in ways that were previously unattainable. Together, they create a powerful framework for innovation, bridging the gap between physical and digital geospatial environments.

Here are 9 examples of how combining digital twins with GenAI is redefining geospatial applications:

1. Enhanced Scenario Planning and Simulation

GenAI empowers digital twins to simulate diverse geospatial scenarios with precision. By incorporating vast datasets and variables, organizations can explore “what-if” situations at unprecedented scales.

Example: In urban planning, GenAI-enabled digital twins simulate how new infrastructure affects traffic, air quality, and resource allocation. This allows cities to predict the impact of changes and optimize designs for sustainable growth.

2. Accelerated Development and Deployment

Building a digital twin traditionally requires significant time and resources. GenAI accelerates this process by generating foundational models, reducing development time from months to weeks.

Example: In disaster management, GenAI can quickly generate digital twins for flood-prone areas, enabling emergency response teams to test mitigation strategies before a crisis occurs.

3. Improved Data Management and Analysis

Digital twins thrive on large, real-time geospatial data streams from sensors, satellites, and IoT devices. GenAI compresses and organizes this data, ensuring that digital twins operate efficiently while retaining critical insights.

Example: In forestry management, GenAI organizes satellite imagery and sensor data to help digital twins detect illegal logging or disease outbreaks in real time.

4. Data Augmentation and Synthesis

GenAI generates synthetic datasets to augment real-world data, filling gaps where data collection is expensive or impossible. This strengthens the accuracy of geospatial digital twins.

Example: In environmental monitoring, synthetic data on rare weather patterns can help digital twins predict the effects of extreme climate events on vulnerable regions.

5. Enhanced Personalization and User Interaction

With natural language processing, GenAI makes interacting with digital twins more intuitive. Stakeholders can ask questions or provide commands in plain language, enabling greater accessibility.

Example: A city planner can ask a digital twin, “How will adding a new subway station affect foot traffic in nearby neighborhoods?” The system responds with clear, actionable insights.

6. Predictive Capabilities and Anomaly Detection

By integrating GenAI, digital twins become better at identifying and predicting potential issues. From urban infrastructure to natural resource management, this combination enhances proactive decision-making.

Example: In utilities, GenAI-powered digital twins predict equipment failures, allowing operators to perform preventative maintenance and avoid costly outages.

7. Resource Optimization

Geospatial applications often involve managing finite resources. GenAI helps digital twins analyze and predict resource usage more effectively.

Example: In agriculture, GenAI-driven digital twins simulate water usage across fields, optimizing irrigation schedules to conserve resources while maximizing crop yields.

8. Continuous Improvement and Innovation

The feedback loop between GenAI and digital twins drives ongoing enhancements. Digital twins test new GenAI-generated models in a virtual environment, ensuring real-world implementations are robust.

Example: In coastal management, this synergy can refine flood defense strategies by analyzing historical storm patterns and suggesting improved designs for levees and barriers.

9. Supply Chain Optimization

Supply chains are inherently geospatial; their operations and efficiency are heavily dependent on location-based data and spatial relationships. For supply chains, GenAI enhances digital twins by identifying disruptions, optimizing inventory placement, and suggesting alternative routes.

Example: During a natural disaster, GenAI-enabled digital twins reroute supply chains to avoid affected areas, minimizing delays and ensuring timely deliveries.

Closing Thoughts

The integration of digital twins and generative AI is not just a technological evolution but a revolution driving the Geospatial 2.0 era. By combining the spatial intelligence of digital twins with the creative and analytical capabilities of GenAI, organizations can unlock unprecedented levels of innovation, efficiency, and sustainability. From urban planning to disaster management and supply chain optimization, these technologies are enabling smarter, more resilient systems that redefine how we interact with the world.

As these tools continue to evolve, their synergy will empower industries to tackle complex challenges and seize opportunities that were once out of reach. Now is the time for forward-thinking organizations to embrace this transformative potential and lead the charge into a geospatially intelligent future.

Matt Sheehan helps companies discover and win opportunities in the evolving world of Geospatial 2.0

In this article I wanted to provide a brief discussion on some important research work being done by Zhenlong Li, Professor of Geography at Penn State. Dr Li and his team wrote a research paper titled: Autonomous GIS: the next-generation AI-powered GIS. The paper explores Autonomous GIS, a next-generation geographic information system (GIS) that leverages artificial intelligence (AI) to operate independently with minimal human involvement.

Below is a short summary of the paper:

What is Autonomous GIS? – It’s a type of GIS that can perform tasks on its own, using AI models like ChatGPT to gather, analyze, and visualize spatial data, making advanced geographic tasks accessible to a broader audience.

How It Works – The core of this system, named LLM-Geo, relies on AI to understand user questions and create a sequence of steps (like a flowchart) to solve them. It can automatically handle data tasks, such as combining datasets, running calculations, and creating maps or charts as needed.

Five Key Abilities – To be truly “autonomous,” this GIS aims to:

• Self-generate: Start new tasks independently.
• Self-organize: Arrange data and steps in a logical sequence.
• Self-verify: Check for errors in its own work.
• Self-execute: Complete tasks on its own.
• Self-grow: Improve over time by reusing solutions.

Real-World Testing – The AI-powered LLM-Geo was tested on various cases, such as calculating populations near hazardous sites, analyzing mobility during COVID-19, and examining COVID-19 death rates in the U.S. The system successfully interpreted each task, created steps, and generated correct answers without human intervention.

How is Autonomous GIS Relevant to Geospatial 2.0?

Autonomous GIS embodies key aspects of Geospatial 2.0, which focuses on making spatial data and insights more accessible, efficient, and relevant across industries. Traditional GIS requires specialized skills and manual processes, but Geospatial 2.0 is about breaking down these barriers through intelligent, adaptive systems. By enabling self-directed, automated analysis, Autonomous GIS aligns with this new era, supporting faster, easier access to spatial insights for decision-making in fields like urban planning, environmental management, and public health.

Future Directions

While promising, LLM-Geo still has limitations and needs further development, such as adding memory capabilities to recall past tasks and improving its ability to handle complex spatial data. Ultimately, Autonomous GIS represents a step toward AI-driven GIS that can simplify spatial analysis for users at all skill levels.

In essence, Autonomous GIS is a significant advancement for Geospatial 2.0, paving the way for a more user-friendly, intelligent approach to spatial analysis that empowers a wider audience to engage with geospatial data and uncover actionable insights.

What is Geospatial 2.0?

Geospatial 2.0 is the convergence of geospatial data and tech. It is the hub of geospatial 3D, digital twin, ML/GenAI and augmented reality/immersive.
𝐉𝐨𝐢𝐧 𝐭𝐡𝐞 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 here
𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥 subscribe

References

Dr Li’s Autonomous GIS discussion on LinkedIn: https://shorturl.at/3cybX

*Autonomous GIS: the next-generation AI-powered GIS: https://shorturl.at/DAkV4

* Autonomous GIS Use Cases: https://shorturl.at/S5uTr

*The papers were difficult to find and download so I have linked to local copies

Introduction

In this article, we are going to explore briefly how two powerful technologies – traditional Machine Learning and Generative AI – are revolutionizing the geospatial industry.

Traditional Machine Learning in Geospatial

1. What it does: Finds patterns and makes predictions based on existing data.
2. Geospatial applications:
   • Land use classification from satellite imagery
   • Predicting traffic patterns in cities
   • Identifying optimal routes for delivery services
3. Example: Imagine teaching a computer to recognize different types of buildings in aerial photos. That’s ML in action!

Generative AI in Geospatial

1. What it does: Creates new, original content based on learned patterns.
2. Geospatial applications:
   • Generating realistic 3D city models
   • Creating synthetic map styles
   • Producing text descriptions of geographic features
3. Example: Think of GenAI as a creative artist. It can design a new park layout that fits perfectly within a city’s existing structure.

Key Differences

1. Output:
   • ML: Analyzes and categorizes
   • GenAI: Creates and innovates
2. Data use:
   • ML: Requires large, labeled datasets
   • GenAI: Can work with less structured data
3. Flexibility:
   • ML: Specialized for specific tasks
   • GenAI: More adaptable to various creative tasks

Conclusion

Both ML and GenAI are transforming how we understand and interact with geographic data, making our maps smarter, our cities more efficient, and our understanding of the world around us more profound.

🔗 𝐒𝐭𝐚𝐲 𝐚𝐡𝐞𝐚𝐝 𝐨𝐟 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝟐.𝟎
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Two terms dominate my working life: Geospatial 2.0 and Dynamic Geospatial Digital Twins (DGDT). The Geospatial 2.0 conversation, started back in 2019, was a future vision of the path of geospatial. It was centred on how digital technology was about to move geospatial from the periphery to the core of business decision making.

Over the last 4 years that technical crystal ball gaze has become reality. DGDT is a core expression of that original vision.

In this article I am going to focus on dynamic geospatial digital twins. I believe these have come into sharp focus in the last few weeks thanks to recent announcements from Bentley Systems.

Before we get ahead of ourselves, let’s first define dynamic geospatial digital twins.

Dynamic Geospatial Digital Twins

There are 3 key parts to the phrase dynamic geospatial digital twin.

First, we have digital twin. This is a “virtual model that replicates a physical object, system, or environment, using real-time data to simulate, monitor, and analyze its performance and behavior.” Digital twins of the past have mostly lived in isolation. That is they had no real world context. An architects building design for example.

Secondly that term dynamic. This refers to real-time or reflecting changes in the real world.

Finally there is that poorly understood term geospatial. Geo means earth. Spatial means place. So we have place on earth, in other words any person or thing which has an earth coordinate. Commonly that is a GPS coordinate: A house can have a static x, y coordinate. A river has a set of connected static coordinates. A person; dynamic coordinates (for those techies reading .. welcome to your geodatabase!)

We will dive a little deeper into geospatial in a moment, but pulling this all together:

A Dynamic Geospatial Digital Twin is a real-time, virtual replica of physical assets or environments that integrates geospatial data, IoT, AI, and advanced analytics. It continuously updates to reflect changes in the real world, enabling better decision-making, monitoring, and management of complex systems such as cities, infrastructure, and natural environments.

The Critical Importance of Geospatial

The importance of geospatial to this evolving world of digital twins cannot be overstated. geospatial provides two critical elements: context and information layers.

a) Context

Geospatial context is what sets digital twins apart from traditional static models. By integrating geospatial data, we give digital twins real-world relevance. Whether it’s the exact location of a utility line, a flood-prone area in a city, or the path of a delivery truck, the addition of geographic information allows digital twins to offer real-time situational awareness. This context makes it easier to understand how a physical asset interacts with its surroundings and supports better decision-making, monitoring, and management of both urban and rural environments.

b) Information Layer Hub

In a dynamic geospatial digital twin, information layers represent the integration of multiple layers of data that are aligned with a specific geographic location. At the center of these layers is the physical 3D structure, geospatially referenced. For example, imagine a building: its geographic coordinates anchor a wealth of related data—architectural plans, its BIM (Building Information Modeling) model, historical maintenance records, energy usage, and IoT sensor data that continuously monitor the building’s structural health.

The digital twin has all those layers of engineering data, reality survey data, IoT data, subsurface data, and all the applications that you use every day contribute and enrich that data structure progressively. And this is your data foundation that you then use to do more design, but also leverage AI. — Julien Moutte, CTO, Bentley Systems

Is this Now the Game-Changing Moment?

Dynamic geospatial digital twins have been much discussed over the years. But we have lacked the data, digital twin platforms and AI algorithms to make them a reality at scale. Today that is no longer the case. “Everything has come together” to quote Arkadiusz Szadkowski at Esri. We are now at one of those magical moments in time; we can now start modelling the real world using dynamic geospatial digital twins.

That has huge potential benefits!

Underground Infrastructure Monitoring and Mapping – Dynamic Geospatial Digital Twin Use Case

I wanted to better paint the picture of the incredible value of dynamic geospatial digital twins with a use case. I’ve spent time focused on underground mapping with various clients, so I thought a sub-surface example might be an interesting use case. I will use my understanding of Bentley’s new technology stack to construct this, as of now, hypothetical scenario:  

• Opportunity: Digital twins for underground infrastructure can dynamically monitor and map utilities, subway systems, and mining operations. Using sensor data and AI analytics, these twins can predict maintenance needs and optimize underground construction activities.
• Data: Google Maps and Google Earth Engine’s surface and subsurface data provide crucial context for mapping underground utilities and ensuring precise geospatial alignment.
• Cesium: Cesium’s 3D tiling can visualize subsurface infrastructure, such as tunnels and utility lines, providing operators with clear, interactive models of underground environments.
• Platform: Bentley’s iTwin platform provides a comprehensive digital twin environment for underground infrastructure. It can federate diverse data sets from different systems, including CAD files, sensor data, and geospatial data, into a single digital twin. This enables real-time collaboration, detailed monitoring, and data integration, making it easier to track underground infrastructure health and operations. The platform supports open formats and integrates real-time IoT data, allowing utilities and engineers to predict failures and optimize asset management
• Immersive Visualization: By using Unreal Engine, engineers can conduct virtual walkthroughs of underground infrastructure, simulating construction scenarios or visualizing sensor data in a real-time 3D environment.
• AI’s Role: AI can predict underground asset failures, such as leaks in water mains or cable damage, by analyzing historical performance data and sensor inputs. AI can also recommend optimized routes for new underground utilities, reducing costs and environmental impact.

The Challenge Ahead

Having all the enabling technology in place to make dynamic geospatial digital twins a reality is our first step. Our next challenge is execution. That is discovering opportunities and applying this technology and data to solve problems. As a commercially focused technologist this is my area of focus. Last week I wrote a mini series of articles entitled: Where are those $100m Geospatial Blue Ocean Opportunities? These laid out an execution framework. These short articles might be worth you time reading if you are exploring dynamic geospatial digital twins.

Closing Thoughts

The emergence of Dynamic Geospatial Digital Twins marks a critical milestone in the evolution of geospatial technology. What began as a vision—Geospatial 2.0—has now become a transformative reality. With the convergence of advanced platforms like Bentley’s iTwin, real-time geospatial data, immersive visualization tools, and AI-driven analytics, we are witnessing the beginning of a new era in how we design, manage, and interact with the world around us.

The potential of DGDTs extends far beyond individual use cases like underground infrastructure monitoring. They have the power to revolutionize industries from urban planning to climate resilience, unlocking value by offering deeper insights and smarter decision-making. However, the true challenge lies ahead: in scaling this technology, identifying new opportunities, and delivering real-world impact.

We are on the cusp of a major breakthrough, and it is clear that those who harness the full capabilities of dynamic geospatial digital twins will lead the way in shaping the future.

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.

I wanted to continue a thread from the popular mini-series of articles I wrote last week entitled: 𝐘𝐨𝐮𝐫 𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 $𝟏𝟎𝟎𝐦 𝐁𝐥𝐮𝐞 𝐎𝐜𝐞𝐚𝐧 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬.

At the heart of the new Blue Ocean opportunities I discussed is Geospatial 2.0. Credit goes to Josh Gilbert for originally coming up with this term back in 2019 in his article: Approaching Geospatial 2.0: Unlocking billions, across verticals, at scale. Since then I have been evolving the concept.

In this article I want to discuss how Geospatial 2.0 has moved from a technology/data conversation to a commercial discussion, and how it represents a huge geospatial shift.

But let’s begin with a definition.

Geospatial 2.0 in 2024

Four years ago I realised we were in the midst of a technology/data revolution. Thanks to new sensors we had begun to collect a tsunami of new geospatial data: imagery, lidar, SAR, hyperspectral, real-time data; below ground, terrestrial and above ground. Terms like reality capture and digital reality were becoming popular. This was also the beginning of the popularization of artificial intelligence (AI). Ground breaking books like AI Superpowers were published shining a bright light on our AI future.

The combination of new multi-dimensional geospatial data (2D, 3D and 4D) and AI to process and analyse that data made me realise that geospatial was moving beyond an abstract representation of the real world (2D maps) to actually modelling the real world (geospatial digital twins).

But in 2020, we were still building out the jigsaw pieces, not all were in place. We are now in 2024, and much has changed. We now have all the Geospatial 2.0 puzzle pieces.

So what are the core elements of Geospatial 2.0 in 2024?

1) Data – There are still a multitude of geospatial data providers, but today we have Google and others who are providing a single access point to a plethora of geospatial application ready data.

2) Platforms – iTwin from Bentley and ArcGIS from Esri have seen incredible advances over the last 4 years. Bentley in particular have embraced Geospatial 2.0 with their acquisition of Cesium, and partnership with Google. But overall, we now have platforms which are making the dream of geospatial digital twins a reality.

3) Immersive Visualization – Immersion was not part of the original Geospatial 2.0 conversation. But the integration of Unreal Engine into geospatial platforms and the release of the Apple Vision Pro and smart glasses respectively have provided a new interface. That is potentially a game changer, as we immerse ourselves in a digital replica of the real world. Both augmented reality and virtual reality will soon be a key part of Geospatial 2.0.

4) Artificial Intelligence & Generative AI – In 2020 AI was the focal point of the Geospatial 2.0 conversation. Two years ago OpenAI announced ChatGPT, and almost overnight the world changed. Generative AI is now with us. The possibilities are endless, and we are only at the very beginning.

As I mentioned earlier, over the last 4 years Geospatial 2.0 has moved from a technology/data conversation to a commercial discussion. That means applying Geospatial 2.0 to solve real world problems.

To help illustrate the incredible possibilities presented by Geospatial 2.0 let’s consider a potential use case.

Geospatial 2.0 Transportation Safety Use Case – Diagnosing Causes of Serious Injuries and Deaths at Intersections

1) The Opportunity:

• By utilizing real-time data, 2D maps, and 3D digital twins, authorities can better understand the underlying causes of severe accidents at intersections. This allows for data-driven redesigns and preventive interventions, reducing fatalities and serious injuries.

2) Google Data:

• Google Maps and Street View can provide detailed geospatial data, including traffic flow, pedestrian density, and driver behavior at intersections. By overlaying crash data on maps, transportation analysts can identify patterns, such as which directions or lanes tend to have more accidents.
• Google Earth Engine can also contribute environmental context, such as analyzing the impact of seasonal changes (e.g., snow, rain) on accident rates at specific intersections.

3) 3D Visualization:

• 3D visualization capabilities allow safety teams to build realistic 3D models of intersections and accident sites, including roads, traffic signals, and nearby infrastructure. By visualizing accidents in 3D, teams can analyze factors like visibility, road gradients, and infrastructure elements that may contribute to accidents.
• Integrate sensor data from vehicles and traffic lights, enabling real-time visualization of vehicle movements, signal timings, and pedestrian crossings at high-risk intersections.

4) Immersive Visualization:

• Immersive technologies using Unreal Engine or Unity can recreate accident scenarios in a photorealistic 3D environment, allowing investigators to simulate and explore potential causes of collisions. Traffic planners can visualize how changes—such as adjusting signal timings, adding crosswalks, or altering lane markings—impact safety in real time.
• Immersive simulations can model accidents under different conditions, such as during rush hour or at night, helping authorities design safer intersections.

5) AI & GenAI’s Role:

• AI-driven analysis of intersection data can detect hidden patterns in accidents, such as how driver behavior changes with time of day or weather conditions. AI can also automate the processing of camera footage and sensor data to identify contributing factors like speeding, red-light violations, or improper pedestrian crossings.
• AI-powered systems can predict future accidents by analyzing historical accident data and vehicle movement patterns, then proposing preventive solutions such as intersection redesign or optimized traffic signal algorithms. AI can also assist in creating automated risk assessments for each intersection.
• Generative AI could generate design alternatives based on crash data to propose safer intersection designs automatically, while real-time AI analysis could alert authorities to potential risks before accidents happen.

Closing Thoughts

As we stand on the brink of fully realizing Geospatial 2.0, it’s clear that this isn’t just a technological evolution—it’s a commercial revolution. From smarter, safer cities to predictive risk management, the ability to harness multi-dimensional data and AI-driven insights is transforming industries. The shift from abstract mapping to dynamic digital twins offers businesses unprecedented opportunities to enter new markets and solve real-world problems in ways never before possible. Whether it’s optimizing transportation safety or developing the next generation of immersive geospatial experiences, the future belongs to those who can capitalize on Blue Ocean opportunities.

Now is the time for businesses to embrace this transformation and position themselves at the forefront of a rapidly evolving geospatial landscape.

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win multimillion-dollar Geospatial 2.0 Blue Ocean opportunities in this new age of dynamic geospatial Digital Twins and AI.

This article series has become far longer than I had originally planned. I realized that I had more material than I had anticipated, and I wanted to present that content in bite-sized chunks.

Before we jump into this; our final article, let’s briefly summarize where we have been in our journey to building a $100m Geospatial Blue Ocean business.

We began by defining Blue Oceans (uncontested market spaces) and comparing them with Red Oceans (competitive blood in the water). With a lofty goal of $100m, Blue Oceans will need to be our target. Luckily our timing is good, thanks to Geospatial 2.0, Blue Ocean opportunities abound.

In our second article, we set the stage. Providing key definitions, foundational elements needed before you get started, and discussion of the Geospatial 2.0 Blue Ocean Triad; our overlapping/interlinking set of guiding frameworks.

In articles three and four we walked through 2 of the 3 core elements of the Geospatial 2.0 Blue Ocean Triad. Rather than making this a theoretical exercise we made this real by discovering and building out a strategy for an actual Geospatial 2.0 Blue Ocean opportunity.

That brings us nicely to this article; part 3 of the Geospatial 2.0 Blue Ocean Triad – Go-to-Market.

Introducing Go-to-Market

In our last article we stepped through our overarching business strategy. Go-to-market (GTM) is a second level of this overall strategy. It outlines how a company will sell its product or service to customers. Sales, marketing and product teams are key to both constructing and executing the GTM strategy.

Our Go-to-Market Strategy

Below are the key elements which make up our go-to-market strategy:

a) Business Overview and Value Proposition

Our intelligent dynamic digital twin solution provides utilities with real-time, AI-driven insights for disaster risk management and resilience planning, significantly improving their ability to predict, prepare for, and respond to severe weather events like hurricanes.

b) Target Audience and Personas

Primary: Chief Technology Officers and Emergency Response Directors at medium to large utility companies in hurricane-prone regions.

Secondary: State and local government emergency management officials.

c) Market Entry and Expansion Plan

• Phase 1: Partner with 2-3 innovative utility companies for pilot projects
• Phase 2: Expand to other utilities in high-risk hurricane zones
• Phase 3: Broaden to utilities in other severe weather-prone areas

d) Competitive Analysis

Primary competitors: Traditional GIS providers, general-purpose geospatial platforms, with a heavy focus on 2D maps.

Our advantage: Specialized focus on utilities and severe weather events, digital twin expertise combined with advanced AI capabilities

e) SWOT Analysis

Strengths: Unique intelligent dynamic digital twin technology, deep geospatial expertise

Weaknesses: New entrant in the utility sector

Opportunities: Growing need for disaster resilience solutions

Threats: Potential entry of large tech companies into the space

f) Brand Messaging and Positioning

Positioning: The most advanced, utility-specific intelligent dynamic digital twin solution for disaster resilience

Key message: “Empowering utilities to predict, prepare, and prevail in the face of severe weather events”

g) Organic Digital Marketing Channels

• LinkedIn for top of funnel content
• SEO-optimized blogs. In-depth articles. Opt-in eBooks
• YouTube channel for in-depth content – Education, product demos, expert interviews etc.

h) Content Strategy & Organic Sales Funnel Strategy Awareness:

Focus on educational content about intelligent dynamic digital twin technology, best practices in disaster resilience, and success stories from pilot projects.

Mixed content will be the focus. This will include, written, video, podcast, newsletters etc. Content length will vary depending on type and channel (eg. LinkedIn content will be short attention grabbing. YouTube content longer and more in depth).

Content will be created targeting each part of the sales funnel, with embedded calls to action (CTA’s) guiding potential customers through the funnel.

Sales Funnel Organic content:

• Attention: Thought leadership content, industry conference presentations, social media shorts
• Consideration: Case studies, webinars, personalized demos
• Decision: Pilot programs, ROI calculators

i) Sales Enablement

Develop comprehensive training programs for sales team on intelligent dynamic digital twin technology, utility operations, and disaster response best practices

j) Partnership Strategy

• Technology Alliances: Form partnerships with complementary technology providers (e.g., IoT sensor manufacturers, satellite imaging companies) to enhance data inputs for the IDDT solution.
• Industry Associations: Collaborate with utility industry associations to gain credibility, access to potential customers, and insights into industry trends and needs.
• Academic Partnerships: Establish relationships with universities conducting research in geospatial technology, AI, and climate science to stay at the forefront of innovation.
• System Integrators: Partner with established system integrators in the utility sector to facilitate smoother implementation and integration of the IDDT solution into existing utility systems.

k) Pricing Strategy

• Tiered subscription (Basic, Professional, Enterprise)
• Value-based pricing tied to utility cost savings
• Module-based options for customization
• Discounted pilot program pricing
• Performance-based component linked to KPI achievements

l) Performance Metrics and KPIs

• Number of pilot projects initiated
• Customer satisfaction scores
• Reduction in utility downtime during severe weather events
• Increase in speed of power restoration post-event
• Market share in target geographic regions

m) Implementation Plan

• Month 1-3: Finalize MVP and secure pilot partners
• Month 4-6: Launch and iterate on pilot projects
• Month 7-12: Expand sales efforts based on pilot results
• Year 2: Scale operations and expand to new geographic regions

n) Budget Allocation

• 40% Product development and innovation
• 30% Sales and marketing
• 20% Customer success and support
• 10% Operations and administration

As discussed in the last article, we looked to use best practices in strategy construction. I have leaned heavily on the work of Roger Martin; being guided by both his thinking and the Strategy Catalyst framework he has constructed.

The GTM strategy we have outlined above aligns with Roger Martin’s approach by making clear, integrated choices that support our overall winning aspiration. This go-to-market strategy is tightly linked to and supports the broader business strategy, ensuring coherence across all aspects of the business.

Closing Thoughts

If you made it here; congratulations. My hope is this series provided you all the building blocks to make your $100m Geospatial business dream a reality. It is based on my geospatial experiences over 25 years: What I have seen done well, and very poorly. As I have mentioned geospatial has entered a new phase; one of growth enabled by Geospatial 2.0.

Blue Oceans abound, my goal here has been to help you find your Blue Ocean.

Good luck in your journey. If you need help, advice, guidance feel free to reach out to me on LinkedIn Matt Sheehan or fire me an email at mattsheehan7365@gmail.com

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.

This is the fourth article in this mini-series. If you have not read the previous article, I would encourage you to do that before you continue here.

My goal in writing this series of articles is to share with you my experience over 25 years in geospatial. To provide you with the building blocks to take advantage of a unique time in our geospatial history – what I have been calling Geospatial 2.0 – so you can construct an incredible Blue Ocean geospatial business.

In our previous article we tackled the Blue Ocean ideation phase. By researching Geospatial 2.0 market trends we narrowed our focus to:

Disaster risk management and resilience planning – An intelligent, dynamic digital twin (IDDT) targeted at utilities with a focus on severe weather events such as hurricanes.

We validated the idea through research. That research included leveraging Generative AI. Here was ChatGPT’s response:

“The use of Intelligent Dynamic Digital Twins (IDDT) for hurricane response in utilities appears to represent a strong Blue Ocean opportunity.”

The design thinking framework gave us a structure to not only validate our idea with key utility staff but to understand better the problem, construct potential solutions and get iterative feedback on a prototype.

From Prototype to MVP to Product/Solution

The goal of prototyping is to quickly and cheaply test concepts and validate them with stakeholders or potential users. Once you’ve validated the core assumptions of your idea through the prototype (and you have a clear understanding of the user needs, problems, and potential solutions), it’s time to move to Minimum Viable Product (MVP). The prototype has served its purpose if stakeholders or user feedback gives confidence that your solution addresses a significant pain point.

The MVP is the first version of your product that delivers enough value for early adopters but is still minimal in scope. This phase helps you learn about the market, test assumptions about product-market fit, and make iterations based on real user feedback.

You move from MVP to full product/solution building when you’ve validated critical aspects of your product/solution with real users. If the MVP is gaining traction, solving user problems, and you’ve iterated enough to achieve a strong product-market fit, you can confidently expand functionality, improve UX/UI, and scale the product.

Constructing the Strategy

As you move into the MVP stage, it is time to start constructing your strategy. Immediately that is centred on the MVP. This might include defining what success looks like for the MVP (e.g., a certain number of signups, retention rates, or validation of key features). You may also look to set up continuous feedback mechanisms to gather data from early adopters and make informed iterations. This is also the beginning of your broader strategy construction.

Let’s pause here for a moment and consider that word strategy. Roger Martin, a world renown expert on strategy, emphasizes that strategy is fundamentally about making a series of integrated choices. He argues that strategy is not a plan, but a set of decisions that guide an organization toward achieving a desired goal. In Martin’s book called “Playing to Win” he discusses five key questions that help in making strategic choices.

Let’s presume we are moving through our MVP phase for our IDDT solution, and are now looking to build out our full strategy based on Roger Martins work. We will break this down into two parts. First the Overarching Business Strategy, or our top-level strategy that addresses Martin’s five key questions (see the diagram above). This strategy encompasses the entire business vision, including long-term goals, market positioning, and core capabilities. In our case that strategy might look like this:

Overarching Business Strategy

a) What is our winning aspiration?

To become the leading provider of intelligent, dynamic digital twin (IDDT) solutions for disaster risk management and resilience planning in the utility sector, revolutionizing how utilities prepare for and respond to severe weather events.

b) Where will we play?

• Geographic focus: Hurricane-prone regions in the United States (e.g., Gulf Coast, Eastern Seaboard)
• Customer segment: Medium to large utility companies
• Value chain focus: Software and data analytics for infrastructure management and disaster response

c) How will we win?

• By providing real-time, AI-driven insights that significantly improve utilities’ ability to predict, prepare for, and respond to severe weather events
• By offering a unique combination of geospatial expertise, cutting-edge technology (IDDT), and deep understanding of utility operations
• By continuously innovating and expanding our solution to address evolving customer needs and staying ahead of potential competitors

d) What capabilities must we have?

• Advanced geospatial data processing and analysis
• AI and machine learning expertise for predictive modeling
• Real-time data integration and management
• Deep domain knowledge in utility operations and disaster response
• Strong partnerships with data providers (e.g., weather data, satellite imagery)
• Agile product development and rapid iteration capabilities

e) What management systems do we need?

• Continuous customer feedback and co-creation processes
• Agile project management for product development
• Robust data security and privacy management systems
• Performance tracking systems aligned with customer success metrics
• Talent acquisition and development programs focused on key capabilities
• Strategic partnership management system

There is a second level to what we have provided above and that is our Go-to-Market Strategy. This focuses specifically on how we will bring our IDDT solution to the utility market, including aspects like target customer segments, value proposition, pricing, and distribution channels. We will cover this in our next and final article in the series.

Geospatial 2.0 Technology Choices

I wanted to cover one more topic before finishing this article. As I mentioned in the first article, the original Geospatial 2.0 conversation was centred around data/technology and its potential to transform the world of geospatial. This article series is focused on the application of that data/technology to transform how we solve real world problems. We have entered a world of Blue Ocean opportunities.

Digital twins have been much discussed over the years. Definitions vary. With IDDT, and what I have described here, this has been my long term Geospatial 2.0 vision. Only relatively recently have platforms been launched which pull all the pieces together, making this vision a reality. I wanted to briefly address those platforms. The IDDT solution we have been evolving here, guided by framework and best practices, would be built on one of these new platforms. In my view there are two to choose from:

• Esri-Autodesk integration
• Bentley’s iTwin.

Which path you might take will depend on your existing business model. If you already have a strong relationship with Esri, you might look to choose the first option. Anybody following recent announcements will know that much is happening at Bentley. The acquisition of Cesium and partnership with Google were two very significant announcements. I am watching the evolution of iTwin closely, its future is potentially fascinating.

Closing Thoughts

Okay that’s it. I hope this article gave you plenty to think about as you map out your Blue Ocean journey. In the next and final article in the series we will discuss go-to-market. Until then ..

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.

In this, the third article in this mini-series, I am going to give you a window into my mind. How I think and how I approach problems. What I have learned over 25 years from both stunning business successes and dramatic failures.

My end goal is to provide you with the building blocks to take advantage of a unique time in our geospatial history – what I have been calling Geospatial 2.0 – so you can construct an incredible Blue Ocean geospatial business.

Let’s get started.

Setting the Stage

In case you did not read the previous two articles in the series, here is a brief summary.

• Most geospatial companies have services based business models, they are sales-led, with existing relationships being critical to generating revenue.
• Most companies operate in so called Red Oceans, or highly competitive geospatial marketplaces. These include sectors within Federal, State, and local government, utilities, transportation, oil and gas.
• Thanks to limited investment in marketing, few companies clearly differentiate themselves in these Red Oceans.
• The Geospatial 2.0 technology revolution characterised by new data sources thanks to new sensors (lidar, hyperspectral, imagery, bathymetric etc), IoT and real-time data, the evolution of multi-dimensional geospatial platforms and artificial intelligence has opened up a world of Blue Oceans.
• Blue Oceans are markets associated with high potential profits, characterized by uncontested market space and a lack of competition.

Building a Blue Ocean Hypothesis based on Geospatial Market Trends

So where do I start these types of big, new opportunity explorations?

I construct a high level hypothesis based on trends in the geospatial market. Above I summarized the technical base for Geospatial 2.0. The high level business value Geospatial 2.0 delivers is the ability to model the real world to drive better understanding and from that improved insight to make better decisions. In essence using digital technology to make ours a better world.

Okay, next we need to drill deeper.

I believe, at its heart, the immediate Geospatial 2.0 Blue Ocean opportunity is centred on intelligent, dynamic digital twins (IDDT). Intelligent suggests artificial intelligence. Dynamic means real time. Digital twins; these are 3D digital representations of the real-world.

So which sectors have clear use cases for IDDT, and might be early adopters of the technology? I believe AEC/infrastructure.

First phase of our hypothesis complete.

Hypothesis Validation with Generative AI

You might know I am a prolific user of Generative AI, and have encouraged my readers to be the same. So, it is time to check our first phase hypothesis with our favourite LLM (ChatGPT, Claude, Morphic, Perplexity etc). Here is our prompt:

“I am building a Blue Ocean business case. Based on a web search, I want you to critique this hypothesis: I believe we are in the midst of a geospatial revolution, what I have been calling Geospatial 2.0. This has been driven by new data sources thanks to new sensors (lidar, hyperspectral, imagery, bathymetric etc), IoT and real-time data, the evolution of multi-dimensional geospatial platforms and artificial intelligence. In real terms this means we can now construct intelligent, dynamic digital twins. Intelligent = artificial intelligence. Dynamic = real time. Digital twins = 3D digital representations of the real-world. I believe AEC/infrastructure have clear use cases for IDDT, and might be early adopters of the technology?”

I’ll not print the full response from ChatGPT (feel free to try this prompt yourself), but here is the first sentence in the reply:

“Your hypothesis aligns with current industry trends and makes a strong case for a Blue Ocean strategy in the geospatial revolution, particularly within the architecture, engineering, and construction (AEC) sectors.”

We’ve made a good start.

Ideation, Discovering Opportunities and Partnerships

Though still at a high level, we have an initial focus: IDDT in the AEC/infrastructure sector.

This gets us started but we need to dig much deeper, to get laser focused or as Geoffrey Moore in his book Crossing the Chasm puts it: ‘a highly focused, targeted approach to capturing a specific segment of the early majority’, see his D-Day analogy for more details.

The ‘getting deeper focus’ or ideation phase we are now in, from my experience, is by far the most difficult. We are still overcoming friction and trying to gain momentum. So how do we come up with ideas? Here is not how to do it, and something I watched with horror. Lock a small group of techies in a room. Have them come up with 5 ‘great’ ideas. Then have them choose the ‘best’ idea and present that to management in the form of a go-to-market strategy,

Epic fail!

Your goal in this phase is to discover real, significant pain point problems, which have high value ($$$) and can potentially be solved by your own geospatial tech/data/expertise in combination with your partners (existing and new).

Any chance of success in this difficult phase requires a guiding framework. And that framework is design thinking.

Design thinking is a human-centered approach to problem-solving that emphasizes empathy, creativity, and iterative testing to develop innovative solutions.

I will briefly describe design thinking below, then apply it to our problem. For those wanting to find out more on this framework see this article from MIT: Design thinking, explained.

There are 5 phases to design thinking:

1) Empathize – See through the eyes of your customer

2) Define – Discover a problem

3) Ideate – Explore potential solutions

4) Prototype – Build a high level solution

5) Test – Get customer feedback on the prototype

Before I discuss a design thinking scenario, I’m going to add another layer of focus to our exploration. Given recent weather events in the US we will turn our attention to:

Disaster risk management and resilience planning – IDDT targeted at utilities with a focus on severe weather events such as hurricanes.

Here are our 5 design thinking steps:

Empathize – Engage with utility companies in hurricane-prone areas to understand their disaster preparedness and response challenges. Ask them about their pain points, such as power outages, infrastructure failures, and slow recovery times.

Define – Problem statement: Utilities in hurricane-prone areas struggle with real-time visibility into power grid health, making it difficult to predict damage and allocate resources efficiently during a hurricane.

Ideate – Real-time grid monitoring with IDDT: Use IoT sensors and satellite imagery to feed live data into a digital twin, monitoring critical infrastructure (power lines, substations) during hurricanes. Resource allocation optimization: AI-driven models within the digital twin could suggest optimal locations for deploying repair teams, equipment, and resources in response to real-time damage.

Prototype: Create a dynamic digital twin that provides real-time visualization of infrastructure, including power lines, substations, and transformers. Integrate AI to provide automated risk assessments and recommend preemptive measures, such as shutting down vulnerable sections of the grid to prevent cascading failures.

Test – Show the IDDT prototype to key staff within the utility to gather feedback on its functionality and effectiveness. Questions might include: How effectively does the system provide real-time insights into infrastructure damage during a hurricane? What additional features would improve the tool’s utility use during hurricane events (e.g., integration with existing systems, mobile access for field workers)?

In the design thinking diagram above you will notice that steps 3 to 5 are iterative. Arriving at a customer validated solution often takes many iterations. Remember customer feedback is critical, they are helping you design the solution. They may actually end up be your first customers!

Blue Ocean Opportunity?

One side question – how do we know we have come up with a Blue Ocean opportunity? This is a question which needs some level of research. That can start with Generative AI. In our case I asked ChatGPT the following:

Q. Do a web search and tell me whether we have uncovered a blue ocean opportunity

A. The use of Intelligent Dynamic Digital Twins (IDDT) for hurricane response in utilities appears to represent a strong Blue Ocean opportunity.

Your research should not stop here. But this is an encouraging start.

Closing Thoughts

For your sake dear reader, I will extend this article to a fourth in the series. Again, this article is getting a little long. In our next article we will continue our journey in building out our winning new Geospatial Blue Ocean business. Until then ..

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.

I was asked this exact question last week after I published the first article in this series: Building a $100M Geospatial Practice: Foundations and Opportunity Discovery. No great surprise I suppose. My response:

“I’m happy to help answer that question, share with me your budget”

That made the questioner quickly scuttle away!

Setting the Stage

They often say the journey is the greatest pleasure, not necessarily arriving at the destination. So the goal of this article is to provide you with some pointers on your journey to finding these new geospatial opportunities with explosive growth potential.

Let’s recap our last article. I discussed building a new geospatial practice and the 3 paths to generating new revenue and growth:

• Low Hanging Fruit – Leveraging existing relationships.
• Red Oceans – Differentiating yourself in the traditional, highly competitive geospatial marketplace.
• Blue Oceans – Uncontested market spaces.

Many companies in the geospatial industry have similar service-based business models, they are sales led, with relationships being critical to revenue generation.

In other words, many have a singular focus on low hanging fruit.

Most companies operate in Red Oceans. Take the Esri ecosystem as an example, the nearly 3000 partners offer a similar set of services – ArcGIS set-up, configuration, data services etc. Partner level eg. Silver, Gold, Platinum, can be a way to open doors to larger client projects. But overall competition is fierce, and differentiation through marketing limited; taglines like ‘All Things Location’ are depressingly all too common! There are exceptions. SSP Innovations were early to promote themselves as Utility Network experts. Soon after the first iPhone/iPad became available, my company WebMapSolutions positioned ourselves as mobile GIS experts.

But those clearly differentiating themselves in geospatial Red Oceans are few and far between.

The overall current state of the market is this: Many existing geospatial companies have flat revenues and struggle to grow, because:

• Services-based models in Red Oceans is hard to scale.
• A sales led, relationship focused go-to-market approach is a good way to discover opportunities in the Red Ocean spaces you inhabit, but do not allow for wider discovery and innovation.

Geospatial 2.0

Five years ago I began writing articles on Geospatial 2.0. Then, my focus was on the evolution of technology; new data sources thanks to new sensors (lidar, hyperspectral, imagery, bathymetric etc), IoT and real-time data, the evolution of multi-dimensional geospatial platforms like Cesium and artificial intelligence. I had in my mind modelling the real world digitally. The original Geospatial 2.0 discussion was my projection and prediction about the future of geospatial. It was very much a future Blue Oceans discussion.

That future is now here!

Blue Ocean Opportunities

In their book “Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant” (2005), Chan Kim and Renée Mauborgne, professors at INSEAD business school, coined the term “Blue Oceans”. They defined Blue Oceans as markets associated with high potential profits, characterized by uncontested market space and a lack of competition.

Geospatial 2.0 has opened an incredible new world of Blue Ocean opportunities. So how do companies discover these new opportunities?

Before We Dive In

Let me share that what I am about to walk you through is based on my long career in the geospatial industry. I’ve worked on both the technical and commercial (sales, marketing, product) sides of the GIS and remote sensing sectors respectfully. I’ve founded and run two successful start-ups. And been a part of both stunning successes and miserable failures.

Those successes and failures have helped shape how I think and the work I do today.

Your $100m Roadmap

I realize many reading this article might hope here I will provide my top 5 Blue Ocean geospatial opportunities. As mentioned earlier, with the right budget, I would of course be happy to do just that!

But here let me lay out your $100m Blue Ocean roadmap.

1) The Success Foundation

To successfully swim in Blue Oceans, your organization will need to change. I’ve worked with many who have tried and failed. Fear and inertia were their biggest enemies.

Before we dive deeper, let’s consider some of those foundational change elements which will help you succeed.

a) In-House Expertise

There is a good chance you will need to recruit. That can be permanent staff or consultants like myself (cheap plug). Too many geospatial companies try to ‘reshuffle their existing pack’. In other words move existing staff into this role. I’ve watched, and advised against this many times. The results in all cases has been disastrous; with much money and time lost and no tangible results. The staff members you hire need close access to leadership, but be given a high level of autonomy.

Note, these folks are hard to find. You are not looking for outside consultants with no geospatial experience. You are looking for the new breed of geospatial commercial thinkers.

b) Market Understanding

There is understanding YOUR market, and then there is understanding THE market. To repeat myself, Geospatial 2.0 has opened an incredible new world of Blue Ocean opportunities. You must understand Geospatial 2.0; what is driving it and the trends which are shaping this new world. In the case of my company WebMapSolutions; it was the release of a large screened mobile device (iPad followed by the next generation of iPhones) with built in GPS, which created our mobile GIS Blue Ocean opportunity. I’d obviously be happy to tell you more about current trends, but let me share with you one recent event you should scrutinize in depth: the acquisition of Cesium by Bentley.

The Geospatial 2.0 implications of this merger are huge!

c) Collaborative culture

You need to have a collaborative culture. You might boast a flat structure and a ‘servant’ leadership team, that is just fluff. I remember one VP of sales telling me “Don’t worry (your little head) over our strategy”. Condescending sure, but in reality he had no strategy, at least not one that was built around winning. How did I know – 5 years of consistent flat revenue growth!

As we will discuss strategies need to be well thought through, adjusted, and available to all in the organization to provide ongoing feedback (as more is learned). Collaboration goes deeper than this, but building a true collaborative culture is critical to Blue Ocean success

d) Innovation Led by the Commercial Team

Geospatial companies are filled with techies. That is the very nature of service companies. Commercial teams are small, and often filled with non-geospatial folks (which i have always found odd). Too often innovation, to drive new Blue Ocean revenue, is led by technologists. I remember one CEO frustratingly saying to me one time, “We have 5 MVP’s on the shelf we simply cannot get to market!” When I dug deeper, I found one very self assured technologists was behind most of these MVP’s. Instead of market and customer validated, these were simply his ‘great’ ideas.

When scrutinized, none of these MVP’s had any market value!

2) Geospatial 2.0 Blue Ocean Triad

Okay, with the success prerequisites out of the way, let’s dive deeper.

Studies commonly estimate that around 70% to 90% of new products/solutions fail. Factors behind this high rate of failure include: Lack of market need, poor product-market fit, insufficient differentiation, pricing issues, weak marketing and positioning, internal factors including poor management, and inadequate funding.

So are there ways to improve your chances of success, particularly when it comes to Blue Oceans?

Let me introduce to you the Geospatial 2.0 Blue Ocean Triad; a set of interlinking and overlapping frameworks.

There are 3 parts to the Triad:

1) Blue Ocean Opportunity Discovery

I remember working with a GIS service company some years ago who wanted to build their first product. One of their clients had shared a particularly challenging problem. After careful consideration, the CEO decided they should pull a small team from their service duties to build a prototype. The prototype was good, the potential client liked it. Encouraged, the CEO decided they should next commercialize the prototype; in other words get this potential client and others to help fund turning the prototype into a product. The sales team got busy. And failed!

Inexperience, and a rush to revenue are common challenges when it comes to arguably the hardest part of the Blue Oceans journey; Opportunity discovery.

Opportunity discovery takes, time, patience, funding and a guiding framework. And that framework is Design Thinking. I discovered design thinking while helping the CEO I mentioned earlier with the ‘5 MVP’s on the shelf’ problem.

Design thinking is a human-centered approach to problem-solving that emphasizes empathy, creativity, and iterative testing to develop innovative solutions.

Leveraging the design thinking framework is one of the cornerstone of the work I do today. I have had major successes in discovering hidden Blue Ocean opportunities, leveraging the framework. I will share the GeoChatGPT story in a future post, just to provide one example.

For this article, which is becoming longer than originally planned, here is a great introductory article from MIT entitled Design thinking, explained.

2) Blue Ocean Strategy

I worked with a mid-sized geospatial company who derived much of their revenue from Federal contracts. Looking to move from single digit to double digit revenue growth, the leadership team debated whether they should drop some of the companies smaller lines of business and focus resources on broadening and deepening the Federal business. A group within the go-to-market team disagreed: “Give us 1 year and we will build a revenue powerhouse in one of the smaller business lines”. The leadership team acquiesced. Fifteen months later, that line of business had grown to 30% of total company revenues. So how did the team achieve such stunning results in such a short period?

They built a winning strategy!

Strategy is much discussed, and still poorly understood. As experts like Roger Martin might say – it is as much art as it is science. But having a real strategy – see a strategy is not a plan – is vital for success in Blue Oceans. Again, we will explore this topic in much great depth in a future post. But to get a taster of what is my favourite strategy framework; see this article on the Strategy Cascade.

3) Blue Ocean Go-to-Market Execution

A large geospatial company were looking to increase their US market presence. They had a group of new products. Many of these products had been developed for very specific verticals outside of the US. But the US sales team were told “Get out there and sell”.

The sales team struggled. One year of hard work, resulted in few new orders. The sales team were replaced. With the new team in place: another year of hard work, again the results were the same. The cycle repeated.

It became clear that the sales team were not the problem, there was a much deeper go-to-market problem. For now, let’s leave this case study right there.

The big take home message – don’t be fooled, you may have successfully executed the challenging first two steps of the Geospatial 2.0 Blue Ocean Triad – discovery and strategy. But presuming this final step will be easy is a huge mistake.

As with the other parts of this discussion, we will dive deeper into this topic in future articles. For now, read this summary article on Crossing the Chasm.

Final Thoughts

This is a longer article than I had originally planned. But it takes you through the building blocks of finding those $100m Geospatial Blue Ocean Opportunities. In the next article in this series, we will build a hypothetical scenario (based on some real work) to help better paint the picture.

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.

In today’s rapidly evolving technological landscape, the geospatial industry stands at an inflection point, poised for unprecedented growth. This article, the first in a three-part series, explores the foundations of building a successful geospatial practice and the art of opportunity discovery in this dynamic field.

The Vision: From Zero to $100M in 5 Years

The geospatial industry is experiencing a renaissance, driven by technological advancements, new multi-dimensional data sources, and the integration of artificial intelligence. For companies looking to capitalize on this growth, the goal of building a $100 million practice in just five years is ambitious but achievable with the right approach.

Core Practice Elements

To lay a solid foundation for a geospatial practice, several key elements must be considered:

1. Focus: Determine the specific areas within the geospatial industry where your practice will excel.
2. Skills & Knowledge: Cultivate a team with diverse expertise in geospatial technologies and domain-specific knowledge.
3. Technology Stack & Data: Invest in cutting-edge tools and ensure access to high-quality, relevant data sources.
4. Business Model: Decide on the right mix of consultancy, services, and products to offer.
5. Business Plan and Process: Develop a comprehensive strategy for growth and operational efficiency.

Opportunity Discovery: The First Step to Success

Identifying the right opportunities is crucial for rapid growth in the geospatial sector. Here are key strategies for effective opportunity discovery:

Start by targeting easily accessible markets with immediate needs that align with your capabilities. Leveraging existing relationships and clients can provide quick wins and help establish your practice in the industry.

Red Ocean refers to a competitive market space where companies vie for a greater share of limited demand. Why red? Blood in the water, as contenders fight over customers!

As you establish your practice you will need to discover opportunities in the existing marketplace. Winning in this crowded space will require careful thought, and a solid strategy. In this competitive landscape, it’s essential to stand out. Differentiation is key. Discover and develop unique offerings or approaches that set your practice apart from established players.

Blue Oceans ‘represent uncontested market spaces with ample potential for growth, where companies can create new demand and differentiate themselves through innovative products or services’. Thanks to technology advances, new multi-dimensional data, and AI the geospatial industry is at an inflexion point in terms of growth. This is a very unique time. A huge untapped geospatial market space is opening up, this offers incredible potential for growth and profitability.

Summary

This is the first article in a three-part series. In part 1, we laid out the foundations. Our core focus was on opportunity discovery – the first part of what we call the Business Growth Triad. We outlined the 3 key areas a new industry practice needs to explore to uncover market opportunities.

From zero to $100M in 5 Years is an ambitious goal. Low hanging fruit and winning in red oceans would contribute, but it is in blue oceans where this ambitious goal can become a reality.

More on this topic in the next article in the series.

Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.