Building Ecosystem Business Models for Mid-Sized Firms are so often under-looked in much of the literature we are referred too. We get caught in the bigger players, often provided by the large consultant companies, for our references- such as Alibaba, Salesforce, Amazon, Apple, Siemens, etc,.
How can Mid-sized Business Organizations set about to build out an Ecosystem Business Model using third party providers for platforms, communication technology, data analysis and use of Gen AI?
Are these as expensive as initially feared, can they work as effectively as those provided for the bigger players offering Ecosystem solutions? You need to build out a projection of possible budgets for costs in the first year and then annual ongoing ones. Ecosystem building often runs into sometimes hundred of millions of dollars but taking a really small step I (really) hesitate here, but $2.0m to $3.0m for the first year to eighteen months provides you your dedicated Ecosystem, and yearly $1m, including team costs BUT it so depends. It is where you take this, in recognizing its value, diversity to your business and worth, determines where you take this out.
So please take these numbers as only a starters point to get your levels of interest up or otherwise I recommend you don’t bother to read the rest of the post! Scaling, then the numbers rise fast but so can the accelerated returns!
This is only a brief guidance to get you to relate and see if the “ecosystem juices” are flowing. It is not comprehensive but it does go into a starting point of a Mid-sized firms starting point to Business Ecosystems. Even if I hear some readers mutter these numbers are crazily low you have a initial framework to build up into a project
I want to here address a number of questions any organization contemplating this building a ecosystem would need to go through. There are a good few more but lets limit this to a level where they can be absorbed and then I suggest to go further then come and ask me.
So let me help you understand how mid-sized ecosystem business models, mostly through third-party ecosystems can build an Ecosystem Business Model.
We do need to forget traditional operating models when dealing in Business Ecosystems be warned
I’ll break this down into key components and provide some examples in a healthcare context to keep it a little focused.
Key Components of an Ecosystem Business Model:
Core Platform Selection:
- Choose established platforms that can scale with your business while maintaining cost efficiency
- Focus on platforms that offer strong API integration capabilities and partner networks
- Example: A mid-sized healthcare provider might use Salesforce Health Cloud as their core platform, integrating it with specialized healthcare apps and services gives domain expertise and proven business cases
Communication Technology Integration:
- Implement unified communication systems that connect internal teams, partners, and customers
- Utilize APIs to ensure smooth data flow between different communication channels
- Example: Using Twilio provides programmable communication tools for patient communications, integrated with your core platform for seamless interaction tracking
Data Analysis Infrastructure:
- Build a data lake or warehouse that can aggregate data from multiple sources
- Implement analytics tools that can process and visualize data from various ecosystem partners
- Example: Using Snowflake for data warehousing, connected to Tableau for analytics and visualization Snowflake operates a platform that allows for data analysis and simultaneous access of data sets with minimal latency. It operates on Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
Gen AI Integration:
- Start with specific use cases where AI can add immediate value
- Partner with established AI providers rather than building from scratch
- Example: Using OpenAI’s API for customer service automation or document analysis. There are specialized AI providers that can add and accelerate health in multiple ways.
Real-World Examples:
- Healthcare Ecosystem: One Medical (before Amazon acquisition) built a successful ecosystem by:
- Partnering with local labs for testing
- Integrating with pharmacy networks
- Using telemedicine platforms
- Connecting with insurance providers
- Implementing electronic health record systems This created a seamless experience for both patients and healthcare providers.
- Local/Regional Example: Farmer’s Business Network (FBN) created an agricultural ecosystem by:
- Connecting farmers with suppliers
- Providing data analytics for crop optimization
- Offering financial services through partnerships
- Integrating weather data and market pricing This helped mid-sized farms compete more effectively with larger operations.
- Industry Space Example: to built a manufacturing ecosystem by:
- Connecting manufacturers with customers
- Integrating CAD/CAM software providers
- Partnering with material suppliers
- Providing quality assurance services
- Offering financing options through partnerships
These can be the suggested implementation Steps for Mid-sized Organizations:
- Start with Core Value:
- Identify your organization’s core strength
- Build initial partnerships around enhancing this core value
- Example: A regional healthcare provider might start with patient scheduling and telemedicine
- Gradual Integration:
- Begin with 2-3 key partners
- Test integrations thoroughly before scaling
- Add new partners based on customer needs and data insights
- Data Strategy:
- Implement data sharing agreements with partners
- Ensure compliance with relevant regulations
- Create value through shared insights
- Example: Sharing anonymized patient data with research partners while maintaining HIPAA compliance
- Technology Stack:
- Choose scalable, cloud-based solutions
- Ensure security and compliance
- Prioritize ease of integration
- Example: Using AWS for infrastructure, with specific healthcare-compliant services
- Partner Selection:
- Focus on partners who complement your core services
- Ensure cultural and technological fit
- Start with established providers who understand your industry
To form the backbone of any ecosystem business model
Let me break this down by core components:
Core Platform & Infrastructure Providers:
- Base Platform Options:
- Salesforce Platform: Offers robust APIs, app marketplace, and integration capabilities
- Microsoft Azure: Full stack with emphasis on enterprise integration
- AWS: Comprehensive cloud infrastructure with broad partner network
- Google Cloud Platform: Strong in AI/ML and data analytics
- Integration & API Management:
- MuleSoft: Enterprise-grade API management and integration
- Apigee: API management with analytics
- Kong: Open-source API gateway
- Postman: API development and testing platform
- Data Management & Analytics:
- Snowflake: Data warehouse with strong partner ecosystem
- Databricks: Unified analytics platform
- MongoDB Atlas: Document database with analytics capabilities
- Amazon Redshift: Data warehouse with AWS integration
- Communication & Collaboration:
- Twilio: Communication APIs and platform
- Zoom Developer Platform: Video/audio communication
- SendGrid: Email communication platform
- Slack Platform: Team collaboration with extensive integrations
- AI/ML Services:
- OpenAI API: Language models and GPT integration
- Google Cloud AI: Comprehensive AI services
- Amazon SageMaker: ML platform with pre-built models
- Azure Cognitive Services: AI capabilities for various use cases
- Things change so this suggested here of possible providers might need some amending through some of your own research and (experienced) viewpoints of your building team
Recommended Stacks for Different Sectors needs some consideration as well, taking three examples:
- Healthcare Ecosystem Stack:
- Core Platform: Salesforce Health Cloud
- Data Platform: Snowflake Healthcare Data Cloud
- Integration: MuleSoft
- Communication: Twilio for patient engagement
- AI: Azure Cognitive Services (HIPAA compliant)
- Security: Ping Identity for authentication
- Manufacturing/Supply Chain Stack:
- Core Platform: AWS
- Data Platform: Databricks
- Integration: Kong Enterprise
- IoT Platform: AWS IoT Core
- AI: Amazon SageMaker
- Security: Okta
- Financial Services Stack:
- Core Platform: Microsoft Azure
- Data Platform: Snowflake
- Integration: Apigee
- Communication: Twilio
- AI: OpenAI API
- Security: Auth0
- Again these are just (suggested) examples, by your own investigation or with a external implementation partner you can define this more to your needs and budget!
I reckon this whole evaluation, learning, evaluating, investigation and ‘stringing it’ all together has an estimated total time to Initial Launch: 12-18 months
You need to recognize and build in more for these pointers as it always grows in levels of complexity or that magic “need to have”
Cost/Time Reduction Factors:
- Using pre-built solutions vs custom development
- Choosing fewer initial integrations
- Starting with smaller feature set
- Leveraging platform accelerators
- Using managed services
Cost/Time Increasing Factors:
- Heavy customization requirements
- Complex regulatory environment
- Multiple country deployments
- High security requirements
- Complex data migration needs
Success Pattern recognizing Common Elements:
- Start with existing customer base
- Solve clear operational pain points
- Add partners incrementally
- Focus on specific industry verticals
- Build on existing relationships
Learning Resources take time and investment:
- Platform-specific training:
- AWS Training and Certification
- Microsoft Learn
- Salesforce Trailhead
- Google Cloud Training
Implementation Frameworks: to go and learn
- The Open Group Architecture Framework (TOGAF)
- AWS Well-architected Framework
- Microsoft Cloud Adoption Framework
Suggested Starting Point so think how you break up (any) your teams focus areas
- Begin with core infrastructure:
- Add integration layer:
- Build communication capabilities:
- Add AI/ML capabilities:
Focusing on key phases and critical success factors: get these really well thought through
Phase 1: Foundation Setting (3-4 months)
Governance Framework:
- Establish clear decision rights and accountability
- Define data governance policies
- Create partner selection criteria
- etc., etc
Value Proposition Design:
- Identify ecosystem value creation opportunities
- Define revenue sharing models
- Create clear partner value propositions
- Establish ROI measurement frameworks
Phase 2: Core Platform Development (4-6 months)
Technical Infrastructure:
- Build/implement core platform components
- Establish API standards
- Create data sharing protocols
- Implement security measures
- etc., etc
Operational Processes:
- Design partner on-boarding processes
- Create service level agreements, support mechanisms
- Define escalation procedures
- Develop change management process
- etc., etc
Phase 3: Partner Engagement (3-4 months)
Partner Selection:
- Identify initial strategic partners
- Conduct capability assessments
- Negotiate commercial terms
- Define joint success metrics
- Create partner development plans
- etc., etc
Integration Process:
- Technical integration planning
- Process alignment and staff training
- Customer communication feedback and exploring
- Testing and validation
- etc., etc
Phase 4: Scale and Optimize (Ongoing)
Innovation Management:
- Create innovation frameworks
- Establish feedback loops
- Set up experimentation processes
- Define scaling criteria
- Monitor market trends
- etc., etc.
Revenue Models:
- Transaction fees
- Subscription or revenue generation models
- Value-added services, phasing and choice
- Data monetization and capturing mechanisms
- Joint venture opportunities to reduce in-house work, tracking
- etc., etc.
The greatest tip in my mind is to give these essential criteria a real focus upon:
- Governance
- Innovation potential
- Revenue Sharing and Partner Alignment
- Data Management
- Customer Experience
- Intellectual Property and Branding Assignments
Finally how to measure and assess if this is going to plan and showing some real Critical Success Factors:
- Trust Building
- Value Creation
- Operational Excellence
- Partner Management
- Risk Management
Keep checking these- a constant looping back to understand and build resolve!
My final views of caution BUT equally encouragement
Building an Ecosystem can be done by any mid-sized firm. It is the ‘burning’ need, the seen and emerging values it can give over your existing business and all the new market and product revenue opportunities that can come out of “opening your mind” and resolving ambition and beliefs.
You cannot give the level of “justice” within this post to the thinking through and work this builds towards. You are in need of a full project plan.
Business Ecosystems need to have a real multiplier on your existing business, otherwise you can get distracted, caught up in managing the existing and shifting a focus to this transformational opportunity and can fall between the two ( existing vs needed)
I would argue you need some external implementation partners to help on this. My role is to advise, mentor and coach, I am not equipped to be a on-site consultant.
+ Errors and omissions can happen in providing this, it is simply an opening guide so do not hold my feet to the flames!
+ I worked with Gen AI to support the structure, some of its validation and the questioning that kept occurring.