How not to get a job!

Let's talk about revenue and growth models just a little bit. How does ISV interact with the large companies in their ecosystems and build revenue streams?

How not to get a job!
Photo by Clark Tibbs / Unsplash

This is a very long article and very personal, so beware. You may have to come back and re-read it. I wrote the outline while having Salad verte and Lyonnaisse Gratin soup on Friday afternoon at one of my favorite French restaurants in downtown Seattle, Le Pichet. It was a luxury as I had not been out to eat in quite some time. Then I finished writing it in my living room over the next 6 hours. It starts slow, so bear with me a bit. I say that again below. ha!

Let's talk about revenue and growth models just a little bit. AWS, Adobe, and Salesforce's revenue growth journey for their business SaaS software units is often visualized as a CRAZY EXPONENTIAL climb. Initially, these SaaS-type companies experience a rapid ascent. They are a platform, not an application. This means they add smaller data products to the platform over time to keep growth moving up. If they do not do this, they plateau. Their success is why smaller SaaS and ISVs attempt to mirror these giants by building solutions that fit the ecosystem and build periphery or complementary applications or even new platforms. You always hear the new founder saying they are the next billionaire and they are building the next billion-dollar company. However ambitious that might be, it is super rare.

Difference between Application sales vs Platform sales

van der Kooij, Jacco; Pizarro, Fernando; Winning by Design. Blueprints for a SaaS Sales Organization: How to Design, Build and Scale a Customer-Centric Sales Organization (Sales Blueprints Book 2) (p. 36). Winning by Design. Kindle Edition.
Model 1: Application Service Model 2: Platform Service

A SaaS application service, such as file sharing and communications tools (think Slack in the early days or Figma). Low value often available with a month-to-month subscription. As the service gains adoption, its growth compounds because of network effects. This model has the following characteristics:

  • Easy to sell. Not critical to the business, and easy to migrate.
  • Easy DIY integration, with expansion coming from peer-to-peer reference selling.
  • The growth is virtually uncapped and can reach 10-100x.
  • Strong focus by the vendor on upsell/cross-sell.

A SaaS platform service, such as CRM and Marketing platforms, commonly offered under an annual contract. This model has the following characteristics:

  • Complex initial sale.
  • Critical to the customer’s business and hard to migrate.
  • Requires sophisticated integration, involvement of engineers in the sales process.
  • The growth is capped to renewal plus a price increase based on new functionality (Premium packages).
  • Strong focus by the vendor on churn prevention/renewal.

However, as a SaaS company grows, its growth slows and begins to plateau; some even start declining because they fail to innovate, merge, or acquire new solutions to sell. I have worked for a couple of these in the past. This phenomenon, known as reaching a saturation point, marks a pivotal moment when once abundant opportunities for expansion start to dwindle. Usually around $100M in revenue.

Why does this happen? Companies like Adobe, Salesforce, and AWS encounter a natural limit to how much they can grow using their initial business model or products, even if they grew through creating a business unit through acquisition. Look at Adobe; that was their initial foray into marketing beyond just media creative tools, then to make an end(media)-to-end(marketing) company - three separate platforms all loosely integrated or connected functionally because of how the $1Trillion marketing industry works. They even recently tried to acquire Figma, an application service to keep showing growth on the Creative Platform side of the end-to-end. They built the AEP as a bigger platform to house the siloed data apps in the Experience cloud suite so they could add in more and more and keep growing.

They did this because the Experience Marketing Cloud slowed down, another thing that happens is changing the pricing models to help growth. Adobe changed its pricing model 10x or more for the Marketing BU products in 2009, and since then, acquired a dozen or more tools and products or added new products to the current portfolio. 

AWS is similar. The reasons are multifaceted:

  • The market might become oversaturated.
  • They have captured the larger customers already.
  • They had to change or manipulate pricing or tools to compete.
  • The competitive landscape becomes so intense that acquiring new customers becomes costly.

They have to change constantly to keep up. Ambiguity keeps them busy.

The concept of "Diminishing returns" is at the heart of the issue, which has been extensively researched. The reason to start a SaaS company is "Increasing Returns ." I first ran across this concept in my last years in college, in 1996. GO HUSKIES!. I went to college late as I was in the Military, and I am unsure where I read about it. W. Brian Arthur expanded on the 100-year-old theory. Here is an article from CNN Money circa 1996 about it. 

The premise is simple: As companies grow, each effort to increase market share becomes less effective and more expensive. This challenge is akin to trying to squeeze more water from a wrung-out sponge – at some point, the sponge cannot hold or give more. There are a few other theories on the periphery, like Long-tail for retailers - which is a model Amazon used early on, but for the most part, Increasing returns is a perfect fit in software.

To navigate beyond this plateau, companies must strategize meticulously, employing one or more of the following tactics with their overall revenue growth strategy dictated by the CFP and CRO teams. I have experienced nearly all of these aspects during my career in some form, and each has impacted me at my role level in some other way or my team that I had to support. 

Again, it's all changing constantly and very ambiguous. 

This premise is why flexibility, adaptability, and not letting ambiguity get to you are top-line items in job descriptions. Some ambiguities occur annually during a Fiscal Year within these SaaS companies. Here are a few.

  1. Expanding into New Markets: Venturing into new geographical areas or industries to uncover fresh customer bases.
  2. Innovating with New Features or Services: Continuously improving or adding to their offerings to create additional customer value, encouraging retention and attraction of new clientele.
  3. Vertical Integration or Expansion: Broadening their solution stack to offer more comprehensive services, thereby capturing a larger share of customer spend.
  4. Acquisitions: This is the idea of buying out other companies quickly to gain new technologies, market segments, or customer bases - aka revenue.
  5. Developing Partnerships and Ecosystems: Creating networks extending the core offerings' value through software integrations or strategic alliances.
  6. Adjusting Pricing Strategies: Modifying pricing models to better match the perceived value among different customer segments, potentially unlocking new revenue streams.

In essence, reaching a saturation point is not the end of growth; it's a signal for transformation. It demands that companies innovate beyond their original boundaries, whether by deepening their market penetration with new offerings, branching into new territories, or even reshaping their business model entirely. This stage involves finding new levers to pull, ensuring the company continues evolving and expanding its horizons, even as some paths become less traversable. Most companies can't do it due to bias, lack of the ability to change, or just static status quo thinking. Let's move to a more person topic in the article.


Last week, on February 2, I met with a hiring manager in downtown Seattle. I was interviewing for a role that managed product ideation for the AWS Partnership relationship. 

The role was to help the two companies intersect from a product perspective and develop different products that add value along the content and data supply chains. This would bring both companies more revenue growth to prevent existing customers from churning, attract new customers, or add value to existing customer relationships. 

The combined value could help overcome the abovementioned issues, which are growing(stop diminishing returns and restart increasing returns) or stopping attrition, all supporting the idea in #5 above. When combined, the idea was to exploit existing technologies to surface better abstractions within the platforms.

Unfortunately, he rejected me for the role. 

Why? I couldn't convey the executive presence he was ultimately looking for in the person for this role is my guess, along with other aspects around product ideation; I get it. That is a common aspect of many roles I have interviewed for.

Most companies are looking for unicorns today. Especially for the roles I get interviewed for.

For this company, however, I suspect the person who gets hired does not come close to what I am presenting below and may call or email me for advice if they ever come across this article.

Maybe, maybe not.

What's below? The ideas I share below would have been exploitable for the hiring manager, FREE OF CHARGE, but if another ISV SaaS company were innovative, they would get there first and create a strategy to sell the capability to AWS, Salesforce, Adobe, or another big platform. It would satisfy #2, 3, or #4 above and again quell the diminishing returns issue. They would do this by getting traction on the platform marketplaces(Adobe and Salesforce have big ones) and selling to the largest customers already captured.

So, let's start with artificial intelligence (AI) and data management technologies for cloud platforms like AWS and Adobe's Experience Platform (AEP). Both represent a frontier of innovation and opportunity for the ISV, not just AWS and Adobe. I barely touched on these topics with my interviewer because I have yet to memorize them all, I don't know what he was looking for as an answer, but I gave the wrong one.

Memorization off the cuff is a trait many hiring managers are looking for today. It isn't enjoyable. He asked me point blank for my ideas right off the top of my head - It was a fair question, but I failed it miserably – I told him I had a deep list of use cases but could not give him detailed specifics verbally – it's nearly impossible due to the complexities of these topics, platforms, and the underlying technologies. However, I did hit on specific themes.

    • AI
    • Taxonomic and semantic management
    • ID and Entity Resolution
    • ML Analytics

They did not trigger much interest, even though my knowledge was apparent. He was very kind and undoubtedly looking for someone with other skills that evening in downtown Seattle, and I couldn't focus on what those were. It was just another failure on my part. I have had so many in my life that I cannot count that high. I thought trying to find a life partner was hard - this is just more condensed, so it's like 1000 knife cuts in 10 minutes vs. over 30 years. It hurts a lot worse and doesn't heal as fast.

The job description was also ambiguous, which is why I mentioned it. Because they want you to play in those areas of continual ambiguity - it's part of most company culture. I lived it at Omniture and Adobe for over a decade - but it's really just corporate America. You also have to play into the hiring managers' ambiguity of not knowing how to describe who they are looking for to fill a role even if you have been in 1000 client meetings in a year ( which I have - another story) and have interacted with Product, Marketing, Engineering, Sales, and Support, all for a single client or a group of clients within your career, which I have many times over. Those, a 20+ year career, and $1B in ARR impact mean nothing today.

This is the longest I have not worked since I was 13 years old. It sucks, to say the least. You can build all this knowledge within a 20-year career with a large company or build a product in a startup mode – which I also did between Omniture and Adobe stints - check out this product I built to get a taste as an entrepreneur. It was an e-commerce application built in NODEJS in early 2011 before that capability was even present in NODEJS - I acted as the Product Manager(Founder) for the FT developer I had hired. It is in my GitHub under the KLEARCHOICE REPOs. 

And still not get a job because of all this experience. It's very weird, and many other adjectives.

I interacted with people on the Omniture/Adobe Analytics tools product teams and, after that, mostly with intersections on the other tools like Campaign, Target, and Audience Manager. I have lots of experience in these realms.

Okay, so let's get on with the rest of the story and the value some of you are most likely seeking compared to my employment story or lack of.

The ideas for these two companies' technologies are complex, but let's see if those ideas resonate with any ISV, Adobe, Salesforce, or AWS teams. If they make sense, they could hire me to help them manage a project to develop them, providing insights into future directions, peripheral models, and the potential for creating novel, value-driven tools and applications. I am pretty pessimistic about that outcome now.


Evolving Beyond RAG Systems: The Next AI Frontier

Retrieval-Augmented Generation (RAG) is an AI framework that combines the generative capabilities of language models with real-time information retrieval from external databases. This integration enables RAG to produce contextually informed and highly accurate responses, enhancing its utility in diverse applications.

💡
Note: Large Language Models (LLMs) generate text (e.g., for ChatGPT). RAG combines two systems: Retrieval to get information from a data source and an LLM to generate a response. LLMs are a component of RAG systems (used for other things) (SOURCE).

RAG (Retrieval-Augmented Generation) systems have set a new benchmark in AI's ability to harness external information for generating nuanced, contextually relevant responses.

Source: https://medium.com/curiosity-ai/introduction-to-rag-genai-systems-for-knowledge-918a34054228

Emerging technologies promise to eclipse RAG systems, introducing more sophisticated forms of information retrieval, reasoning, and generation. Let's start at a high level and then extrapolate them into more finite solutions. Bare with me as you read below. Let's start with the emerging CORE foundations.

    • Dense Retrieval Techniques: Moving past keyword-based searches to semantically rich, context-aware retrievals that understand the nuances of human language.
    • Chain-of-Thought Reasoning: This is a leap towards AI systems capable of intermediate reasoning, mimicking the human thought process to tackle complex problem-solving tasks.
    • Few-Shot Learning and Meta-Learning Models: These models adapt swiftly with minimal data, embodying the learning agility of a startup pivoting in real-time to market feedback. Apple is developing some of these.
    • Multimodal Retrieval-Augmented Models: Envision AI reading texts and interpreting images, sounds, and videos, crafting responses that resonate on a human level. We see this with Open AI now.
    • Knowledge-Enhanced Transformers: This technology integrates structured knowledge directly into transformers, enhancing generation accuracy by accessing a wide array of structured information.
    • Decentralized and Federated Learning Systems: These systems enable AI models to operate without centralizing data, addressing privacy concerns and data regulations through more secure, private information augmentation.
    • Interactive and Iterative Learning Systems: Involves dynamic interaction between the model and information retrieval, refining queries iteratively for more precise and relevant outcomes.
    • Explainable AI (XAI) in RAG Systems: Aim to enhance transparency and understandability in complex RAG systems, improving trust and usability through clear explanations of decision-making processes.

These evolving technologies are not just upgrades but paradigm shifts, offering new frameworks for AI's role in data analysis, content creation, and decision-making. Can you imagine what they could do for marketers or sales? Think about how quickly an ISV or, if you think AWS or Adobe can move quickly, how fast they could eclipse any smaller company with newer technologies. The issue is that AWS and Adobe are not nimble or quick. They have severe structured processes. 

AWS is faster than Adobe based on its culture but still slow. The better option is a small partner ISV company that has a few developers who have done the work using Steve Blank's Customer Development framework to see what exactly is needed in terms of getting out of the office and talking to the actual Adobe or AWS customers and building something that exploits one or more of the above and below approaches.

These are foundational technologies and are still emerging but very promising. What are the current peripheral or complementary technologies that the ISVs, Adobe, and AWS can more deeply exploit and build more customer value as an embedded offer?


Peripheral Models and Systems: Expanding the AI Ecosystem

Peripheral technologies complement core AI advancements, addressing the broader needs of data management, automation, and business intelligence. They represent the tools and infrastructure that make AI applications feasible, efficient, scalable, and aligned with business objectives. Here is the next layer.

  • Automated Machine Learning (AutoML) simplifies model selection and training, democratizing AI for non-specialists. Many vendors have attempted to use this model and failed miserably. I can't name names on who those vendors are, but if you don't get specific with this model and embed it to do something specific, you will fail, and the marketer will not use it.
  • Natural Language Processing (NLP) for Business Intelligence turns unstructured data into actionable insights, mining gold from the internet's digital chatter. We see lots of variations of NLP - I have seen variations of it since 2001. GPT is NLP on steroids. However, you can flip the script with it and build something novel and unique to help the revenue generation process within companies.
  • Robotic Process Automation (RPA) with AI automates mundane tasks, freeing human creativity for higher pursuits. This approach is prevalent now(it's a $ Billion industry). Automation Anywhere, UI Path, and even Microsoft are deep into this model and approach. It's still very valid, but I have yet to see the Martech world adopt it well, and it has substantial exploitable potential.
  • Predictive Analytics and Forecasting offer the foresight of an oracle, enabling businesses to navigate the future with data-driven confidence. This theme has many variations but has never been exploited well in Adobe, AWS, and others for MarTech or Sales teams. They create fancy tools and give them human or Meme names. They will all say they do this already, but they really don't. I have a list of ideas below for this area, but not sure how valid they are for customers. Can you help validate them?
  • AI-Driven Cybersecurity: This approach utilizes AI and machine learning to efficiently detect and respond to cyber threats, safeguard data, and ensure business continuity. The theme is heavily neglected in various touch points within Martech - the only place I ever see it is in Commerce and anywhere a Credit card is consumed, but there is so much more that could be exploited to save millions of dollars in theft annually. Albeit the platforms have deep infrastructure security, what about surfacing this as observability and lineage for other uses?
  • Blockchain for Data Integrity and Traceability: This technology integrates blockchain with AI to ensure secure, transparent data management, crucial for supply chains and financial transactions. In my opinion, it is still too slow and costly, but it has valid merits.  I want someone to prove me wrong.
    • I like the idea of DIDs(Digital or sovereign IDs), but many other approaches have potential.
  • Edge Computing and AI: This technology combines AI with edge computing to enable real-time analytics and decision-making, reducing latency in critical applications like IoT and autonomous vehicles. I love this one, but it's still too expensive. 
    • Adobe Target and Adobe Data Collection both use this approach. Adobe I/O and Runtime have considerable potential to expand this area and exploit better personalization for any channel, not just IoT or AVs.
    • I have alluded DPO (direct preference optimization) and Reinforcement learning in other articles – I think a lot more could be exploited in these areas leveraging EDGE.
  • Digital Twins: Employs AI to create virtual simulations of physical objects or systems, enhancing operational efficiency and product design across various industries. I love this one too. I think there is a tremendous synergy between AI synthetic data with Digital twins to test campaign theories along with product GTMs, pricing matrices, supply chain JIT ERP, and just about any scenario you can think of. This approach has foundations in scenario planning, which Herman Kahn invented. It is used extensively in the Military. I used it in my Electronics Warfare role in the early 1990's. It was heavily industrialized into OIL via Royal Dutch Shell and Financial Services. It has enormous implications for Media mix and budgeting, and Adobe uses some of it already in Mix Modeler, but it has much more potential.
  • Conversational AI and Virtual Assistants: Uses advanced AI to provide personalized, automated customer service and productivity enhancements through natural language understanding. We all have seen these and variations of them. It will only expand and improve.
  • AI for Environmental Sustainability: Leverages AI to optimize energy use, predict environmental risks, and support sustainability, contributing to cost savings and regulatory compliance. There are many prominent uses here. I can see this from monitoring the cost of the platforms these customers buy from Adobe and AWS, but I am also nimbly managing all the compliance regulations. As a peripheral tool, abstracting the data from the big platforms can provide substantial cost savings yet to be exploited.

So that is the scaffolding. What are the actual tools? How do we bridge the emerging core tech with the established tech?

How do we create tools that abstract, complement, or augment new revenue streams? Let's consider some ideas below.


Bridging Technologies: Creating New Value

As I mentioned, the true potential lies in integrating the core and peripheral technologies with AWS' and Adobe's cloud services. This synergy can spawn innovative solutions that enhance a customer's operational efficiency and open new avenues for growth and customer engagement. They all meet our original thesis of growing revenue to prevent the diminishing returns issue. The names of these tools may be ambiguous, but hey, that was one of the original points above. If interested, make up a new name and post it in the article comments or the LinkedIn post. The good thing is that I now have the copyright of the idea. Haha. Not really, because - that is nonsense today. I doubt I will ever get paid for it. HA!

Much of this functionality would serve as a central nexus for managing metadata, semantic taxonomies, and the entire data/content supply chain, supported by the robust infrastructure of AWS and the customer experience finesse of Adobe's AEP.

Creating a comprehensive solution is probably not the goal, but it could be if you created all the below as a single platform, adding value to the more prominent platforms that might get some attention. I suggest approaching it at the bottom vs. starting from the top and building it up.

However, it's better served in smaller, bite-sized components to test the sophistication, integration, and, more importantly, the complexity it will introduce to the end user. Composability is key - while more complex and management intensive, it's also unit-based, can evolve faster over time, and is priced separately.

These capabilities should leverage the evolved RAG future systems, peripheral models and applications, and the strengths of both AWS and Adobe's ecosystems.

Here's an expanded vision for individual tools and services for such a solution:

Integrated Knowledge Management Tools

  • Integration(Core and Peripheral): Use Knowledge-Enhanced Transformers(RAG+LLM) and NLP for Business Intelligence with Automated Machine Learning to create dynamic, self-updating knowledge bases.
  • Functionality: Develop tools that automatically aggregate, organize, and synthesize information from diverse sources into the AWS and AEP platforms, then provide actionable insights for decision-makers. These can be productized for sectors like finance, healthcare, and legal services, where data-driven insights are crucial. Adobe Analytics workspace seems to be doing just that but there are many more that could be added and Adobe can't develop them fast enough.

Advanced Predictive and Prescriptive Analytics Tools

  • Integration(Core and Peripheral): Merge Predictive Analytics and Forecasting with Chain-of-Thought Reasoning and Dense Retrieval Techniques. I have written on this topic. Check out this post and the list below.
  • Functionality: Create tools that predict future trends and recommend actions based on complex simulations of potential outcomes. This could revolutionize the customer lifecycle in marketing and the entire pipeline for the customer within the supply chain, marketing, and strategic planning. Here is an example list that somebody could expand beyond the traditional formats or doormats. Hahaha. While the current analytics tools like Adobe Analytics and Tableau or PowerBI or even AWS Sagemaker and Quicksight will tell you they already do this, that might be true, but it takes an act of god to get value. It must be fixed, especially if you tie it to Chain-of-Thought Reasoning and Dense Retrieval Techniques.
        • Customer Lifetime Value (CLV) Prediction
        • Customer Segmentation
        • Churn Prediction
        • Purchase Probability
        • Product Affinity Analysis
        • Conversion Rate Prediction
        • Campaign Performance Forecast
        • Sentiment Analysis
        • Market Basket Analysis 
        • Propensity modeling
        • Lookalike modeling
        • Data-driven Attribution Modeling 
        • Association Modeling
        • RFM Analysis

Next-Generation Customer Interaction Systems

  • Integration(Core and Peripheral): Integrate Conversational AI and Virtual Assistants with Multimodal Retrieval-Augmented Models and Edge Computing and AI.
  • Functionality: Develop advanced customer service platforms that offer personalized, context-aware interactions across various channels, including voice, text, and video. These systems can significantly enhance customer experience and operational efficiency in retail, banking, and telecommunications.

Secure, Decentralized Data Ecosystems

  • Integration(Core and Peripheral): Pair Blockchain for Data Integrity and Traceability with AI-Driven Cybersecurity and Federated Learning Systems.
  • Functionality: Build secure, decentralized platforms for data sharing and collaboration, ensuring privacy and integrity. These platforms can be vital for healthcare, financial services, and supply chain management, where secure and efficient data exchange is essential. I have a few other ideas here with VR, DID's, and Sovereign IDs. This can also drive relationships in taxonomy and attributes - A global data layer within the enterprise. Kind of like we did for the browser - using blockchain for all taxonomic and semantic ownership. In many cases a GUID is used. It fits with several of the tools below. There are so many wrapped around this idea for the data pipeline and the entirety of the data management stack. The world is wide open with this one.

Personalized Learning and Development Platforms

  • Integration(Core and Peripheral): Merge Few-Shot Learning and Meta-Learning Models with Automated Machine Learning and Natural Language Processing.
  • Functionality: Create adaptive learning platforms that offer personalized education and training programs, adjusting to the learner's progress and needs in real-time. Such platforms can disrupt corporate training, higher education, and professional development markets.

Data Taxonomy Wizard - MY FAVORITE

  • Integration(Core and Peripheral): This approach utilizes NLP, Knowledge-Enriched Transformers, and Interactive Learning Systems to create an educational tool on data taxonomy and semantic schemas.
  • Functionality: This wizard-like tool simplifies learning about data structures and metadata for teams, integrating LMS features and visual aids for easy understanding. It enhances data governance and literacy in AWS and Adobe ecosystems.

Advanced Semantic Engine (Another FAV - builds on above)

  • Integration(Core and Peripheral): Utilize Dense Retrieval Techniques and NLP for Business Intelligence.
  • Functionality: It would enable deep semantic understanding and categorizing data and content across systems. It would facilitate creating and managing semantic taxonomies that reflect the nuances of different business domains.

Dynamic Metadata Management

  • Integration(Core and Peripheral): Leverage Knowledge-Enhanced Transformers and AutoML.
  • Functionality: Automate metadata generation, consolidation, and optimization across data sources and destinations. It includes adapting metadata to changes in business processes and regulatory requirements.

Decentralized Data Governance Framework

  • Integration(Core and Peripheral): Use Blockchain for Data Integrity and Traceability with Federated Learning Systems.
  • Functionality: Ensure data integrity, privacy, and compliance across the data supply chain, enabling secure data sharing and collaboration.

Real-Time Data & Content Integration Hub

  • Integration(Core and Peripheral): Combine Edge Computing and AI with Multimodal Retrieval-Augmented Models.
  • Functionality: Facilitate real-time data and content flows between PIM, warehousing, order management, Commerce, ERP, CRM, and service platforms, ensuring that all systems are updated and synchronized. This happens in the AEM DAM. Adobe might argue that this already exists, but it doesn't, and AI could enhance and improve it.

Do you have thoughts on these and ideas to expand or refine them? 

Would anyone like to hire me to help them build one or more of them?


What is the Collaboration Strategy for the AWS and Adobe Partnership

I could write extensively on this topic alone – the article is already long in the tooth. The obvious are the combined product suites.

AWS Services: Utilize AWS's vast array of services, including Amazon S3 for data storage, AWS Lambda for serverless computing, Amazon SageMaker for machine learning, and AWS IoT Core for real-time data processing.
Adobe Experience Platform (AEP): Leverage AEP's real-time customer data platform, data governance, and intelligent services to manage customer profiles, content, and experiences across channels.

From here, start integrating and do the following, enabling ISV and the joint partnership to become more tightly integrated. They are probably both already well down the road to these aspects within their dual strategy - if not they should consider them.

  • Joint Development and Innovation Labs: Establish collaborative labs focusing on co-developing and testing the individual products or a bundle as a CORE platform, ensuring tight integration between AWS and Adobe technologies.
  • Unified API Framework: Develop a comprehensive set of APIs that enable seamless integration and interoperability between the CORE tool sets above, AWS services, and the Adobe Experience Platform, ensuring that data and content can flow freely and securely across all connected systems. GraphQL might be a good starting point to collapse the REST APIs.
  • Cross-Training and Certification Programs: Implement training programs for developers, data scientists, and business analysts to master the integrated platform, fostering a community of experts and driving adoption.

Market Positioning and Productization

  • Industry-Specific Solutions: Package the CORE tools into solutions tailored for specific industries, such as retail, manufacturing, healthcare, and finance, addressing their unique data and content management challenges.
  • Consulting and Professional Services: Offer consulting services to help businesses implement, customize, and optimize the tools for their specific needs, ensuring they achieve maximum value from their investment.
  • Global Partner Ecosystem: Build a global ecosystem of partners who can extend and enhance the new combined CORE Platform, offering additional modules, integrations, and services to meet businesses' evolving needs.

By combining the advanced capabilities of AWS and Adobe's platforms with the envisioned features of the new combined CORE Platform, businesses can achieve unprecedented levels of efficiency, agility, and innovation in managing their data and content supply chains, driving growth and competitive advantage in the digital age.

Please reach out if you have a job role for me based on what you just read. I am looking for a full-time role anywhere in the world. I will do anything from driving a truck to picking apples to sweeping floors, but I prefer something more challenging in software - Pre-Sales, Post-Sales, Partner Management, Engagement Management, TAM, Solutions Design or Architecture, Solutions Specialist, Product Management, Field CTO, or even R&D Strategy.

Thank you for reading – I am very grateful for your help and support.

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