Boundaryless and the Return on AI Institute (ROAI), experts respectively in platform-product design and artificial intelligence, are collaborating to provide organizations with a comprehensive offering on the intersection of artificial intelligence and platform strategies.

If you are implementing Platforms & AI solutions, contact us to get 360° support. 

We can support your organization or project with:

Learning with Implementation Coaching:

• AI Executive learning program for business leaders – Gamified and customized to client context with implementation coaching
• Platform Exploration and Design training and coaching

Consulting to maximize strategic and tactical return from AI to start or scale AI implementations

• Defining Strategic Intent for AI
• AI Money Map – identify high-value AI use cases specific to the org
• AI Readiness Assessment
• AI Value Enablement
• Algo Audit
• Exploring markets and ecosystems to identify the best growth opportunities and platform strategy models
• Understanding network effects, AI mutual traction, liquidity and go to market tactics
• Design the platform-product and connect it with AI services and features, define the core services bundles and the extensions patterns

Special Projects or needs: custom design to support specific needs, including new edge topics research, and teams or orgs design to enable data mesh structures, AI-DevOps, and more.

Based on our extensive research and experience, we know that when organizations of any kind want to embrace Data Analytics and Artificial Intelligence-powered solutions, they will sooner or later encounter some typical problems or challenges:

• AI Misunderstanding – A lack of enlightenment around what AI can and cannot (or should and should not) actually do, especially at senior levels
• Missing AI Strategy – No specific set and sequence of potential solutions aligned to customer & business needs
• “Business-as-usual” Mentality – Not keeping up with business model innovations and disruptions and business experimentation
• Lack of Data-Driven Culture – Data not treated as a strategic asset – data may be disparate and analytics a minor consideration
• Technical & Data-Product Talent Gap – Ill-defined roles, minimal advanced skills & tooling, and/or siloed technology teams

On the other hand, organizations that are launching and growing platform business models can highly benefit from the adoption of Artificial Intelligence based services and solutions, improving some recurring needs in the ecosystems (examples):

• Orientation among the offering, matching algos, learning which possibilities are reachable through the platform
• Marketing tech, and automation of marketing experiments & strategies
• Categorizing needs and cataloging items, extracting or assigning the right ontology and taxonomy to value items, so as to make the resources searchable and actionable
• Automatization of processes and High Throughput process managers
• Data intelligence, business intelligence, predictive and prescriptive insights generation, and visualization
• Other “deep tech” special needs that are vertical niche/domain dependent, even more based on AI solutions. As examples, computer vision to scan and verify documents, deal with natural language processing to interact with users or transcript audio or visual contents into machine readable ones, etc.

Moreover, there are some interesting overlaps between platform models and organizational design, especially regarding data management and governance, being data fundamental to any AI application.

Those who are familiar with the paradigm of Data Mesh know that as the integration of AI-based services progresses in organizational and business processes, it will be crucial to success to organize teams in such a way that sets them ready to scale fast, guarantee consistency, and quality, and the flexibility necessary to manage data streams coming from – and going to – anywhere.

Entrepreneurs and C-suite managers are required to deeply evolve their organization towards data centric and data product organizations, so they have to:

• Define data product teams and their bounded contexts 
• Identify roles as data stewards and custodians
• Support at the technological level the brokerage, translation, aggregation, and transformation of data raw elements into something consumable as a product

This landscape is a perfect breeding ground for Boundaryless and ROAI, given the multidisciplinary capabilities required: 

• leadership and culture, elevating AI intuition of C-suite 
• identify and design aggregation and network orchestration opportunities, interfaces, team composition, value measurement and attribution, data elaboration best practices, CAPEX and OPEX impacts, business unit relationships. 

As an additional opportunity, organizations and entrepreneurs who are designing and launching AI-based solutions can strongly benefit from the adoption, as a foundational aspect, of platform model key principles.

By their nature, AI models need multi-contexts continuous data streams, need progressive and seamless integration of new features, or modularity and composability of enabling components and services into final solutions at disposal of the end users, serving niche needs and highly personalized outputs.

Everything of the abovementioned finds the perfect application of platform models, born to leverage the ecosystem to extend, integrate, feed platform-enabling and empowering services, and design highly scalable and tailor-made users’ solutions. 

If your project falls under one or more of the mentioned cases, we provide guidance, consulting, and training, especially to C-suite, team leaders, entrepreneurs and professionals, on how to prepare, execute, and scale strategies to integrate and bolster artificial intelligence elements into platform businesses. 

In particular, we offer the following: 

• for AI-driven companies: a comprehensive set of tools and tactics to scale their products and services with diminishing marginal costs by applying Platform-Ecosystem Thinking and designing a platform strategy around the core product, thus planning the reinforcing mechanisms to foster network effects, studying the most favorable go-to-market tactics, understanding liquidity and network properties, and develop platform experiences journeys with value propositions resonating with entities real needs.
• for platform-based companies: tailored and precise tools to equip the team with the knowledge of how and which specific AI can be harnessed to streamline, strengthen and enlarge the company’s competitive advantage. Evaluate the business and organizational patterns to adopt new technological elements, and plan for an adoption and evolution roadmap, also growing the skills necessary to successfully develop these game-changing features. 
• for both types of companies or for those that possess both characteristics: provide expertise to employ data mesh paradigms, and address the needs of AI models by complementing the technology with the right organizational asset, introducing data product teams, test and learn mindset, bounded contexts to define homogeneous taxonomy and modular, program management, cultural transformation, composable interfaces between teams, departments and also towards the ecosystem. Also, the data management roles – data steward, custodian, data product owners – can also be assigned to external independent teams in the ecosystem, adopting a platform organizational model, based on Boundaryless’ 3EO model, helpful to scale fast and efficiently.