Framework

The Hard Job of Driving Value from AI

| Charlie Ranalli

Generative AI arrived quickly, with bold promises dominating headlines. But anyone who has lived through past technology shifts knows the real story: the hard job of driving value is not proving it can work – it is adopting it to transform how companies work. Just as with PCs, e-commerce, or mobile, impact will not be fast or easy.

Customers, colleagues, and friends — nearly everyone I exchange perspectives with is already using GenAI to work faster, smarter, and better. That matches findings from a recent paper by Andrew McAfee and colleagues, which shows the technology delivering rapid productivity gains across many occupations. (And yes, I used GenAI tools in researching this article.)

The challenge is that individual gains – especially in knowledge-based work – don’t automatically scale into business or economic impact, at least in most sectors. To matter, they must flow into optimized processes, unit economics, and value levers like prices, margins, or productivity in high-volume workloads. As McAfee and his co-authors note, that means “complementary innovations and organizational reinvention.”

The good news is we finally have some evidence to work with. Early experiments show where AI can create traction and where it tends to stall. They also make one thing clear: getting value out of AI means tough choices and visible commitment from the top team.

The Road to Structural Gains

Across industries, AI pilots are everywhere – copilots for coders, chatbots for service, tools to automate routine tasks. They show what’s possible, but most remain stuck at the pilot stage. Local productivity improves, yet the broader structure of how work gets done stays the same.

This is familiar. Every major technology shift follows a pattern: early enthusiasm, a rush of experimentation, and only later the hard work of embedding new tools into the fabric of business.

The PC revolution is a widely researched case. Email, spreadsheets, and collaboration tools became available in the 1980s. Yet measurable productivity gains only showed up in the mid-1990s, once workflows were redesigned, people retrained, and cultures changed. It took 15 years before the technology showed up in macroeconomic statistics.

E-commerce followed a similar path. Despite 30 years of innovation, investment, and exceptional consumer convenience, only about 16% of U.S. retail spending is online today.

Gartner’s 2025 Hype Cycle places generative AI just past the Peak of Inflated Expectations, signaling that today’s surge of pilots and proofs of concept will only translate into value once organizations take on the harder work of reinvention.

Generative AI is best understood as a general-purpose technology, much like electricity, PCs, or the internet: broad in potential, but dependent on complementary change before its impact can be fully realized. Research by Andrew McAfee and colleagues suggests this wave could diffuse faster than earlier ones because the infrastructure is already in place, tools are widely accessible, and adoption often requires less retraining. Yet the speed of progress still depends heavily on an organization’s own maturity – some have ways of working and modern tech stacks ready to absorb AI, while others face years of groundwork before they can capture meaningful value.

Early signals remain ambiguous. A Stanford study found that workers aged 22–25 in AI-exposed roles saw employment fall by about 13% since 2022, while more experienced peers held steady – suggesting the first effects of AI might be showing up in hiring rather than productivity. A more recent analysis from Yale’s Budget Lab offers a different lens: instead of losses, it examines shifts in the occupational mix – which tasks are being automated, which are being augmented, and how work itself is changing as AI spreads. Apparent declines in some roles may instead reflect a reallocation of tasks. The evidence remains faint — more about changing work than vanishing jobs.

As a technology executive and entrepreneur who lived through the dot-com wave, the rise of e-commerce, mobile, and social media, I’ve seen this pattern repeat: first the hype, a dose of disillusionment, and finally the long, grinding work of structural change. Generative AI may spread faster than past waves, but the hard job to generate value will be the same.

Why AI Value Will Be Slow to Materialize

Integration Matters More than Deployment

Customer service bots, document summarizers, and coding copilots are easy to deploy. They spread fast but don’t transform most industries. The next step is Agentic AI – systems that combine business logic and different types of AI to perform multi-step workflows, interact with humans, and span business functions.

The challenge is integration. Agents don’t operate in isolation. They need redesigned interfaces and workflows: decision points, exception handling, escalation rules, and system integration. As McKinsey observed after studying 50+ agentic AI builds: “It’s not about the agent, it’s about the workflow.”

“Fewer than 10% of generative AI use cases have made it past the pilot stage.”
“Only 21% of organizations using gen AI report that they have fundamentally redesigned at least some workflows.”
“Among the 25 organizational practices tested, workflow redesign has the strongest relationship with reported EBIT impact.”
– McKinsey, State of AI: How Organizations Are Rewiring to Capture Value (2025)

Reengineering Operating Models

Some industries will see direct disruption – translation, for example, where AI can deliver acceptable quality at near-zero cost. But most require operating model reinvention: reconfiguring how decisions are made, how accountability flows, and how value is created.

McKinsey’s State of AI 2025 survey illustrates the gap: “Fewer than 10% of generative AI use cases have made it past the pilot stage.” Tools are spreading, but few firms have revamped their operating models to absorb them.

No Sustainable Edge in Base Technology

Generative AI is trained on public domain knowledge and released broadly. As MIT Sloan has argued, AI itself is unlikely to provide sustainable competitive advantage.

McKinsey’s survey is blunt: “Only 21% of organizations using gen AI report that they have fundamentally redesigned at least some workflows. Among the 25 organizational practices tested, workflow redesign has the strongest relationship with reported EBIT impact.”

The edge comes from differentiated data, proprietary processes, and organizational creativity. Cloud platforms can be acquired easily; unique ways of working cannot.

The Human Factor

AI changes organizational dynamics. Vertical supervising tasks give way to orchestrating systems of people and agents. Leaders must re-skill employees, manage resistance, and build trust in AI-driven systems.

Human-in-the-loop design is critical. Generative AI introduces risks of hallucinations, bias, and IP leakage that can’t be solved by technology alone. In a Harvard Business Review article, Andrew McAfee and his co-authors argue that employees need to be trained to recognize these risks, escalate them, and build confidence in using AI responsibly.

And it’s not only frontline or entry-level roles. A recent MIT Sloan article argues that AI will increasingly automate coordination and monitoring – traditional managerial functions – while creating demand for new roles in oversight, design, and orchestration.

Adoption will be uneven. Early enthusiasts embrace new tools; skeptics push back, especially after errors. Clear accountability, transparent communication, and cultural adaptation are as critical as technical readiness.

Platform Readiness: Data, Systems, and Governance

Finally, structural adoption depends on platform readiness. AI requires clean, governed data — still a rarity in most organizations. Just as critical is the underlying architecture: many ERP, CRM, and supply chain systems are built on rigid interfaces and hard-coded business logic. To take advantage of AI, those systems need more flexible designs that can support dynamic workflows. AI can’t be simply bolted onto legacy systems.

Governance must also mature alongside technology. Privacy, compliance, and risk management can’t wait until systems are scaled if they are exposed to the outside world – they need to be embedded from the start.

The Productivity Promise Is Still Real

Despite these challenges, the long-term upside is enormous. McKinsey estimates that Agentic AI could unlock $450–650 billion annually by 2030 in advanced industries such as manufacturing, logistics, and energy.

Functions like supply chain, software development, and customer service can all be reimagined. But because the base technology is broadly available, the competition to innovate will be fierce. The winners will be those that integrate faster and deeper, building on proprietary assets.

From my experience with past technology shifts, this is where the champions pull ahead. When mobile and social networks disrupted consumer engagement, the companies that succeeded weren’t the ones with the flashiest apps – they were the ones that cleverly reimagined marketing, sales, and service for two-way, mobile-first interactions. The same will be true with AI.

From Experimentation to Transformation

The practices below reflect what I’ve seen in working with clients, along with insights from global leaders and academics. Together, they highlight what helps organizations move beyond experimentation toward real transformation.

Activate the Leadership Team

Transformation begins at the top. This is not just a technology project but a significant change effort. The senior team has the credibility to ask the hard questions, the clout to mobilize resources quickly, and the leverage to remove obstacles. If they aren’t in the room and visibly committed, the rest of the organization will sense it – and resistance will dampen progress.

Map Opportunities

With customer teams, we start broad and deep. The goal isn’t just to identify obvious efficiencies, but to uncover disruptive opportunities. We map ideas across two dimensions: strategic business pillars, and the angles of innovation that AI can unlock.

This stage is about thinking differently – exploring what could change the rules of the game rather than just automate today’s tasks. It’s a step for conceiving opportunities, not a commitment to chase them all.

Invest in a Balanced Portfolio

Once opportunities are conceived, the next step is to commit to a portfolio that balances ambition with pragmatism. In practice, the portfolios that resonate most with leadership teams usually contain three kinds of bets: disruptive ideas that carry risk but could redefine value creation; quick wins that deliver visible results and momentum; and defensive moves that may not create lasting advantage but are quickly becoming industry baseline.

Start with the Questions

AI is not an end in itself. The right starting point is a business question: If I could forecast X more accurately, what would change? If I could automate Y, how much capacity would I free up?

One industrial client is embedding AI agents into a reactive B2B service chatbot. The initial aim is faster response, but the next goal is to extend into proactive commercial processes – identifying cross-sell opportunities, guiding orders, anticipating needs. The key question wasn’t an open-ended “what can AI do,” but “can AI turn knowledge into growth while managing risk up front?”

Advance Initiatives and Capabilities

Run promising use cases first but do so with discipline: set substantial business goals, track adoption and value, and be ready to kill experiments that don’t deliver. In parallel, invest in the capabilities that enable scaling: data assets, governance, orchestration technology, and workforce skills.

One of my retail clients launched a Copilot program to organize and classify knowledge. The first phase focuses on individual and small team productivity, but the explicit goal is to mature into AI-enabled collaborative innovation. Sequencing matters: early initiatives build experience and momentum, while capabilities ensure the organization is ready to accelerate what works.

Close Business, Technology, and Risk Collaboration

AI can’t sit in a technical silo. Business leaders must co-own initiatives. To build trust and ensure each step forward stands on firmer ground, risk and compliance need to be involved from the start. Governance has to be continuous, with clear paths for when agents fail.

One useful approach, highlighted in HBR and practiced by most early adopters, is to move deliberately in stages: experiment in a sandbox, then run tightly scoped pilots, and only scale once risk and governance structures are proven to hold.

The Case for Simplicity

When teams start mapping AI opportunities, the challenge isn’t scarcity – it’s abundance. The possibilities are endless, and the actions required to pursue them quickly become complex and interdependent. Without a simple top-level frame, discussions can fragment across business, operations and technology perspectives.

A practical way to bring structure is to group AI applications into broad categories, for example:

Predictive & Optimization – improving foresight and decision quality through analytics and modeling.

Autonomous Decision & Action – embedding intelligence into processes that can sense and act in real time.

Knowledge-Management & Generative – expertise, content, and amplifying human capability.

End-to-End – connecting and orchestrating workflows across functions to create compound value.

Illustrative opportunity matrix. Sanitized from project material to show structure, not content.

This structure, borrowed from a recent workshop, isn’t meant to be followed verbatim – it must be reframed based on each organization’s industry and AI posture, and paired with strategic pillars to give a full 360-degree view. It simply shows how to organize a portfolio around general concepts rather than specific technologies or use cases.

The Takeaway

AI opens unprecedented opportunities but is not a silver bullet. Pilots and use cases are necessary, but they are not sufficient. Like PCs, e-commerce, and mobile before it, AI’s real value will only come when organizations reinvent their structures to embed it deeply in business and operating models.

The winners won’t be those who run the most experiments. They will be the organizations willing to rewire themselves so that AI becomes part of the fabric of how business is done. For leaders, that means rolling up your sleeves and doing the hard job of transformation.

References

The impact of generative AI as a general-purpose technology
MIT Sloan School of Management, 2024
https://mitsloan.mit.edu/ideas-made-to-matter/impact-generative-ai-a-general-purpose-technology

Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence
Stanford Digital Economy Lab, 2025
https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/

Evaluating the Impact of AI on the Labor Market: The Current State of Affairs
Yale Budget Lab, Yale University, 2025
https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs

One Year of Agentic AI: Six Lessons from the People Doing the Work
McKinsey & Company, 2024
https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work

The State of AI: How Organizations Are Rewiring to Capture Value
McKinsey & Company, 2025
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Why AI Will Not Provide Sustainable Competitive Advantage
MIT Sloan Management Review, 2025
https://sloanreview.mit.edu/article/why-ai-will-not-provide-sustainable-competitive-advantage/

How to Capitalize on Generative AI
Harvard Business Review, 2023
https://hbr.org/2023/11/how-to-capitalize-on-generative-ai

Why Robots Will Displace Managers – and Create Other Jobs
MIT Sloan Management Review, 2025
https://sloanreview.mit.edu/article/why-robots-will-displace-managers-and-create-other-jobs/

The Economic Potential of Generative AI: The Next Productivity Frontier
McKinsey & Company, 2023
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

Building a Digital Business Platform with Microsoft Tech

| Charlie Ranalli

Key Concepts

  • Microsoft’s digital business platform vision has been developed over time, combining modular business applications, flexible infrastructure, and structurally embedded AI.
  • Dynamics 365 provides a composable architecture at multiple levels: business domain modularity, application modularity, and separation of data, logic, and user interfaces.
  • Dataverse and Microsoft Purview deliver a unified operational data layer and governance framework for data quality, consistency, and compliance.
  • Microsoft Fabric integrates real-time analytics, operational reporting, and AI-driven insights into a unified environment, enabling continuous intelligence.
  • Power Platform enables low/no-code development, extending business applications and workflows.
  • Azure platform services – hosting, identity governance with Microsoft Entra, CD/CI with Azure DevOps and monitoring with Azure Monitor.
  • Multi-vendor platform with systems like SAP S/4HANA can be integrated through API-first models, identity federation, DevOps pipelines, and unified monitoring.

This article follows on from Digital Business Platform: A Reference Architecture to Accelerate Digital Metabolism, moving from architectural principles into practical implementation and outlines how Microsoft’s technology ecosystem –Dynamics 365, Azure, and Microsoft Fabric – can be engineered to realize a composable, scalable digital business platform.

Microsoft’s vision has developed over time, shaped by three complementary dimensions that reflect a long-term platform strategy under the leadership of Satya Nadella: business applications as a platform, elastic cloud infrastructure as an enabler, and Data and AI as a ubiquitous, embedded capability across the stack.

Microsoft continues to develop its technologies with a platform-oriented mindset – supporting open standards, architectural extensibility, and modular design. This guiding principle is reflected in recent developments, like the announced support for Model Context Protocol (MCP) and Open Agent2Agent (A2A) standards originally created by Anthropic and Google respectively.

A Platform Approach to Business Applications

In the mid 2000s, Microsoft Business Solutions (now Microsoft Dynamics) was formed with a clear architectural ambition: business applications needed to be modular, extensible, and designed as a platform. One of the most consequential moves was the acquisition of Navision, whose Axapta product – later Dynamics AX and eventually Dynamics 365 – was built with distinct data, business logic, and UI layers. This architecture predates composability principles but laid the foundation for a suite of modular applications spanning finance, operations, sales, marketing, and service.

Azure: Cloud Infrastructure

In parallel, Microsoft built Azure as a global infrastructure-as-a-service (IaaS) platform to support this modular vision. More than a hosting environment, Azure was designed to be open and interoperable, capable of supporting Microsoft-native, open-source, and third-party technologies. It includes all foundational elements of a digital business platform – identity, security, integration, observability, and operations – and acts as the connective tissue that links business applications, analytics engines, and operational systems into a cohesive whole.

Azure also enables modern software engineering practices. Team Foundation Server (TFS), launched in 2005, introduced early CI/CD capabilities. It eventually evolved into Azure DevOps, which today supports high-velocity, modular, distributed delivery pipelines at scale.

AI: Intelligence Embedded Across the Platform

The third dimension – artificial intelligence – has increasingly become an integral layer within Microsoft’s platform strategy. Far from being treated as a separate system, AI is embedded into applications, development tools, and infrastructure services. Azure AI and Microsoft Copilot technologies exemplify this convergence: enabling natural language interaction, automating complex workflows, enriching analytics, and accelerating software development itself. AI is not simply layered on top of the platform – it is woven through it, transforming how capabilities are delivered, consumed, and scaled.

Implementation Blueprint

A digital business platform is not a single product or solution, but an architectural model for assembling modular technologies into a unified, scalable foundation to manage the infrastructure. It integrates core business applications, shared data services, analytics and AI engines, low-code tools, and modern infrastructure under a common governance model. The goal is to create a composable environment where capabilities can be deployed, extended, and reconfigured dynamically in response to business needs.

This blueprint outlines how Microsoft’s ecosystem – Dynamics 365, Azure, Microsoft Fabric, and Power Platform – can be engineered to implement a full-scale digital business platform. While based on Microsoft technologies, the architecture is vendor-neutral by design, enabling the structural adoption of systems such as SAP as first-class components. The document is structured around five layers: business applications, data services, analytics and AI, low-code, and platform management, and an extension exploring multi-vendor architecture.

1) Composable Business Architecture

True composability is achieved when business capabilities are modularized at different layers of the application and data stack. Microsoft’s modern business application ecosystem realizes this principle through three nested forms of modularity:

Macro Modularity: Independent Applications by Business Domain

At the highest level, Dynamics 365 offers a set of independent but interoperable applications organized around major business domains – commerce, finance, supply chain management, customer service, etc.

Credit: Microsoft Dynamics 365 Commerce Component Overview, © Microsoft Corporation

Each application is built to be deployable individually or in combination with others, enabling organizations to adopt and expand their platform gradually, based on business needs. This modular application approach allows organizations to compose and recompose their digital operations as market conditions and strategies evolve.

Application Modularity: The Case of Dynamics 365 Commerce

Zooming in, each Dynamics 365 application itself is designed with modularity in mind. Dynamics 365 Commerce, for example, structures its internal architecture to support composability across retail channels, back-office operations, call centers, and e-commerce sites. Core subsystems – such as the Retail Server, Channel Database, and Commerce Scale Unit – are modular components that can be configured, scaled and extended independently, depending on channel-specific needs.

Credit: Microsoft Dynamics 365 Commerce Headless Architecture, © Microsoft Corporation

This modular design allows for greater operational flexibility and agility, enabling rapid rollout of new experiences across physical and digital storefronts without massive reengineering of backend systems.

Data, Logic, and UI Separation: Headless Engineering

The separation of data, business logic, and user interface is a fundamental modularity principle implemented in Dynamics 365 Commerce’s headless commerce model. In this design:

  • Commerce APIs expose business logic and data independently of any specific user interface.
  • Retail Server serves as an orchestration layer between front-end applications and core business services.
  • Channel Databases and Commerce Runtime (CRT) services maintain data and transactional integrity across retail stores and e-commerce channels.

This separation enables unique customer experiences across channels and devices while relying on consistent, reusable business processes underneath. Custom front-end applications (for example embedded in a branded mobile app), third-party platforms, and Microsoft Power Platform connectors can all consume these APIs to build tailored solutions, enabling a true omnichannel strategy.

API-First Integration: Building for Composability

An API-first integration approach guides all layers of modularity. Dynamics 365 Commerce APIs, delivered via OData protocols and Web API standards, offer standardized, discoverable interfaces that support both first-party (Microsoft) and third-party application development.

This model-driven, API-centric design:

  • Decouples service consumers from service implementations
  • Enables external systems to access business services cleanly and securely
  • Facilitates agility and scalability in integrating new applications, devices, or platforms

Support for industry standards like OData (an OASIS Open Standard) ensures that integration approaches are interoperable across ecosystems, whether consuming APIs directly from Dynamics 365 applications or via connectors.

2) Data Services

The digital business platform requires a unified data service capable of supporting operational transactions, integrating across applications, and providing governance across environments. Microsoft’s Dataverse, combined with Microsoft Purview and Azure integration services, addresses these needs within a modular and standardized framework.

Dataverse: Operational Data Layer

Dataverse provides a structured and secure environment to manage operational data models across business applications. Key capabilities include:

  • A standardized schema for common business entities (customers, products, transactions)
  • Support for role-based access control and security policies
  • Metadata services for defining validations, relationships, and business rules
  • Native integration points with Dynamics 365, Microsoft 365, Azure services, and the Power Platform ecosystem

Dataverse abstracts storage complexity, allowing applications to interact with business data through a consistent and scalable service layer.

Data Governance: Microsoft Purview Integration

Microsoft Purview extends governance capabilities across Dataverse and connected data sources:

  • Register and scan environments to catalog assets and track data lineage
  • Define and enforce data policies, classifications, and retention strategies
  • Manage compliance and data protection across multi-cloud, on-premises, and SaaS environments

Purview standardizes governance processes, supporting auditability and operational compliance without duplicating management efforts.

Data Integration and Interoperability

Dataverse supports integration through an OData-compliant API surface, providing a standardized REST-based protocol for external and internal service communication:

  • Direct exposure of entity data models for programmatic access
  • Compatibility with OData-enabled platforms like SAP Gateway
  • Integration with external services via standardized connectors, such as Informatica’s OData Connector

This architecture supports loose coupling between systems, enabling dynamic integration patterns across operational and analytical workflows without requiring bespoke adapters or point-to-point configurations.

3) Analytics, AI, and Data-Driven Workstreams

A digital business platform must integrate operational and analytical layers to enable data-driven processes across the enterprise. Microsoft Fabric provides an end-to-end environment for integrating, managing, analyzing, and visualizing data, connecting directly with operational sources such as Dataverse and Dynamics 365.

Fabric as the End-to-End Analytics Platform

Microsoft Fabric consolidates data integration, engineering, data science, real-time analytics, and business intelligence into a unified SaaS-based architecture with:

  • Centralized data storage and processing across data lakes, warehouses, and real-time engines
  • Native integration with Power BI for operational and strategic reporting
  • Role-based workspaces supporting both IT-driven and business-user-driven analytics

Fabric’s architecture aligns with composability principles by allowing businesses to manage diverse analytics workloads through modular, interconnected services, reducing redundancy and complexity.

Real-Time Operational Analytics: Azure Synapse Link for Dataverse

Azure Synapse Link enables near real-time replication of Dataverse data into Fabric environments without requiring complex ETL.

  • Changes in Dataverse are automatically synchronized to Fabric, enabling up-to-date analytics without impacting transactional systems.
  • Data remains available for advanced modeling, dashboarding, and real-time insight generation through Power BI and other Fabric services.
  • Insights can be operationalized by surfacing recommendations within Dynamics 365 workflows, enabling closed-loop decisioning between data and action.

This architecture supports continuous, data-driven operations while minimizing the latency and complexity traditionally associated with analytical data flows.

Data Integration for Analytics Workflows

Fabric extends its operational analytics capabilities through Data Factory pipelines, supporting over 200 prebuilt connectors to integrate data from internal systems, cloud platforms, and external providers.

  • Ingestion pipelines enable hybrid analytics scenarios combining Dataverse operational data, ERP historical data, and external market or partner data.
  • Data transformations and enrichment processes can be managed within Fabric workspaces, supporting advanced analytics, machine learning, and AI-driven models.
  • Open-standard integration protocols and connectors ensure interoperability with enterprise platforms such as SAP, Salesforce, and others.

Standardizing ingestion and transformation enables the creation of scalable, reliable analytics environments tightly coupled with operational systems.

A Connected Analytics and Operations Loop

The combination of Dynamics 365, Dataverse, Azure Synapse Link, and Fabric enables a fully connected operational and analytical architecture. Data is captured through business applications, replicated and analyzed in near real time, and operationalized back into workflows and decision points. This model supports not only reporting and dashboarding, but also the development of AI-augmented processes, predictive insights, and autonomous operations, consistent with modern digital business platform principles.

4) AI integration + Low-Code/No-Code

Extending the digital business platform requires a structured approach to building new capabilities with minimal overhead, maintaining alignment with core operational systems, and avoiding complex and risky customizations that increase technical debt.

Microsoft’s Power Platform is the unified low-code/no-code environment designed to accelerate solution development, automate processes, and broaden participation in digital innovation.

Power Platform: Unified Low-Code/No-Code Environment

The Power Platform suite combines tools for application development, process automation, data analysis, and AI. Core components include:

  • Power Apps for custom business application development
  • Power Automate for workflow automation
  • Power Pages for secure external-facing portals
  • Copilot Studio for building conversational AI agents

Native integration with Dynamics 365 and Dataverse ensures that applications and workflows built within Power Platform are compatible with existing security models, operational datasets, and compliance frameworks.

Extending Line-of-Business Systems: Power Apps

Power Apps enables the development of custom applications that connect directly to Dataverse and Dynamics 365 data models. Applications can be designed by both professional developers and citizen developers, supporting a range of use cases from simple data capture forms to complex business process extensions. Prebuilt templates, reusable components, and connectors accelerate the development of solutions that extend ERP, CRM, and operational processes, enabling rapid adaptation without compromising system integrity.

Process Automation: Power Automate

Power Automate facilitates the automation of business processes by connecting applications and services through configurable workflows.

  • Supports event-driven, scheduled, and manually triggered workflows
  • Integrates with a broad range of Microsoft and third-party services through standard connectors
  • Enables automation of approvals, notifications, data synchronization, and task management based on business events

Workflows developed in Power Automate can interact with both operational systems (e.g., Dynamics 365) and external services, improving process efficiency and reducing manual effort.

AI-Enabled Agents: Copilot Studio

Copilot Studio allows the creation of conversational agents and automated assistants that interact with users across multiple channels, including web, mobile, and messaging platforms.

  • Agents can connect to Dataverse, APIs, and external services to retrieve and process information
  • Supports guided conversations, knowledge base retrieval, and action execution
  • Enables organizations to augment traditional applications with natural language interaction and AI-driven automation

By structurally and natively embedding AI agents into business workflows, organizations can streamline support processes, enhance customer engagement, and introduce new operational models.

Extending Composability Through Citizen Development

Power Platform extends the composable architecture of the digital business platform by enabling faster development cycles, broader participation in innovation initiatives, and the embedding of automation and AI into business processes. Structured governance, standardized connectors, and integration with Dataverse ensure that low-code solutions maintain alignment with enterprise operational and compliance standards.

5) Centralized Management, Provisioning, and Delivery

Hosting, identity governance, integration orchestration, software delivery, and monitoring must all align with the same modularity principles to ensure a structured, scalable approach to operational management and agile delivery. Azure provides a comprehensive set of services to standardize and streamline management, provisioning, and delivery across the platform architecture.

Hybrid Hosting and Platform Services: Azure Core Services

Azure supports flexible hosting models, including public cloud, private cloud, and hybrid deployments. Core services for compute, storage, networking, containers, and Kubernetes orchestration enable IT teams to optimize workloads based on performance, scalability, and security requirements. Modern provisioning patterns such as Infrastructure-as-Code can elastically scale resources, maintain consistent configurations, and enforce operational policies across environments.

Identity and Access Management: Microsoft Entra

Microsoft Entra provides a comprehensive suite of services for identity governance, authentication, conditional access, and policy enforcement across cloud and hybrid environments. Through Entra, organizations can implement Zero Trust principles by ensuring that every user, device, and service is authenticated, authorized, and continuously validated. Identity federation and role-based access control further simplify managing access across Dynamics 365, Power Platform, Azure services, and third-party applications, ensuring security and compliance at scale.

Software Delivery and ITOps: Azure DevOps

Efficient software delivery is essential to sustaining a modular platform. Azure DevOps delivers an integrated toolchain for source control, build automation, continuous integration, continuous delivery, and release management. Standardizing deployment pipelines across application modules and infrastructure components ensures consistent, repeatable, and auditable delivery processes. Integration with Microsoft services, open-source tools, and third-party systems enables end-to-end automation, supporting agile development methodologies and DevOps practices across the digital business platform.

Monitoring and Observability: Azure Monitor

Azure Monitor provides centralized telemetry collection, log analytics, metrics tracking, and alerting across applications, infrastructure, and services. Azure Monitor supports real-time health dashboards, implement predictive analytics for operational tuning, and automate incident response workflows. Unified observability accelerates troubleshooting, enhances performance optimization, and supports a proactive operations model that aligns with modern platform management practices.

Multi-Vendor Platform: SAP Technologies as an Example

While this blueprint is coiceived using Microsoft technologies, the architectural model is intentionally vendor-neutral. A digital business platform must be designed to support the structural adoption of modules from different enterprise vendors – not simply integrate with them.

Microsoft’s open ecosystem philosophy, exemplified by its support for Linux, Kubernetes, and multi-cloud patterns, extends to core enterprise platforms such as SAP. In this context, SAP is not treated as an external system to be interfaced, but as a foundational participant in the composable architecture – capable of occupying critical roles in finance, supply chain, and operations within a unified platform model.

S/4HANA workloads on Azure

Microsoft Azure offers certified reference architectures for deploying SAP S/4HANA, accommodating various deployment options, including public cloud, private environments, and hybrid models, providing flexibility in adopting or modernizing SAP’s core modules.

Implementation strategies include traditional lift-and-shift migrations of ECC or S/4HANA instances, as well as fully re-architected, cloud-native implementations aligned with SAP’s clean core principles. Azure supports these approaches with elastic scalability, high availability, and integrated disaster recovery capabilities, utilizing SAP-certified virtual machines, storage configurations, and networking patterns.

In a composable platform architecture, SAP workloads can be integrated as interoperable modules within the broader system. Running S/4HANA on Azure facilitates participation in shared identity frameworks, such as Azure Entra (formerly know as Active Directory), observability services like Azure Monitor, DevOps pipelines through Azure DevOps, and data integration layers alongside Microsoft-native components. This integration ensures that SAP functions cohesively within a unified digital business platform.

Integration Patterns: API-First and Data Exchange

SAP Gateway enables exposure of SAP business logic and data models as OData-compliant services, facilitating API-based integration with Microsoft services and external applications.
Azure Integration Services, combined with Service Bus, provides a middleware layer for:

  • Event-driven integration between SAP and Dynamics 365 applications
  • Data ingestion and transformation pipelines into Microsoft Fabric for unified analytics
  • Federation of SAP operational data with Dataverse and Power Platform applications

These integration patterns allow SAP systems to participate in hybrid operational workflows and contribute data into broader data-driven processes across the digital business platform.

Identity and Access Governance Across Systems

Microsoft Entra extends centralized identity governance to SAP systems, enabling:

  • Synchronization of SAP user roles and permissions with Azure Active Directory
  • Unified authentication and conditional access across SAP and Microsoft environments
  • Consistent policy enforcement and compliance reporting

Support of Zero Trust security principles simplifies access management across heterogeneous platforms.

Deployment and DevOps Integration

SAP environments can be deployed and managed on Azure using the SAP Deployment Automation Framework, which automates infrastructure provisioning, installation, and configuration processes. DevOps practices can also be applied to SAP application development, particularly in SAPUI5 and Fiori projects. Azure DevOps can orchestrate source control, continuous integration, and release management for SAP artifacts.

Monitoring and Observability: Azure Monitor for SAP Solutions

Azure Monitor provides dedicated services for SAP observability, including telemetry collection across:

  • SAP infrastructure (compute, storage, network)
  • Databases (HANA, AnyDB)
  • SAP application layers

Unified monitoring enables proactive incident detection, performance optimization, and integrated reporting across SAP modules and broader platform components.

Conclusion

Implementing a digital business platform requires a methodical approach to modularity across applications, data services, analytics, low-code development, operational management, and integration frameworks. Microsoft’s ecosystem – Dynamics 365, Azure, Microsoft Fabric, and Power Platform – provides a comprehensive and interoperable foundation aligned with these principles, enabling organizations to modernize systematically while managing operational complexity.

The platform architecture is inherently extensible. It supports not only Microsoft-native applications but also the integration of third-party and custom-built solutions. SAP was presented as a reference model to illustrate coexistence, interoperability, and operational governance; however, the integration patterns apply broadly to other enterprise platforms and ecosystems.

References

Composable business architecture
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/commerce-architecture
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/retail-server-architecture
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/crt-services
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/define-retail-channel-communications-cdx
https://learn.microsoft.com/en-us/dynamics365/commerce/dev-itpro/consume-retail-server-api

API/Model Driven App
https://learn.microsoft.com/en-us/power-apps/developer/data-platform/webapi/overview
https://learn.microsoft.com/en-us/power-apps/developer/data-platform/overview
https://www.oasis-open.org/standards/

Data services
https://www.microsoft.com/en-us/power-platform/dataverse
https://www.microsoft.com/en-us/security/business/microsoft-purview
https://www.microsoft.com/en-us/security/business/risk-management/microsoft-purview-data-governance
https://learn.microsoft.com/en-us/purview/register-scan-dataverse
https://www.microsoft.com/en-us/power-platform/blog/power-apps/govern-your-business-applications-data-with-microsoft-purview/

Data integration
https://www.odata.org/ecosystem/
https://marketplace.informatica.com/listings/cloud/connectors/odata_connector.html
https://pages.community.sap.com/topics/gateway

Analytics + AI
https://www.microsoft.com/en-us/microsoft-fabric
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/azure-synapse-link-view-in-fabric
https://learn.microsoft.com/en-us/%20fabric/data-factory/connector-overview

Data driven
https://www.microsoft.com/en-us/power-platform
https://www.microsoft.com/en-us/power-platform/products/power-apps
https://www.microsoft.com/en-us/power-platform/solutions/extend-lob-systems
https://www.microsoft.com/en-us/power-platform/products/power-automate
https://www.microsoft.com/en-us/copilot/microsoft-copilot-studio Agents

Platform services
https://azure.microsoft.com/en-us/products

Digital Business Platform

Digital business platform
| Charlie Ranalli

A Reference Architecture to Accelerate Digital Metabolism

Key Concepts

  • Why it matters: overcome technical rigidity to accelerate innovation and growth
  • Architecture + infrastructure: combines design and management for flexibility and agility
  • Composable business architecture: modular capabilities enable adaptable and flexible operations
  • Data services: unified, high-performance data store for master and operational data
  • Analytics and AI integration: real-time insights “loops” for data-driven operational workflows
  • Low/no-code platform: tools for rapid innovation through citizen-developed apps
  • Centralized management and DevOps: standardized provisioning and delivery across the platform
  • Technology selection: balance best-of-breed solutions with seamless interoperability for agility
  • Simplicity as a mindset: streamlined, standardized design enhances efficiency

Breaking Free from Legacy Architectures

All the organizations we’ve worked with over the past few years to develop or accelerate their digital strategies faced technical debt in their tech stack, the product of legacy architectures, amorphous cloud migrations and complex operations practices. This debt hampered their “digital metabolism”—the speed and agility with which they could innovate, adapt, and transform digitally.

Some signs of this debt are easy to spot: outdated legacy applications, siloed business processes, point-to-point integrations, and inaccessible or poor-quality data. Others are more subtle and require a more ambitious aim, like modern data governance, DevOps, and release management practices—capabilities that digital leaders have mastered. (As a side note: coming from the technology industry certainly helps some of us identify modern engineering practices that can be adapted and applied broadly.)

Technical debt slows down new projects, drives up costs, limits data accessibility and usability, and often results in makeshift attempts at digitization and automation. These fragmented approaches compound the “digital spaghetti” problem: a tangled clutter of overlapping processes, apps, technologies, and data flows. The result? Slower innovation, higher risks, and in some cases, stalled digital transformations.

Technical debt is expensive: according to McKinsey & Co., “Some 30 percent of CIOs we surveyed believe that more than 20 percent of their technical budget ostensibly dedicated to new products is diverted to resolving issues related to tech debt.”

For years, business and technical thought leaders have been sounding the alarm and proposing solutions, including composable architectures, planned cloud migration, application refactoring, and data modernization. Until recently, these ideas seemed futuristic or, at best, targeted efforts to address isolated problems.

The good news is that advances in off-the-shelf applications, cloud platforms, integration technologies, engineering practices and industry standards have made holistic solutions more achievable, with surprisingly lower costs and risks than before.

This article introduces the concept of a digital business platform, bridging the gap between a reference architecture and operations framework to help organizations approach the management of their technology ecosystem holistically to break free from innovation-limiting legacy infrastructures and address technical debt. These design and management principles –curated from real-world cases– provide a structured, methodical approach to building an agile, scalable infrastructure.

Key Components of a Digital Business Platform

1) Composable Business Architecture

At the core of the digital business platform is a modular grid of business capabilities (marketing, sales, production, logistics and the like) implemented in applications –or, in some cases, custom-built software– and wired together in macro-processes to support the desired business models, operating processes, customer journeys and employee experiences.

Gartner’s concept of composability is a robust framework for designing this architecture. Just a few years ago, implementing this approach was difficult for most organizations due to the limited modularity and flexibility of off-the-shelf solutions. Today, with the latest omnichannel, customer relationship management (CRM), supply chain, digital banking, and financial management solutions, composability is much more attainable and affordable.

To maximize flexibility, reusability, and long-term value, discrete business capabilities and connected end-to-end scenarios should be managed as digital products—designed for continuous evolution, adaptation and reuse across projects, businesses and geographies.

2) Data Services

In the client-server era, data architecture was relatively straightforward: data resided in centralized databases, neatly structured and managed to support business applications, maybe with a universal integration bus orchestrating flows and transactions. Analytics platforms accessed, processed and organized this data for visualization tools, advanced models for decision making and, in some cases, feed augmented insights into the centralized stores.

SaaS platforms changed all this, offering elasticity, lower costs of ownership, faster implementation, and reduced risk, but obscuring data management. Each platform usually comes with its own proprietary data structures.

This challenge can be addressed deploying a Data Service that meets both data and integration architecture needs. Data services can be assembled using various underlying technologies, and is designed to provide standardized data models, centralized governance and security, reusable interfaces, and robust data management – ensuring that data is handled with high performance and reliability.

Unlike data lakes or data warehouses, which are typically associated with analytics-focused architectures, a Data Service handles day-to-day business transactions and real-time integrations. In contrast to an integration bus that merely replicates data across systems, the Data Service maintains the reference version of each dataset as the authoritative source.

A data service must include or be integrated with a data governance solution to ensure visibility, consistency, compliance and ultimately high-quality data across all applications, enabling trusted, well-managed data for both operational and analytical use.

3) Analytics, AI, and Data-Driven Workstreams

A well-designed common data service can feed master and transactional data into advanced analytics and AI platforms via efficient data pipes and extract, transform and load (ETL) processes. Output from advanced analytics models featuring leading-edge data science and AI then loop back into business applications to enable data-driven workstreams —fully-automated or human-in-the-middle processes that respond to real-time insights.

Depending on the use case, technology stack, and performance requirements, this “data-driven loop” can be implemented using various architectures, from event-driven systems to data streaming.

4) Low/No-Code Platform

To extend the functionality of out-of-the-box applications and accelerate innovation, the digital business platform should prescribe a single low-code/no-code platform. These tools empower “citizen developers” to create small applications and workflows without deep technical expertise.

For easy adoption and maximum impact, these platforms should be structurally integrated with business applications, the common data service, and analytics tools. This set-up allows business users to quickly roll-out new business processes and leverage data driven insights at speed and scale, while maintaining alignment with IT standards.

5) Centralized Management, Provisioning, and Delivery

Agile IT delivery requires more than just a well-designed architecture—it depends equally on robust infrastructure management. While architecture defines how applications, data, and processes interact, infrastructure management ensures that these components are deployed, provided, and maintained efficiently.

A flexible digital platform operates within a hybrid hosting model, conceived to seamlessly manage workloads across cloud, on-premises, and edge environments. This flexibility allows case-specific optimization: leveraging the cloud for performance-intensive tasks, on-premises infrastructure for high-availability requirements, and secure edge zones with high-speed connectivity to support sensors, industrial controllers, and other IoT devices.

A standardized hosting model serves as an enabler of the organization’s cybersecurity strategy. As distributed hybrid infrastructures become the norm, the zero-trust security model has emerged as the predominant approach, requiring strict verification for every user, device, and service attempting to access resources. To be effective, zero-trust principles must be tightly aligned with the integration architecture, DevOps pipelines, and change management processes, ensuring that security is embedded at every stage of the design, engineering and deployment to support a robust DevSecOps program, where security is not an afterthought but an integral part of the platform’s lifecycle.

Normalized identity and access management services and policies improve user experience, accelerate adoption, enhance security, and reduce total cost of ownership.

Meanwhile, a common DevOps platform, synchronized release schedules and centralized configuration management streamline the orchestrated delivery of new features and updates across applications and infrastructure, increasing speed, productivity and simplifying change management. These delivery mechanisms should be harmonized with relevant transformation and IT “ways-of-working” like project management and Scrum.

This platform-based approach transforms the IT operations and support teams into a highly efficient internal “service provider,” offering a scalable and flexible infrastructure. By standardizing processes, tools, and delivery mechanisms, IT can seamlessly provision resources on demand and elastically support a diverse range of “internal customers” from project teams to entire business units.

Technology Selection

Choosing the right technology to build a digital business platform depends on the organization’s business context, application portfolio and digital strategy. Organizations that rely primarily on off-the-shelf SaaS or IaaS solutions focus on hosting, integration, and management technologies that support and interoperate with these platforms.

In contrast, organizations with a sizable number of custom-developed applications can adopt dynamic provisioning patterns such as virtualization, containerization, and infrastructure-as-code, enabling flexible, scalable, cost-effective deployments.

Selecting technologies to build a digital business platform requires an exquisite compromise between best-of-breed solutions and seamless interoperability. While best-of-breed technologies may offer specialized capabilities and advanced features, their integration into a broader ecosystem can introduce complexity, increased costs, and operational inefficiencies.

Prioritizing technologies that support integrated management is desirable, as this enables greater flexibility, agility, and cost-efficiency. An integrated and gradually automated infrastructure simplifies deployment, maintenance, and updates while ensuring streamlined processes and data flows across layers of the architecture.

Beyond TOGAF: Architecture Design and Infrastructure Management

A keen observer would reason that these principles reflect the TOGAF architecture framework, which also emphasizes modularity, interoperability, and alignment between business and IT, and addressed the same “layers”: business, data, applications, technology and governance.

The TOGAF framework –widely applicable in this context– certainly provides a strong foundation for architecture design. However, it often falls short or is too complex in addressing modern operational needs, such as hybrid hosting, DevOps practices, and streamlined resource provisioning in cloud environments – areas that expert practitioners might reasonably place within the technology or governance layers.

By embedding these management elements with simplicity, the digital business platform seamlessly extends TOGAF, enabling organizations to not only design scalable architectures but also implement them with speed, efficiency, and agility.

Simplicity to Manage Complexity

While extremely powerful in streamlining IT delivery, the digital business platform is –and must be approached as– a very simple concept: a set of architecture patterns and technology standards methodically adopted to enhance flexibility, scalability, and operational efficiency. It ensures that business processes, data services, and applications are modular, interconnected, and easily adaptable to evolving business needs.

By also envisioning and standardizing infrastructure management and delivery elements such as hosting, access control, and DevOps practices, it enables organizations to rapidly deploy solutions, optimize costs, and continuously innovate without being weighed down by technical rigidity.

Simplicity should be both a target and a mindset, guiding design and decision-making to create a streamlined, efficient ecosystem. When applied methodically and thoroughly, simplicity unlocks extraordinary long-term benefits. In most cases we’ve worked on, retroactive normalization and rationalization have resulted in less work for operations teams, not more.

Accelerating Digital Metabolism

The platform approach accelerates digital metabolism, enabling faster time-to-market for digitally enabled business innovations, enhanced data-driven decision-making, and continuous process improvements. Over time, it fosters a “digital-first” mindset by dispelling the myth of overwhelming technical complexity.

Implementing a modern digital business platform to manage technical debt is a strategic enabler of growth. As McKinsey & Co. notes, “Companies effectively managing technical debt experience revenue growth rates 20% higher than those with poor technical debt management.”

In the next article, we’ll present an example of a digital business platform implemented with actual technology components. In the third article, we’ll explore the organizational model necessary to support this architecture.

Further Reading

Tech debt: Reclaiming tech equity, McKinsey & Co.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-debt-reclaiming-tech-equity

Digital acceleration is just a dream without a new approach to tech, Boston Consulting Group
https://www.bcg.com/publications/2020/how-to-successfully-accelerate-digital-transformation

Strategic Roadmap For The Composable Future Of Applications, Gartner, Inc.
https://www.gartner.com/en/doc/433984-2021-strategic-roadmap-for-the-composable-future-of-applications

Why You — Yes, You — Need Enterprise Architecture, MIT Sloan
https://sloanreview.mit.edu/article/why-you-yes-you-need-enterprise-architecture

How to build a data architecture to drive innovation—today and tomorrow, McKinsey & Co.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/how-to-build-a-data-architecture-to-drive-innovation-today-and-tomorrow

8 Steps for a High-Impact Enterprise Architecture Program, Gartner, Inc.
https://www.gartner.com/smarterwithgartner/8-steps-for-a-high-impact-enterprise-architecture-program

Six Concepts That Will Shape Your Digital Strategy

Six Concepts That Will Shape Your Digital Strategy: Vision, People, Models, Architecture, Data and Execution
| Charlie Ranalli

We recently published an updated digital strategy framework to help organizations start – or restart – their digital strategies.

Drawing on years of experience and collaboration with visionary leaders, this article explores the six essential concepts at the heart of the framework. These concepts—Vision, People, Models, Architecture, Data, and Execution—offer a clear and actionable foundation for organizations to achieve transformative success, even amid the inherent complexity of the implementation.

Vision: The Catalyst for Change

At the heart of any digital strategy lies a bold and innovative vision. This vision sets the organization on a new path—or accelerates its existing trajectory—by leveraging technology as a transformative force. It is the driver that redefines what’s possible, enabling businesses to challenge and disrupt the status quo.

It’s easy to dismiss Netflix, Amazon and Uber as “digital natives”, but they exemplify how a compelling vision can revolutionize industries. By reimagining entertainment, retail, and transportation, they disrupted decades—and in some cases centuries—of established norms, leaving competitors struggling to catch up.

Netflix executed three transformative strategy pivots over three decades, each reshaping its industry. First, in the late 90s, it delighted customer eliminating late fees and decimated Blockbuster with a DVD delivery subscription model. Ten years later, it revolutionized television with on-demand streaming, replacing traditional viewing habits. Finally, Netflix disrupted Hollywood itself by producing its own content —and established binge-watching, starting with House of Cards— in the process becoming a powerhouse studio and redefining entertainment forever.

A powerful vision doesn’t merely guide technology adoption; it redefines the organization’s mission and purpose in a digital-first world. It inspires teams to think beyond incremental improvements, pushing them to explore entirely new business models and customer experiences. The vision must be ambitious yet achievable, offering a clear direction that unites stakeholders around a common goal.

People: The Driving Force Behind Transformation

While technology is an enabler, people are the true rainmakers of digital innovation. From executives to frontline employees, the success of a digital strategy depends on the creativity, expertise, and commitment of the organization’s workforce.

Digital transformation starts at the top, with leadership embracing their role as champions of change. The CEO and executive team must drive the vision, ensuring alignment across all levels of the organization. However, transformation cannot stop at the leadership level. A digital strategy requires a cultural shift that permeates every corner of the organization, encouraging innovation, collaboration, and a willingness to challenge the status quo.

Empowering teams with the skills, tools, and autonomy to experiment and iterate is critical. As technology becomes increasingly commoditized, it is the ingenuity and determination of people that will differentiate successful organizations from their competitors.

Models: Unlocking Innovation Through Disruption

Identifying opportunities for innovation should be first step of any digital strategy. Yet, even creative business leaders sometimes struggle to envision what to transform, especially in traditional industries where long-standing practices and structures dominate.

This is where the Models component of the framework comes into play. By disassembling the business model, customer lifecycle, and operating frameworks, organizations can uncover hidden opportunities for disruption and growth.

This process of creative deconstruction allows leaders to think like startups—challenging established paradigms and imagining new ways to deliver value. Whether it’s rethinking customer engagement, rewiring supply chains, or introducing new pricing models, the possibilities are endless. The goal is to create structural and lasting competitive advantages that set the organization apart in a crowded marketplace.

Architecture: Building the Foundation for Innovation

Once new business models and processes are identified, the next step is to design a Digital Business Platform that brings them to life. The Architecture component of the framework focuses on building a modular, flexible, and scalable infrastructure that enables rapid innovation and adaptation. This requires more than just adopting state-of-the-art technology—it demands a fundamental rethinking of how processes and models are reflected in technology.

Digital leaders are not afraid to start with a blank slate, rebuilding their IT tech stacks and engineering practices to enable composable designs that align with operational capabilities. These architectures must enable seamless integration across legacy systems, cloud platforms, and emerging technologies.

None of the organizations we worked with over the past decade had architecture functions configured to drive digital innovation effectively from the outset. But with the right approach, they were able to build teams to support agile, customer-centric operations and enable transformative growth.

Data: The Nervous System of the Digital Enterprise

Data is the central nervous system that connects every moving part of the organization. To succeed, businesses must skillfully capture, manage, and analyze data. The goal is to create a unified data repository that removes silos, enables efficient execution and becomes a single source of truth that informs decision-making, uncovers insights, and drives innovation. This often requires overhauling existing data models, architectures, and governance practices.

The importance of data-driven decision-making has grown exponentially with advancements in analytics and AI. These technologies offer organizations unprecedented opportunities to understand customers, optimize operations, and uncover new pockets of growth.

However, building a data-driven culture takes time and effort. It requires robust governance structures, seamless integration of data assets, and a commitment to turning raw data into actionable intelligence.

Execution: Connecting Strategy to Results

The final concept—Execution—is where ambition meets reality. Execution is about connecting all the pieces of the digital strategy with agility, precision, and a relentless focus on value.

It starts with a solid strategic execution discipline, ensuring that initiatives and investments are aligned with business priorities and measured against clear performance targets. Organizations with well-established strategic execution practices have a significant advantage, as they can integrate digital initiatives into existing management processes and drive results more effectively.

Success requires a remarkable commitment to managerial hygiene: plan, execute, measure, adjust, and repeat. While this may sound straightforward, the graveyard of failed digital transformations proves that it’s anything but. According to McKinsey & Company, approximately 70% of digital transformation initiatives fail to achieve their intended goals. Leaders must prioritize alignment, accountability, and continuous learning, ensuring that the organization remains focused on implementing change and delivering meaningful outcomes. Digital innovation is a marathon, not a sprint.

The six concepts—Vision, People, Models, Architecture, Data, and Execution—are the foundation of a successful digital strategy. They offer a structured yet flexible framework that empowers organizations to navigate the complexities of digital innovation with confidence.

Transformation Strategy: An Execution Toolbox

| Charlie Ranalli

Originally published May 2022, updated June 2024.

More than a decade ago, George Westerman, a Research Scientist at MIT Sloan’s Center for Digital Business, and his team embarked on a quest to answer a question that had eluded them thus far: why do companies with comparable investments in technology yield radically different impacts and returns?

The resulting report was named one of the five most influential thought leadership papers of the decade. The revealing finding is that there are two dimensions to measure digital maturity: digital intensity and transformation management intensity. These two ingredients have different impacts on revenue growth and profitability. Digitally intense companies may drive more revenue from their assets, but they are not necessarily more profitable.

The difference? Transformation: mastering the deployment of technology to reshape business models, customer experiences and operating capabilities. Organizations excelling in this second dimension are 9% more profitable than their peers, while those excelling at both dimensions enjoy a 26% increase in profitability over their peers. Conversely, laggards experience a 24% gap in profitability.

It’s common for leaders to feel excited about implementing innovative technologies but less enthusiastic about embarking on the painstaking task of redesigning their business from strategy to every customer touchpoint, process and job description. This toolbox aims to simplify the latter. While digital strategy outlines “what” needs to be achieved to remain relevant in the digital age, transformation strategy defines the “how” of achieving these objectives.

Defining the “How” in Digital Transformation

The methods for crafting digital and transformation strategies differ. As discussed in a previous article (refer to Anatomy of a Digital Strategy), the “strategy matrix” offers a nearly universally applicable framework for a digital vision. It is a logical sequence, starting with a maturity diagnostic, aligning with key strategic business priorities in the “pillars,” formulating ambitious visions for digital innovation initiatives, and then meticulously planning the development of technical, talent, and execution enablers. The matrix feeds a roadmap that establishes a pace of implementation and change, which must be calibrated to the organization’s ambitions, capabilities and resources. This structured approach not only simplifies the process but also channels creative energies toward defining the “what” within the matrix.

The transformation strategy, on the other hand, is highly tailored to the target organization’s initial maturity level, aspirations, operating model, and practical constraints. Over a decade of experimentation, research, and experience by businesses, academics, and consultants has yielded a set of best practices encapsulated in “building blocks” that can be customized to suit the unique needs of individual organizations. I refer to this collection as the transformation toolbox.

Redesigning the Organization for Digital Innovation

At the heart of a transformation strategy is a constant, organic and methodical retooling of the organization. This begins with clearly assigning each “pillar” of the digital strategy to a senior executive. These executives become the champions of their respective domains, driving technology-enabled business model innovations. The Chief Marketing Officer (CMO) could be responsible for digital customer engagement, whereas the Chief Information Officer (CIO) will in most cases be tasked with the pillar of digital infrastructure.

In parallel, middle management must be organically restructured around key “macro-processes” that are pivotal for disruptive change or organic digitalization. This involves identifying processes that are ripe for digital reinvention or enhancements.

The Transformation Office: Central Command

A transformation office serves as the “control tower” of the entire digital transformation process. This centralized entity orchestrates strategic execution, oversees digital initiatives, manages change, and removes obstacles that impede progress. It is responsible for maintaining the momentum of transformation efforts and ensuring that all parts of the organization move cohesively towards the shared digital vision.

The Digital Innovation Hub: Idea Engine

The digital innovation team (more here) is the source of vision, knowledge, and experience in how technologies will transform the way a company does business. It is the heart of the transformation program and will cement the organization’s ability to innovate by identifying and adopting future technologies beyond the initial vision.

While the name may vary depending on company size and convention – Digital Innovation Center in larger corporations, Digital Innovation Team in smaller ones, Excellence Center(s) after becoming a digital leader – the purpose is similar: a compact but assertive group of specialists to lead the design and implementation of initiatives that bring Digital Transformation to life.

Agile and Fusion Teams: Catalysts of Change

Change is driven by people, and in the implementation of digital transformation, Agile and fusion teams are the established approach. Agile teams are small, multidisciplinary groups that work in rapid iterations and adapt business capabilities to change. They are designed to experiment, iterate, and deliver solutions swiftly.

Fusion teams, on the other hand, combine the expertise of traditional business functions with digital savviness. They are instrumental in embedding digital capabilities into the DNA of the organization without disrupting core operations. These teams are essential for ensuring that digital transformation initiatives are not siloed but are integrated across the business.

The composition, management, and governance of these teams are crucial. Typically, the leads (often referred to as “product owners”) should possess extensive experience in the company’s specific business capabilities that the team aims to transform. They report to the line of business, with the transformation office (and if applicable the innovation teams) guiding the inception and vision, establishing processes, providing training, facilitating change and orchestrating governance.

Ideally, these teams will assume operational responsibility of a small fraction of the business (for example a single or a few stores or branches in a retailer or bank, a few routes in a distribution business) to test the digitally transformed models or processes.

This is why business ownership of the outcomes is non-negotiable. These change-focused teams operate in parallel to “live” functions but are not as pressured for short-term results, affording frontline and middle managers additional leeway and resources to experiment safely. However, design and testing cannot occur in isolation, nor can responsibility be solely delegated to the transformation office, innovation functions or project organizations.

Governance Model: Assigning Responsibility and Streamlining Decision Making

The governance model establishes the framework for decision-making, responsibility, and accountability. It is essential for ensuring that digital initiatives are aligned with the strategic objectives and that there is clarity regarding decision rights and responsibilities. This model facilitates effective management of the transformation process, ensuring that resources are allocated efficiently and that initiatives progress as planned.

Most digital transformation programs result in a “federated” model with centralized teams, typically innovation teams and a transformation office, reporting to a C-level executive. These teams establish and orchestrate agile teams operating within business units. The governance model formalizes the mechanics of this federated system, clearly defining leadership responsibilities, operational accountability, decision rights, and effective coordination mechanisms to manage the program, initiatives, and outcomes.

Reference Frameworks: The Blueprint for Transformation

A critical component of the transformation process is the set of reference frameworks. These are comprehensive guidelines that emerge from the digital strategy, dictating the design and implementation of the initiatives.

Customer Lifecycle

The customer lifecycle framework addresses how the organization will attract, serve, and delight customers in a digital context. It outlines models for customer engagement across various touchpoints, leveraging digital tools to create a seamless and personalized customer experience.

Operating Model

The operating model acts as the “internal cabling” of an organization, describing macro-processes in a silo-less manner. This horizontal approach to design end-to-end processes produces customer focused outcomes, efficiency and flexibility, allowing the company to respond swiftly to market changes or customer needs.

Enterprise Architecture

Enterprise architecture defines the technological backbone of the organization. It dictates how technology will be deployed to support and enhance business processes, ensuring that the digital solutions are scalable, secure, and integrated with existing systems.

A common misunderstanding is that the architecture is merely “technical stuff.” While some layers of the architecture are indeed very technical, the uppermost layers describe the business: operating model components, data domains, customer touchpoints, functions and end-to-end processes. Leaders, managers, and teams working on transformative projects all need to know, use, and comply with the enterprise architecture as a frame of reference. This requires a fresh approach from IT leaders and teams, elevating the scope of the architecture from wiring schematics to digitally enabled business capabilities.

The IT Delivery Model: From Services to Value

Redesigning IT delivery mechanisms from a service to a value-driven model is crucial for supporting digital transformations. This shift focuses on aligning IT efforts with the pillars of the digital strategy, success and impact of the initiatives and business outcomes, emphasizing the creation of tangible value rather than supporting business operations.

By adopting a value-centric approach, IT can better support strategic initiatives and support innovation. This transformation requires a fundamental change in mindset, processes, and metrics, ensuring that IT initiatives are directly tied to the organization’s strategic goals. We will explore this topic in greater detail in a future article, discussing practical steps and best practices for achieving this enabling transition.

Digital Transformation Dashboard: Measuring Progress and Impact

Lastly, a digital transformation dashboard is indispensable as the single source of truth to track the transformation journey’s advances. It provides a holistic view of strategic and operational gains, measuring key performance indicators (KPIs) and tracking milestones. This dashboard is crucial for keeping leadership informed and making data-driven decisions.

Conclusion

Digital transformation is not a destination but a journey of continual adaptation and growth. A robust transformation strategy provides the “how” to navigate this journey, ensuring that the organization remains nimble, innovative, and resilient. It requires an orchestrated effort across all levels, a commitment to agile and collaborative ways of working, and a dedication to a customer-centric approach in a technologically advanced business environment.

By embracing these principles and putting in place a structured approach to transformation, businesses can achieve the digital excellence necessary for success in today’s dynamic market landscape.

More on Strategy

Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI

| Charlie Ranalli

Authored by experts at McKinsey, this guide is a comprehensive and insightful exploration into the transformative power of digital technologies and artificial intelligence. They meticulously outline the frameworks and methodologies that successful companies have employed to not only adapt but thrive in the rapidly evolving digital era. It presents a blend of theoretical insights and practical applications, making it a valuable resource for business leaders aiming to navigate the complexities of digital transformation.

One of the standout features of “Rewired” is its pragmatic approach to integrating digital and AI technologies into existing business models. The authors emphasize the importance of aligning digital initiatives with core business strategies, ensuring that technological advancements contribute to business goals. The book is full of case studies and real-world examples, illustrating how various organizations have successfully implemented digital transformations. These examples provide readers with tangible, actionable insights that can be adapted to their unique business contexts. Additionally, the guide addresses common challenges and pitfalls associated with digital transformation, offering solutions and best practices to mitigate risks and maximize returns.

“Rewired” highlights the human aspect of digital transformation, focusing on the crucial role of leadership, culture, and talent management. Aligned with the core tenants of this blog, the authors argue that while technology is a powerful enabler, the true drivers of transformation are the people within the organization. They provide strategies for nurturing a culture of innovation, continuous learning, and agility, which are essential for sustaining competitive advantage in the age of digital and AI. Overall, “Rewired” is an indispensable resource for any business leader seeking to harness the full potential of digital and AI technologies, offering a roadmap to outcompete and excel in the modern business environment.

Rewired: the McKinsey Guide to Outcompeting in the Age of Digital and AI
by Eric Lamarre, Kate Smaje, Rodney Zemme

Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers

| Charlie Ranalli

This is a belated review of a book very, very useful for enterprise intrapreneurs and start-up entrepreneurs.

Business Model Generation provides a comprehensive and practical toolbox to design and evaluate business models: a reference framework (the widely adopted Business Model Canvas), a set of patterns (e.g., unbundling, long tail, multi-sided platforms), design technics (a lot in common with Design Thinking), strategy (in the authors’ own words “approaches to reconsider business strategy though the lens of the business models canvas”) and finally a process for business model design.

It is full of real-life examples of how companies invented or disrupted markets with ingenious business models.

For teams that want to push the boundaries of business model innovation, this book provides an extremely practical framework to walk the exploratory journey with focus and technique.

Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
by Alexander Osterwalder and Yves Pigneur

Anatomy of a Digital Strategy

| Charlie Ranalli

The strategy is the starting point of the digital transformation journey. Digital leaders have a sound strategy. Strategy is repeatedly cited in digital transformation papers, books, media coverage, and this blog. Strategy, strategy, and more strategy.

So, how does a digital strategy look like?

One important clarification before going further, this post is not a comprehensive strategic thinking methodology to develop a digital transformation strategy. For those beginning the digital journey, the framework provides a practical guide to get started. This post proposes a structure to organize ideas, priorities, initiatives, and projects to describe the digital future and the route to get there, in a logical and understandable format.

Back to the strategy. The details will vary significantly depending on industry characteristics, competitive dynamics, starting maturity, the possibilities available to each company, and the ambition of the leadership team in charge. But experience points to a few common elements.

Structure of a Digital Strategy

Digital Vision

The digital vision describes as precisely and as simply as possible how business will be conducted after being transformed by technology.

Will the means of delivering value to customers change? Can the customer experience be thoroughly reinvented? Can a digitally enabled commercial model change the rules of an industry?

Fractional or pay-per-use, peer-to-peer platforms, anything-as-a-service, open-anything are the sort of models that give digital visions stamina and have a higher chance of putting the leaders behind the strategy in the offensive and the competitors on the back foot.

Not all business or sectors can be wholly reconfigured by technology, but many seem stagnant until a clever entrepreneur comes with a disruptive idea. Simply prescribing more use of technology, for marketing, process automation or old-school ecommerce (it’s almost 30 years old already!) will rarely make a digital strategy disruptive or even competitive enough to move the needle. Combined adoption of several technology innovations, aggressive investment in exceptionally strong capabilities and nimble execution may provide sustainable competitive advantage. Designing and successfully implementing a D2C model that complements the role of traditional channels without creating conflict may also it.

Summary: the digital vision must point to structural and significant changes to how business is conducted, por instance to novel approaches to engage with customers, create value or use assets, enabled by technology. Adopting more platforms, no matter how advanced, is an IT plan, not a digital vision.

Digital Aspiration

The digital vision translated into measurable ambitions: market busting pricing or value creation structures, disruptive growth or market penetration targets, massively reconfigured financial ratios, [probably resulting in] improvements in EBITDA, etc. There are two references to develop the targets: one is bottoms-up, coming from the business cases supporting specific initiatives and investments, the other is top-down: the aggregate jump in financial performance compared with the past, the industry, or benchmarked against digital leaders.

The digital aspiration converts the vision in quantifiable impact, provides targets to align expectations, and supports the case for investment and change. The targets in the digital aspiration should be one of the first exercises of the “single source of truth” practiced by digital leaders. Tweaks and recalibrations are typical in a journey full of uncertainties and experimentations, but if the metrics or targets keep on changing, or different stakeholders look at different versions, it is time to reconsider if things are really going in the right direction.

It is beyond debate that digital leaders drive better results, here is where a leadership team must agree on the drivers that will turn investment and change in quantifiable impact.

Strategic Pillars

These are a few “themes” that support the vision and align the components of the strategy. Some are industry specific (streaming may a theme for media but not for other industries) but some (like customer experience and data-driven business) can be innovation vectors in many industries.

Identifying and adopting the pillars serves several useful purposes:

  • Build consensus on which are the key innovation and value-creation drivers
  • Provide a structure to align priorities, initiatives, technologies, capabilities and investments across teams and business units, and
  • Give the vision and strategy focus, stability, and credibility over time as priorities, initiatives and technologies evolve and shift

The pillars are one – if not the most – significant elements connecting the pieces of the digital strategy. They should be carefully picked and tightly aligned with the vision.

Initiatives and Technologies

These are the concrete plans, actions, and investments to convert the vision in actions and tangible results. They should be described at a very high level in the digital strategy itself, leaving the details to stand alone plans or mission statements for the dedicated agile teams tasked with the execution. (More on this below.)

Some initiatives and technologies will span multiple pillars – in fact these are the more attractive plays because application of multiples technologies to the same business situations have a multiplicative effect in terms of innovation, and if properly executed and the underlying competencies perfected are very difficult to imitate.

An example: bundling industrial equipment “as-as-service” with auto-reordered supplies based on historic consumption rates and real-time customer site inventory spans customer experience, data-driven business and process automation as pillars, and will require integrating remote sensors, advanced analytics, IoT and a highly autonomous B2B e-commerce platform.

The proposed format of compact summaries in each intersection of pillars, initiatives and technologies promotes strategic alignment and consistency. From this point commonly employed strategic planning methodologies like OGSM can be used to define and track measurable goals and actions across different projects, parts of the organization, etc. Organizations with robust strategic planning processes can leverage them.

Capabilities

The vision describes the future, the pillars the change/value drivers and the initiatives and technologies map actions and investments. The capabilities are the enablers.

Most companies will have significant gaps to fill here. Some of are new organizational functions like the digital innovation hub and the transformation office, others are new skills like change management and Agile methodologies, and others are capacities like a modern operating model on the IT function, a modular digital architecture or good data governance. Each is described at more detail in the framework or specific articles.

The example in the picture is representative, but actual strategies can vary significantly. The capabilities section of the strategy will play a significant role in the design of the transformation strategy.

Goals and KPIs

Finally, the goals and KPIs are an execution-grade version of the targets in the digital aspiration, complemented with those from the business cases or initiative-specific.

I recommend using three types of KPIs. The execution indicators track that the basics are happening: people hired or reassigned, projects started, organizational changes implemented. These seem obvious, but when the to-do list is long this basic tracking anticipates roadblocks early on. The second set of transformation indicators track change. These are initiative-specific, but the common denominator is that they confirm that projects are churning along, and the innovative changes are being rolled-out. Then final set is the real thing: the digital KPIs track the actual impact: customers adopting new revenue models or better yet migrating from the competition, shift from human-assisted to fully digital order processing, etc. These are the ultimate proof of success!

Execution and transformation indicators are initiative or project-specific and probably transitory. Once enough execution and change momentum is achieved they can be discarded and focus shifted to the digital indicators that track the deep, disruptive vectors. Some metrics related to capabilities or culture (e.g. mid-level managers fully embracing digital and Agile by leading or originating ideas) may be kept in place for years to track transformation momentum beyond specific initiatives and projects.

A Word About Simplicity

The best digital strategies are surprisingly simple and compact. The structure pictured above can extend to two or three pages after initiatives, technologies, capabilities, and key metrics are broadly described and maybe some business or functional-unit level details are incorporated.

But if it cannot be kept at two or three pages, something is out of place. Details may have to be pushed down to specific action plans, or worse yet, the quantity of initiatives, projects or technologies be unrealistic. A complicated strategy or even a complicated presentation of the strategy can negatively impact communication, comprehension and alignment.

There are usually challenging actions, changes, and investments even behind beautifully simple and focused strategies. If it looks too complicated, it most likely is.

More on Strategy

The New Elements of Digital Transformation

| Charlie Ranalli

The team behind The Digital Advantage: How Digital Leaders Outperform their Peers in Every Industry and The Nine Elements of Digital Transformation reflected on their influential research after surveying 1300 executives in more than 750 global organization.

Their earlier research on digital transformation identified two dimensions through which leading companies outperform their peers: digital capability and transformative leadership capability. They found that the elements of leadership capability have endured, but new elements of digital capability have emerged.

Particularly opportune is the addition of a digital architecture as a critical platform that enables nimble innovation. In my own experience, the lack if a well-designed and implemented architecture prevents the roll-out of initiatives large and small, consuming more technical resources and frustrating business partners.

The New Elements of Digital Transformation
By Didier Bonnet and George Westerman
MIT Sloan Management Review, November 2020