Redefining enterprise transformation in the age of intelligent ecosystems

As IT professionals and business decision-makers, we’ve routinely used the term “digital transformation” for well over a decade now to describe a portfolio of enterprise initiatives that somehow magically enable strategic business capabilities. Ultimately, the intent, however, is generally at odds with measurably useful outcomes. Transformation initiatives usually defy gravity in terms of what is practical and realistic for modern enterprises with legacy applications and infrastructure, yet we persist in funding them on a large scale and positioning them as value and outcome-driven  

When we consider the implications of fixed infrastructure costs and capex investments, efforts like cloud migration, enterprise data platforms, robotic process automation (RPA), and API-first initiatives presented an almost irresistible opportunity to enable and unlock business capabilities and value. What we consistently overlooked were the direct and indirect consequences of disruption to business continuity, the challenges of acquisitions and divestitures, the demands of integration and interoperability for large enterprises and, most of all, the unimpressive track record for most enterprise transformation efforts. The scorecard speaks for itself. A study by McKinsey found that less than 30% of digital transformation initiatives are successful in achieving their objectives. For large enterprises, the success rate is even lower, with estimates hovering around 16-20% due to the scale and complexity of the initiatives. 

The API-first era 

In 2012, as a software architect in a global sportswear and apparel enterprise, it became clear to me during the API-first era that transformation was no longer a matter of lofty ambitions that included monolithic service bus implementations, refactoring, reverse engineering or re-engineering in-house applications along with infrastructure modernization. Later, as an enterprise architect in consumer-packaged goods, I could no longer realistically contemplate a world where IT could execute mass application portfolio migrations from data centers to cloud and SaaS-based applications and survive the cost, risk and time-to-market implications. Our commitments to the businesses we supported as architects were perpetually at odds with reality. A tectonic shift was moving us all from monolithic architectures to self-service models and an existential crisis for architecture and IT was upon us.   

What emerged was a pattern of adopting abstracted interfaces, process automation and interoperable ecosystems where applications, cloud and data platforms and infrastructure were implemented as low-code, processes engineered through automation and data consumed through common, open interfaces. It was clearly more about modernization and transformation in place. The landscape was evolving to a focus on sustaining continuity while gaining competitive advantage through access to data through the most practical path of least disruption. 

My experiences as architecture principal in B2C-connected vehicle integration for the automotive OEM industry focused on unlocking and enabling the access and flow of data from vehicles globally and at scale. I saw this pattern emerge again as a chief architect for a B2B SaaS platform company also in the automotive industry, where our team made a conscious effort to pivot away from reengineering archaic RPG applications on an AS400 platform and focus instead on abstracted and open interfaces to data and process automation, cloud infrastructure and SaaS-based ecosystems. 

The mega-vendor era 

By 2020, the basis of competition for what are now referred to as “mega-vendors” was interoperability, automation and intra-ecosystem participation and unlocking access to data to drive business capabilities, value and manage risk. By the peak of the pandemic, aggregated systems of record data in SaaS-based data lake houses became the preferred destination for global enterprises.  We were all coming to grips with profound operational risk and unprecedented platform disruption when data resides everywhere and nowhere. Monolithic architecture reliability and sustainability across core systems for companies built on acquisitions faced demand spikes, sudden market retractions and the inevitable supply chain crisis that would ensue. 

In 2022, as an enterprise architect in the consumer tools industry, I found that companies that grew exponentially through mergers and acquisitions began to feel the pain of disparate ERP systems, supply chain management platforms and customer experience fragmentation all impacted by redundant data stores and data quality issues. The real risk of making impactful business decisions with questionable data lineage and quality was obvious. The convergence of all these factors resulted in a drive toward unified data pipelines and enterprise data platforms that had the potential to support aggregated and contextualized access to data via a semantic layer for business. Machine learning algorithms were also being included for data cleansing and anomaly detection. A “slow-moving” AI coup was happening in parallel with data at the center and it was more significant than any prior technology inflection points. The marriage of data with systems of intelligence started gaining momentum as early as 2014 through the evolution of OpenAI. 

The new new moats 

How do systems of intelligence fit in? I first encountered the term “systems of intelligence” in 2023 when I stumbled on a great article by Jerry Chen entitled “The new new moats: Why systems of intelligence are still the next defensible business model.” In the article, Chen highlights the effects of large language models (LLMs) on businesses built on “moats.” When we think about classic systems of record, moats are the last line of defense in proprietary ecosystems. The “drawbridge” for the moats from an IT perspective has generally included things like APIs, service buses, ETL tooling and integration platforms as a service.   

In modern enterprises, we’ve unwittingly built data conduits with redundant data sets within and between systems of record for downstream consumers via systems of engagement like web, chat and mobile that directly adversely affected the customer experience architecture and our ability to manage data integrity and accuracy. Ultimately, if the data lineage is fragmented, of low quality and without context, the customer experience would surely be fragmented or broken as well. The middle tier – dominated by classic enterprise bus architectures – also lacked any flexible intelligence apart from brittle and sometimes convoluted business logic that further added to the complexity of managing applications, infrastructure and integrations. 

LLMs coupled with generative AI and agent-based process automation can negotiate actions via APIs, qualify context and confidence rankings based on data quality and lineage and navigate the landscape of data stores that reside behind the moat and between other ecosystems. Data and AI-driven conversations are now emerging between humans and systems where agency and interoperability now replace codified integration and centralization. 

So, what do systems of intelligence mean in terms of the same ecosystem-based players that have plagued IT with vendor lock-in for decades? Enterprises survive and thrive through their capacity to pivot and adapt. Learning systems pivot and adapt based on events and new training data. Pivoting our architectural design assumptions away from systems of record that are abstracted by integration layers and redundant data stores to systems of intelligence that can enable fully autonomous process automation with agents is the nature of modern transformation. Semi-autonomous, human-mediated “conversations” and agents trigger workflow automation based on events, interactions, system metadata and aggregated enterprise data platforms. 

Interoperability: The heir apparent 

The basis of competition in the age of intelligent ecosystems resides with industry leaders who recognize that enabling inquiry and automation by creating intelligence-based value chains within ecosystems and between them using agentic automation will rule the day. This will facilitate a fundamental shift from the use of terms like digital transformation as a path to business value and capability enablement. Some current examples might include SAP’s Joule agentic automation and Salesforce’s Agentforce technology. The business value of Joule-based agents has obvious value within the SAP ecosystem just as Agentforce has value within the Salesforce ecosystem, but agency workflow automation that can traverse and train on multiple ecosystems has a clear advantage. For architects, this means homogenous ecosystem players like SAP and Salesforce will no doubt proliferate the landscape with locked-in agentic workflow solutions, but the heir apparent is still interoperability and the open standards that facilitate it.   

How will agentic intelligence architectures facilitate business capability and value?  

• Orchestration: Agents orchestrate an inquiry process by managing it into steps, and assigning agents to each task.   
• Goal-based: Agents “understand” and execute to specific goals, enabling complex and deep interactions.  
• Planning and Reasoning: The agents are capable of complex planning and multi-step reasoning.   
• Context-aware: Agency-based automation evaluates situationally, including past interactions, and user context to make decisions and take appropriate actions.  
• Iterative Learning:  Intelligent agents learn and improve over time.   
• Flexibility and customization: Agency-based architectures provide flexibility and context-sensitivity to suit specific domains.  

What does ‘redefining transformation’ really mean? 

When I reflect on what redefining enterprise transformation in the age of intelligent ecosystems really means, I recall some of the most profoundly disruptive events in recent experience, and how they might have gone differently.  Systems that learn and train on change events in core systems of record, demand patterns in systems of engagement and adapt contextually to support systems of interaction are what defines true enterprise system resilience. 

In revisiting my own pandemic experiences as an architect, I can re-imagine what a world might look like when faced with overwhelming odds again using agentic architectures and AI-enabled systems. Through the lens of a transformation defined by intelligent ecosystems and agentic automation system availability, reliability, data and transactional integrity, business semantics and data context survive the risks of ERP and business model diversity and fragmentation because they relentlessly train on contextual system changes, data, and business characterization. 

The future of the modern enterprise doesn’t belong to agent-based ecosystems, but agentic architecture can certainly facilitate shifts through new value streams that flexibly and responsively adapt to and learn from change. 

Dion Eusepi is a technology industry veteran focused on practical innovation in the architectural design, development and delivery of enterprise data and AI-ML platforms and intelligent ecosystem solutions for hybrid cloud environments, multi-tier data pipeline aggregation architectures and infrastructure, for on-premises, cloud and edge compute environments. Dion has had the privilege of contributing to multi-industry Fortune 100 and 500 companies including Ford Motor Company, General Motors, Stanley Black & Decker, IBM and Salesforce. His work includes comprehensive platform solutions for cloud, data, integration and AI-led enablement strategy and spans core ERP, CRM and HCM systems, SaaS and digital channel integration, ML ops, IIOT and I4.0 edge compute data distribution that connect broad, deep PLM eco-systems. 

This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects.