Build Your Enterprise Architecture Discipline

Labor productivity in advanced economies has fallen by half since 1980. And productivity levels in all economies have declined since 2008’s great financial crisis. An aging workforce, declining fertility rates and skill shortages are exacerbating the situation, moving stakeholders’ focus from revenue growth to profitability. However, traditional moves like cutting costs and increases won’t fix this problem in the long term. To boost productivity, Heads of Enterprise Architecture must apply their cross-enterprise perspective to the four dimensions of business capabilities.

• People: Enable radical flexibility that boosts human productivity. Human beings, at least for the time being, are essential to delivering business capabilities, and they’re more productive when they can prioritize their well-being, education and professional development. Partner with chief HR officers to build an exceptional employee value proposition. Deliver radical flexibility by enabling HR and digital workplace applications and systems that can  provide choices to your workforce on location, timing, content and collaboration. 

• Process: Apply value streams for process innovation. Siloed process innovation doesn’t boost productivity, so create value stream maps. With each step, help visualize and link business processes with business capabilities that deliver customer value.

• Information: Improve data usability to increase asset quality. Quality data is usable data: It is relevant, accessible and actionable. Use your expertise to address access problems, because inability to access data is a major obstacle to productivity.

• Technology: Invest in AI to drive digitally dominant productivity. Total factor productivity depends on the ability to create, acquire and use advanced technology. Design for, invest in and integrate AI capabilities to move toward digitally dominant productivity.

As distributed decision making becomes the norm in enterprises, boards of directors are increasingly shifting digital business investment dollars to business functions vs. IT. The trend toward democratized operating models is intended to improve delivery speed, scale agility and support local autonomy. But it also disrupts the Heads of enterprise architecture's landscape of familiar business processes and governance structures.

Faced with new ways of working and a growing constellation of users, Heads of enterprise architecture  must update internal service and operating models as well as the enterprise architecture strategy and value proposition. Consider an internal management consultancy approach with a service catalog that delivers targeted services fueled by collaboration and an enterprise architecture value proposition based on enterprise-level priorities.

A postdigital operating model, or an operating model that allows for the removal of separate digital and business strategies should focus on product-centric (vs. project-centric) delivery. Place enterprise architecture at the enterprise level with enterprise architects building enterprisewide principles and standards for technology governance; business architects working on capability-based planning against enterprise outcomes; and group, product, solution and domain architects at the product, line and team levels supporting stakeholders at various levels of technical maturity.

Keep in mind that a high-functioning enterprise architecture function must be continually revisited and revised. The value proposition, charter, service offerings and operating model must evolve to support the moving targets that are business priorities. A flexible enterprise architecture resourcing model and the ability to rigorous reprioritize are essential to keeping up with distributed stakeholders’ demands, resource constraints and emerging technologies like AI.

Heads of enterprise architecture who welcome emerging technologies like AI hold the key to delivering innovative, productive architectures. With productivity and growth flagging, major tech companies, corporations and investors are betting billions that generative AI will be the panacea that will boost productivity, raise profitability by scaling operations and reduce operating labor costs. In a postdigital age, digital and business strategies can no longer function separately; productivity measures must be accompanied by technology innovation.

Despite the dollars being thrown eagerly at AI, there are major barriers to scalable AI adoption. Here are the top barriers and how to overcome them:

1. Low visibility and poorly coordinated AI experiments lead to duplicated efforts and wasted resources. The way forward: Establish an internal marketplace of AI tools and initiatives to be used for repeated use cases.

2. Insufficient, unstructured or low-quality data impedes accurate AI deployment. The way forward: Partner with data and analytics to create data journey maps, break data silos and spread accountability.

3. Stakeholders have limited knowledge about fit-for-purpose platforms and models to support AI adoption. The way forward: Empower stakeholders to identify the data and tools required to build/buy AI models by holding best practice or training workshops.

4. Heavy-handed governance guidelines for AI-specific risks hinder free experimentation and slow down scaling. The way forward: Provide innovation hubs or sandboxes teams can use to experiment freely without exposing enterprise data or disrupting systems.

5. There is a lack of frameworks to justify scaling or measure return on AI investments. The way forward: Use business capability models to quantify AI opportunities, outcomes and risks, and support AI use case prioritization and planning.