Codeless operating architecture is the future

One of my clients is AgilePoint . A codeless adaptive automation platform. They solve digital transformation problems for large enterprises with a particular focus on reducing complexity, removing technical debt (a software chokepoint) and making the operating system dynamic and adaptable, which means you only have to do it once. AgilePoint will grow with you. It is the highest form of software abstraction I have seen, which means it allows for true citizen development, making your company faster, better and more innovative. Since my work with PMI, citizen development has been a subject that is close to my heart. See:

• https://www.ronimmink.com/how-to-future-proof-your-business-be-athletic-and-apply-citizen-development/
• https://www.ronimmink.com/citizen-development-unlocks-transformation/
• https://www.ronimmink.com/a-world-without-work-but-with-citizen-development/
• https://www.ronimmink.com/citizen-development-will-eat-the-world-and-possibly-your-organisation/

Competing in the Age of AI

AgilePoint has been AI-enabled since 2003. That is why I picked up ?“Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World”. The book examines a recurring pattern of digital firms colliding with traditionally structured firms in sector after sector.

Paradigm shift

We cannot escape the fact that the digital and analogue worlds are becoming one. We are no longer looking at some new technology, at a special kind of company, or at the “new” economy. We are looking at the economy—the entire economic system, every industry, every segment, and every country, across manufacturing, services, and software products.

Opportunity

The emergence of the age of AI has created the greatest entrepreneurial opportunity in the history of civilisation. Gone are the days when competitive advantage could be based on unique, static assets and capabilities, often going decades without disruption.

Asia proofs it

• In China, JD.com has already leveraged a less aggressive digital operating model to roll out thousands of convenience stores each week.9 Walmart should pay attention.
• Tencent adapted ICQ functionality and centralised user data and chat histories to Tencent servers, enabling portability across computing devices. Tencent analyses the data through machine learning algorithms to inform and automate an expanding variety of services.
• WeChat has built as an open platform with easily accessible application programming interfaces (APIs) for software developers.

Limited by the systems

Firms are shaped and limited by their operating models. Large firms often use thousands of enterprise applications and IT systems, use highly specialised and often incompatible legacy technology, work with a variety of scattered databases and supporting diverse data models and structures. Integrating data across different functional silos (without rearchitecting the entire system) is a long, horrifically complicated, unreliable process, requiring significant dedicated investment and extensive custom code.

Architectural inertia

Our Harvard colleagues Rebecca Henderson and Kim Clark argued in a 1990 paper that architectural innovations—ones that require changing the architecture between technological components—are a particular danger for established firms. The concept of architectural inertia—the resistance to adaptation—in turn, informs Clayton Christensen’s disruption theory. Critically, architectural inertia has been woven into the story of enterprise information technology over the past three or four decades.

Digital, AI-centered operating models?

Digital, AI-centered operating models challenge virtually every traditional managerial and operational assumption, forcing us to fundamentally rethink the nature of firms and of their management teams, their ability to grow, and the constraints on their impact and power.

New operating architecture

That old world of software is dead. With AI maturing fast, you need a distinctly different kind of operating architecture—one that is horizontally connected, designed to leverage an integrated foundation of data and to drive the rapid deployment of AI-powered applications, enabling exponential growth in scale, scope, and learning.

No more silos

Departing from the traditional, siloed structure of firms, which limits growth and responsiveness, prevents agile communication and coordination, localises decision-making, and traps technology and data in isolated pockets.

Collision

What we are experiencing is a collision between an exponential system and a saturated system—one that has reached its limits. Ignoring a system until it reaches critical mass is a recipe for catastrophe. Every organisation should get to work now to digitise and structure its processes, systems, and capabilities to accelerate operational scale, scope, and learning. There is no longer any rationale for waiting.

Reaching full potential

Networks and AI are reshaping the operational foundations of firms, enabling digital scale, scope, and learning and erasing deep-seated limits that have constrained firm growth and impact for hundreds of years. We have seen the emergence of firms that are designed and architected to release the full potential of digital networks, data, algorithms, and AI.

Moderna

For example, Moderna. Moderna is built on what the authors call an “AI factory”. The fundamental idea behind the AI factory is to industrialise the company’s approach to data, analytics, and artificial intelligence. An AI factory is the scalable decision engine that powers the digital operating model of the twenty-first-century firm. The AI factory creates a virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement.

Moderna has a data-centric operating model that extends well outside of the R&D process to encompass every aspect of the firm. The foundations of Moderna are an integrated data platform: a single, consistent “system of record” that embeds data originating from every functional speciality.

Data is processed in a systematic, standardised fashion, catalogued and centralised, cleansed, normalised and integrated, and exposed through APIs, which are available to Moderna teams to power new business applications.

The data platform forms the core of the firm, with an organisation consisting of scientists and managers overseeing it and harvesting its power. The technology underlying Moderna also shapes its organisational architecture and processes.

The Massachusetts General Hospital

MGH is a lot older than Moderna and is (in many ways) a traditional organisation. Much of its information technology infrastructure is dated and is limited by regulatory constraints and long-standing processes. MGH decided to transform on the fly to create the kind of horizontal, integrated information architecture that characterises the most efficient digital firms.

They created and deployed a structure that integrated and coordinated data, information, and activities across the vast organisation to manage the predicted rapid growth in Covid-19 cases. This information architecture enabled MGH to work on every problem identified by the planning process,

At the heart of the MGH crisis response architecture was its information system and data platform. The system enables centralised aggregation and accumulation of data and integrates information. The architecture is the key to coordinating and integrating the many different elements of a complex response with unprecedented agility.

Other companies

Other examples are Verizon, IKEA, Novartis, Amazon, Walmart, etc., all showing the value of rearchitecting the operating model on a digital and AI-enabled foundation where traditionally siloed enterprise software systems are being replaced by an integrated, cloud-based architecture.

Some lessons?from the book

• Operating architecture really matters.
• In company after company, processes, software applications, and data are still embedded in individual, largely autonomous and siloed organisational units.
• As AI enables more of the operating processes in complex corporations, the way it is embedded and architected in the broader operating model becomes increasingly critical.
• A firm’s operating architecture has become a strategic consideration that should be thought through at the most senior levels.
• Traditional IT custom-built process takes orders of magnitude more time and cost and becomes a nightmare to maintain and update.
• Data platforms, and the organisations that work with them, should avoid siloed structures and instead be designed modularly.
• The design of interfaces is critical in ensuring modularity in code and organisation.
• Dealing with operational complexity is the goal of many managerial and administrative systems developed over the past century, from the assembly line to the multidivisional company structure.
• That complexity becomes the downfall of traditional organisations, increasing operational costs and decreasing service levels.
• Runtime” is going to shape all of what we do.
• An AI-centric firm is not defined by the sophistication of any individual algorithm it deploys but by the structure and processes that enable the quick deployment of many AI solutions, each solving a real business problem.
• AI is becoming the universal engine of execution.
• AI is becoming the new operational foundation of business—the core of a company’s operating model, defining how the company drives the execution of tasks.
• When a business is driven by AI, software instructions and algorithms make up the critical path in the way the firm delivers value.
• The new risks are privacy and cybersecurity.
• A digital operating model also fundamentally changes the architecture of the firm. Beyond removing human bottlenecks, digital technologies are intrinsically modular and can easily enable business connections.
• When fully digitised, a process can easily be plugged into an external network of partners and providers or even into external communities of individuals to provide additional, complementary value.
• Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide or even automate a variety of operational actions. This is what enables the superior scale, scope, and learning capacity of the digital firm.
• Have a common foundation of data inputs, software technology, and algorithms.

More than technology

This transformation is about more than technology; it’s about the need to become a different kind of company. Rearchitecting how the firm works and changing the way it gathers and uses data, reacts to information, makes operating decisions, and executes operating tasks. Ultimately, AI-powered transformation can provide opportunities for any organisation if it makes the required commitments and investments.

Rethinking the firm

The value of a firm is shaped by two concepts. The first is the firm’s business model, defined as the way the firm promises to create and capture value. The second is the firm’s operating model, defined as the way the firm delivers value to its customers. In a fully digitised business, the options are much broader because value creation and capture can be separated much more easily and often come from different stakeholders. For example, most of Google’s services are free to users, and the company captures value from advertisers across its product portfolio.

The critical path in the delivery of value

A digital firm transforms the critical path in the delivery of value. By deploying a fundamentally new kind of operating model, this new type of firm is reaching new levels of scalability, achieving a vastly broader scope, and learning and adapting at a much faster rate than a traditional firm. Removing human and organisational bottlenecks from the critical path has a huge impact on the nature of the company’s operating model.

Essential AI factory components?

• Data pipeline: This process gathers, inputs, cleans, integrates, processes, and safeguards data in a systematic, sustainable, and scalable way.
• Experimentation platform: This is the mechanism through which hypotheses regarding new prediction and decision algorithms. For example, Ant Financial’s operating model is a sophisticated experimentation platform that runs hundreds of experiments daily, enabling the company to learn and understand the opportunities and risks provided by new features and products.
• Infrastructure: These systems embed the pipeline in a consistent and componentised software and computing infrastructure. Ultimately, the data, software, and connectivity underlying an AI factory must reside within a secure, robust, and scalable computational infrastructure.
• Governance and security: Building a state-of-the-art AI factory with a well-designed data platform improves the organisation’s ability to focus on the crucial challenges of data governance and security. As part of the essential data governance challenge, carefully defining clear and secure APIs is essential to the AI factory.

Do not underestimate data

Many incumbent businesses that are attempting to build AI factories find that the data they possess is fragmented, incomplete, and often siloed within divisions and disparate IT systems. And after the data is gathered, much work remains to be done in cleaning, normalising, and integrating it. Executives at these companies consistently underestimate the challenge and the urgency of the investment they face in cleaning and integrating their data across the enterprise so that they can build an effective AI factory.

Digital firms

Rather than rest on a traditional organisational model and operate through a variety of specialised and siloed organisational processes, digital firms rest on an integrated, highly modular digital foundation. An AI-driven company is accustomed to ongoing transformation. It is about fundamentally changing the core of the company by building a data-centric operating architecture supported by an agile organisation that enables ongoing change.

Four Principles for Transformation

1. One Strategy The first essential principle in transformation is to develop strategic clarity and commitment. The goals should be stated clearly, as in building an integrated data platform or organising as agile teams.
2. Architectural Clarity Second, it’s critical to bring clarity to the technical goals of the transformation. Everyone must understand what you want your future operating architecture to look like.
3. Capability Foundations The most obvious challenge in building an AI-centered firm is to grow a deep foundation of capability in software, data sciences, and advanced analytics.
4. Clear, Multidisciplinary Governance

AgilePoint makes a lot sense

Agilepoint is a codeless adaptive enterprise process automation platform. Our philosophy is that all code creates future choke points, ultimately leading to system collapse.

That is why our platform is completely codeless. Allowing you to transform your business model by simplifying the processes that fundamentally underpin the applications you are running. Making your organisation faster, more resilient and fully adaptive.

We save our clients?$ 32 million?or an ROI of over 400% on average. For more of our amazing result metrics, see?https://www.agilepoint.com/total-economic-impact-agilepoint ?