Enhancing DevOps with AI. Empowering every team.

Unifying the organization in every industry.

The history. Keeping up with consumer and producer demand has never been more important.

DevOps or “Development Operations” teams go the extra mile to promote collaboration between the Development, Operations, and the Business teams. DevOps staff or a dedicated Site Reliability Engineering (SRE) team monitor data to stay ahead of reliability issues and feed insights back into the development cycle. Most top firms have DevOps to thank for much of their innovation.

The reality. Beyond a set of tools, however, DevOps is a full-scale philosophy of work culture. Most organizations struggle to nurture this ethos.

Surveys suggest that 96% of organizations do not consider their systems to be at the expert level in terms of continuous delivery, deployment or monitoring1. Effectiveness remains a challenge in many cases.

The way forward. There is a need to rigorously unpack relationships between alerts and to engage the right team and person for the right job at the right time.

This must be achieved in globally-distributed teams in many cases. Not to mention the many teams not even currently represented in the status quo.

What’s more, these events must happen before there is any negative business impact. There is also a need to isolate positive impacts that can be learned from. Finally, there must be a direct route to management for rigorous decision making.

AIOps is a powerful innovation that combines AI with Operations.

In spite of this potential, the impact on organizations remain limited, in part due to the challenges of reconciling algorithms with the philosophy of minimizing siloes in practice. The absence of some teams from DevOps and AIOps, such as human resources, research and development or staff training are other constraints. Algorithmic biases and fairness shortcomings have further potential to put teams and entire organizations at risk of faulty decisions.

Data and AI can revolutionize all organizations and economies.

An innovative approach must unlock organizations of all types, while being tailored to their individual contexts. It would thus be able to unpack incentives within and across organizations, as well as industries and the economy. It is critical that AI improve the human condition and have a positive impact on society.

There is a solution.

Machine Learning X Doing DevOps. Next-level AI.

Welcome to the next level. (2021).


Machine Learning X Doing DevOps.

Next-level AIOps for DevOps Teams

Machine Learning X Doing DevOps is an AIOps approach to handle operational issues and spans all aspects of your strategy and business. By integrating AI with economic and social science, the approach is compatible with existing DevOps and AIOps approaches and does not require overhauling existing processes; just adding AI throughout.

Instead of scaling human labor with technology to make operations efficient, AI means that organizations can free labor to focus on mission-critical tasks and empower them to build better products and services to enrich consumer experiences.

Organizations can now scale by applying AI to observability and monitoring data and generating alerts into insights at the edge where datasets are large to maximize sample sizes. Machine Learning by Doing DevOps also identifies root causes and impacts, and prescribes potential solutions based on previous resolution steps and feedback in a virtual space. Here, the right people are notified of what action to take, with complex team structures, engagement methods, on-call schedules, the need to stay in sync, and escalation paths all controlled for throughout the life cycle of the incident, even when staff are global. On resolution, a streamlined post-mortem uses similar events and AI to identify future concerns.

The goal of AI is to free humans to focus on being creative in obsessing over their users. The future of Development and Operations and AIOps is Machine Learning by Doing DevOps.

Machine Learning X Doing DevOps. The next generation of DevOps and AIOps.

DevOps and AIOps are not a mere set of tools that enable development and operations teams to operate in harmony. No tool can do this singlehandedly. These are entire philosophies and practices to increase organizational effectiveness. For the benefits to be felt, DevOps must live and breathe throughout the organization. This is easier said than done, especially in large, complex and secretive organizations, where breaking down silos is a challenge unto itself

Recent progress may feel premature for something as big as the management of the organization. Leaders understand that an understanding of economics and social science would help ground technical work in the relevant incentives, but software engineering is not part of the social science toolkit. Even within technology firms, DevOps and AIOps must transcend development and operations and touch new teams such as human resources, marketing, education, and many more.

It is up to us all to make the world a better place, starting with insights for your organization.

Introducing the next-generation of AI, designed around the fundamental question of what it means to be human

Together, we will inspire change in the world, by first introducing your organization to its true potential

Next-level AI that meets or exceeds decision maker imaginations, tailored to unique environments and needs, for every client sector of the economy.



Kweku Opoku-Agyemang, Ph.D.

Kweku Opoku-Agyemang, Ph.D., is former faculty at the University of California, Berkeley in development economics and a former computer science researcher at Cornell University. He has advised Google scientists, given talks at Facebook, presented to government officials from 12 countries and others.

A former session Chair at the Canadian Economic Association, Kweku believes that his next-generation Machine Learning x Doing approach can help organizations and countries to do better by their people by meeting or exceeding their potential. He is based in Toronto, Canada.