jaam automation Launches M. Platform to Scale Agentic AI and Bridge the Business Productivity Gap

London-based jaam automation has launched M., an AI platform designed to bridge the gap between human workflows and automated agents. By providing a "Context Engine" to coordinate disparate systems, the platform addresses the critical challenge of scaling AI beyond individual productivity to drive meaningful enterprise-wide impact and measurable financial returns for fintech and professional services firms.

What was announced

M. by jaam, launched on 29th September 2026, is a new platform built for "Agentic Work Management." The solution arrives at a time when 88% of organisations have adopted AI, yet only 7% have managed to fully scale the technology across their business. This gap is reflected in financial performance; while 80% of employees report that AI has improved their individual productivity, only 37% of organisations have seen a positive impact on their EBIT.

The platform functions by coordinating work across people, existing systems, and AI agents. At its core is the Context Engine, which aggregates information about an organisation’s specific knowledge and live workflows. This allows AI to understand the broader business environment, learning from decisions and outcomes to improve future automation. The platform is designed to integrate with the Microsoft ecosystem, including Azure AI, Copilot, and Power Platform, capitalising on a market where active Microsoft 365 agents have grown 15x in a single year.

The deployment of M. follows a structured five-stage methodology to move organisations from analysis to full automation:

• Analyse: Understanding requirements, information, and existing processes.
• Build: Turning that understanding into the necessary structures for work.
• Work: Supporting live operations with the correct context and information.
• Intelligence: Monitoring progress and flagging risks where human action is needed.
• Optimisation: Identifying specific areas where AI and agents can further improve the process.

"Many businesses have adopted AI incredibly quickly but are struggling to make it work across the business. Every organisation has its own processes, systems, people and ways of doing things. If we’re going to ask AI to take on more of that work, it needs to understand how the business actually works."

Andrew Murphy, Chief Strategy Officer and Co-Founder at jaam automation.

The companies involved

jaam automation is a London-based technology firm that specialises in the orchestration of complex business processes. The company focuses on the intersection of human decision-making and machine efficiency, aiming to solve the fragmentation that occurs when work is spread across multiple applications and teams. By launching M., jaam automation is positioning itself within the emerging Agentic Work Management category, a sector defined by the need to manage autonomous AI agents alongside traditional human workforces.

Oaka Studio, represented in this launch by Co-Founder Lucy Bourne, operates as a strategic partner in the AI and digital transformation space. The firm provides expertise in how organisations can practically and securely introduce new technologies while maintaining governance. Oaka Studio’s involvement highlights the collaborative nature of the AI ecosystem, particularly for businesses heavily invested in Microsoft technologies. Both companies operate in a market where the primary challenge has shifted from basic AI adoption to the sophisticated coordination of "Universal Orchestration"—a term identified by Gartner as a primary enterprise hurdle in early 2026 as businesses attempt to manage bots and people across diverse vendor environments.

What this means

The launch of M. by jaam signals a necessary shift in the AI narrative from "individual tools" to "enterprise orchestration." For too long, AI has been treated as a series of isolated plugins, leading to a productivity paradox where individual efficiency fails to translate to the bottom line. By focusing on "context," jaam is targeting the primary reason AI agents fail in complex environments: they lack the tribal knowledge and process awareness that human workers possess. This move puts significant pressure on traditional Business Process Management (BPM) providers, who must now prove they can handle autonomous agents rather than just static, human-led workflows. The industry is moving toward a reality where the value lies not in the AI itself, but in the glue that connects it to the business.