As AI gains traction, many organisations are piloting AI use cases successfully, but stalling when it comes to enterprise-wide implementations. Closing the gap between pilot and production value requires strategic planning, an AI-specific implementation framework and the right tools.
So says Tsholo Madi, Agile Business Analyst at eBlocks Software, a software engineering specialist focusing on data, AI and DevSecOps.
Madi says many pilots fail to go enterprise-wide not because of the AI technology, but because organisations are challenged in embedding it into processes and workflows. “The problem lies in a lack of ownership and accountability, governance and security, workflow management and change management, and due to difficulties in measuring the business value of the implementation,” she says.
Madi says the people element plays a significant role in operationalising AI too. Employees commonly have concerns about job displacement, and a limited understanding of AI’s role in current workflows.
“AI is different from many other disruptive technologies that emerged in the past – it comes with a lot of misconceptions,” she says. “There is still some fear, resistance and questions, along with a lot of excitement about what AI could do to transform operations. Used well, it can change the landscape to allow you to move faster, realise value faster and improve productivity, but it's still unknown territory.”
To help organisations close these gaps and operationalise AI, eBlocks has introduced its OnTrack AI implementation model, which uses AI agents to help embed AI into organisations’ software delivery processes and workflows.
Madi says: “The eBlocks approach – before we even recommend any particular AI tool – is to understand the business, its workflows and challenges, and identify bottlenecks and challenges where AI could improve processes.”
Deon Thomas, CEO of eBlocks Software, says: “Many organisations have successfully experimented with AI through pilot projects, but struggle to convert those initiatives into measurable enterprise value. OnTrack AI was developed specifically to bridge that gap by combining a structured implementation approach with a platform that embeds specialised AI agents directly into business and software delivery processes. What makes OnTrack AI different is that it does not treat AI as a standalone tool. It enables AI agents to participate within governed workflows, creating traceability, accountability and human oversight throughout the delivery life cycle. This allows organisations to accelerate delivery, improve consistency and quality, and operationalise AI in a controlled, production-ready environment.”
Madi adds: “Designed after extensive research and experimentation, our OnTrack AI implementation model has structured processes including kickoff, discover and define, and the delivery phase using agile implementation processes to ensure AI is successfully embedded. Because our OnTrack AI platform embeds AI into software delivery processes, it creates traceability, consistency and visibility, while keeping humans in the loop and accountable.
“OnTrack was built specifically to address the delivery challenge, because traditional software development delivery cycles can run long. We wanted to speed them up with AI, so we built the platform to improve agile software delivery and help onboard AI into the way organisations work, with AI agents as partners in everything from onboarding to quality assurance,” she says.
“Through OnTrack AI, eBlocks aims to help organisations move from isolated AI experimentation to more repeatable and governed and production-ready delivery practices.”
Thomas concludes: “Ultimately, successful AI adoption is not about deploying more technology; it is about embedding AI into the way people work. Our focus is helping organisations move from experimentation to repeatable, measurable business outcomes."
To explore the principles behind AI-first software delivery and the OnTrack AI implementation framework, visit OnTrack AI – AI-First Delivery Transformation.
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