“The technology is rarely the hardest part. The hard part is connecting AI to real-world workflows, responsible ownership, high-quality data, security controls, and measurable outcomes.”
On the other hand, Deloitte The current state of AI in the enterprise Although this report primarily focuses on large organizations, many of the issues identified in the report are increasingly being faced by midsize businesses as they move AI from experimentation to day-to-day operations. The report explains that moving from pilot to production is “arguably the most important step in capturing value from AI, yet this is where many companies get stuck.”
Nitin Mittal, global AI leader at Deloitte, says companies are increasingly focused on translating their AI ambitions into operational impact. “Across the enterprise, we are seeing significant ambition around AI. Organizations are starting to pivot from experimentation to integrating AI into the core of their business with a focus on scale and impact,” says Mittal.
“As organizations seek to unlock the full value of AI, leaders must consciously weave AI into the fabric of business workflows to realize enterprise value through a better combination of human and machine intelligence.”
Deloitte believes much of that difficulty stems from what it calls the “proof-of-concept trap.” While pilots can be developed quickly with small teams, cleansed data, and isolated environments, production deployments require integration, security reviews, compliance checks, monitoring, and ongoing maintenance.
Organizations looking to move AI into production often discover that they need to address broader operational gaps, such as data quality, governance, ownership, and change management, before scaling AI. This is one reason why many companies are shifting their focus from individual AI tools to the systems, processes, and governance structures that surround them.
According to Heaton, successful implementation starts with understanding how work is done today and identifying where AI can truly improve outcomes.
Paul Heaton, co-founder and CEO of Microsoft-focused cloud and AI consultancy cubesys.
“The right question is not, ‘What can AI do?’ – it’s, ‘What workflows or service experiences need to change, and can AI help?’” he says.
Workflow redesign
Similar patterns are occurring across industries. Early experiments often focused on standalone use cases, such as summarizing documents, drafting content, and answering internal questions. Organizations are increasingly looking at how AI can support end-to-end processes, improve customer experiences, and enable employees to work more efficiently.
In reality, workflows often require more than just automating existing tasks.
According to Deloitte research, many organizations have yet to make that change. Despite the promise that AI will automate a significant portion of jobs, 84% of companies surveyed have not redesigned jobs around AI capabilities. This report suggests that realizing value from AI often requires organizations to rethink their operating models and the way they work, rather than simply layering new tools on top of existing processes.
It also requires clear ownership.
One of the recurring themes when deploying enterprise AI across an organization is the need for responsible business leaders, clear success criteria, and clear oversight. Without these foundations, organizations may end up accumulating disconnected pilots that generate interest but provide limited operational value.
“An AI pilot without a path to production is just a play,” Heaton says.
For cubesys, these lessons came through first-hand experience.
Before bringing the AI Forge methodology to market, the company adopted a framework internally called a “client zero” approach.
The objective was to understand first-hand how AI can be integrated into daily operations, how governance should be applied, and what organizational changes are needed to support implementation.
This experience reinforced the view that value comes from redesigning workflows based on specific business outcomes, rather than deploying AI as a standalone tool with task-specific agents, clear accountability, and human oversight built into the process from the beginning.
This process has extended far beyond testing technology. The company used AI to reimagine service delivery, knowledge management, and internal operations, while establishing governance controls and determining how employees interact with AI within existing workflows.
“At cubesys, AI became relevant when we stopped treating it as a side experiment and started looking at how it could reshape the way we deliver services and support our clients,” says Heaton.
That experience helped shape the company’s view that AI adoption is fundamentally an organizational issue, rather than a purely technical one.
This experience formed the basis for AI Forge, cubesys’ structured approach to operationalizing AI. Rather than focusing on tools, AI Forge combines workflow mapping, governance design, and purpose-built AI agents to create repeatable and scalable models for incorporating AI into daily business operations.
Governance as a catalyst for growth
As AI moves closer to core business operations, governance is emerging as a central consideration. Organizations need confidence that information is properly accessed, decisions are understood and considered, and accountability is clear when AI is involved in processes.
Deloitte’s report describes governance as a “catalyst for responsible growth” and suggests that organizations that put governance on the back burner may struggle to move AI from pilot to production.
Heaton agrees that governance is most effective when it is built directly into day-to-day operations, rather than added as a compliance layer afterwards.
“Without governance, AI will remain locked in informal experimentation,” he says.
“Governance creates trust. Governance tells people where they can and cannot use AI, who will check the output, and who will be responsible.”
This thinking influenced the development of cubesys’ AI agent contracts, which define clear operational boundaries such as data access, decision-making rights, escalation paths, and human oversight for each AI agent or use case. The aim is to make governance practical and repeatable rather than bureaucratic.
Deloitte’s research and early adopters both point to the same conclusion.
“The next step is moving from AI curiosity to AI responsibility,” Heaton says.
“Boards will stop asking how many pilots there are and start asking how AI is changing business performance, including cost, quality of service, throughput, risk, and customer experience.”
For Australian organizations, escaping the AI pilot trap is becoming less about finding new technology and more about building the systems, governance and skills needed to use it effectively.
The organizations making the greatest progress are often not the ones running the greatest number of pilots. They are developing repeatable approaches to integrating AI into workflows, governance structures, and decision-making processes.
“The winner is not the organization with the most pilots,” Heaton said.
“They will be the organizations with the best systems to turn useful pilots into managed, adopted, and measurable business capabilities.”
To learn more about how cubesys helps organizations move beyond AI pilots, visit: Cubesys AI Forge.
