This article is based on a recent conversation with Joe David, Vice President of Professional Services at BCforward, about the shift from generative AI to agentic AI and what enterprise leaders need to do to prepare their organisations for AI-enabled execution.
Watch the full interview below for Joe’s insights on AI readiness, governance, and the practical steps organisations can take to move from AI experimentation to scalable business outcomes.
Generative AI helped organizations imagine what was possible. It made it easier to create content, summarize information, analyze data, and move faster. For many teams, that was the first practical entry point into enterprise AI. But the conversation is already moving forward.
As Joe David, VP of Professional Services at BCforward, explained, enterprise leaders are now asking a more advanced question: what happens when AI does not just support work, but begins to take action inside business workflows?
This is the shift from generative AI to agentic AI.
Generative AI may summarize a weekly project status report. An agentic system can go further by pulling data from project management tools, flagging risks, notifying stakeholders, and recommending next steps. In that environment, AI is no longer just helping people move faster. It is becoming part of how work is executed.
This shift creates real opportunity. It also raises the stakes.
What Agentic AI Really Means
Agentic AI can sound complex, but the core idea is straightforward: it is the movement from systems that support decisions to systems that act on decisions within defined boundaries.
Instead of only saying, “Here is what you should do,” an agentic system can say, “I have taken the next steps, and here is the result.”
In a finance workflow, for example, a traditional AI tool might flag an out-of-policy expense. An agentic system could route that expense for approval, notify the manager, and update the system once the issue is resolved. This is where AI becomes embedded into operations. It is not just producing an answer or creating a summary. It is participating in the flow of work.
For leaders, this changes the conversation. The question is no longer limited to whether a tool can generate useful output. The bigger issue is whether the organization is ready for AI-enabled action.
Why Agentic AI Requires Stronger Governance
“The more AI can act, the more intentional organizations need to be about oversight.
“Controls and ownership need to be clear right from the start.”
— Joe David, VP of Professional Services, BCforward
The more AI can do, the more important governance becomes.
When systems move from insight to action, organizations are giving away a degree of autonomy. That does not mean giving up control completely, but it does mean leaders need to be much more intentional about visibility, decision logic, human oversight, and accountability. Without those controls, risk can move quickly.
In a supply chain workflow, an AI agent might automatically reorder inventory based on forecast data. If that forecast is flawed, the organization could end up with excess inventory or stockouts before a human catches the issue. The concern is not only whether the system made a mistake. It is whether the organization had the right guardrails in place to detect, prevent, or correct that mistake before it affected the business.
As David put it, “The more AI can act, the more intentional organizations need to be about oversight. Controls and ownership need to be clear right from the start.”
This is a strong way for leaders to think about agentic AI. Governance should not be treated as an afterthought or a compliance layer added at the end. It needs to be part of the foundation from the beginning.
The Strategy-to-Execution Gap Is Where AI Initiatives Stall
Many organizations have an AI vision. Fewer have a clear execution model. This gap is where initiatives often lose momentum.
The issue is usually not a lack of interest or ambition. It is that strategic goals do not get translated into ownership, process changes, governance requirements, success measures, and adoption plans.
Ownership is especially important. Someone has to own the business outcome, not just the technology. When that ownership is unclear, decisions slow down, priorities compete, and AI starts to feel like a side experiment instead of an operating model change.
A customer service initiative is a useful example. A company may decide that AI-driven support is a priority. But if no one owns bot training, escalation paths, performance metrics, or adoption, the pilot may never scale.
The vision may be strong. The execution details are what determine whether the business sees value.
What a Professional Services Partner Should Bring to AI Execution
A strong professional services partner should help turn an AI idea into an executable plan.
This work starts by clarifying the business outcome. From there, the partner should help define workstreams, identify roles, create a realistic timeline, and ensure governance is in place before the effort scales.
The best partners also connect the technical work to the operating model. That means answering practical questions before implementation moves too far:
- How will the workflow change?
- Who owns the decision?
- How will teams be trained?
- How will adoption be supported?
- How will success be measured?
This is where execution discipline matters. A promising use case is not enough on its own. Organizations need a governed delivery plan with clear accountability, adoption support, and measurable outcomes.
Without this structure, even strong AI ideas can stay trapped in pilot mode.
How BCforward Supports the Path From Planning to Execution
BCforward’s model is designed to support the full path from planning through execution. That matters because AI success depends on more than technical implementation alone. The work typically requires project leadership, change enablement, and digital delivery moving together.
Project management creates the delivery structure. This includes scope, cadence, risks, dependencies, governance, and stakeholder alignment. This keeps the effort coordinated and accountable.
Organizational change management focuses on adoption. Teams need to understand what is changing, why it matters, and how new ways of working will affect their day-to-day responsibilities. Without that support, adoption is often assumed rather than achieved.
Digital delivery handles the technical side: building, integrating, and scaling AI-enabled solutions into the systems and workflows where they need to operate. Consider an organization modernizing IT support. The digital team may implement AI-assisted ticketing. The project management team drives governance and stakeholder alignment. The change management team helps support teams adopt the new process. The result is not just a new tool. It is a better operating model, with faster resolution times, stronger visibility, and a more consistent user experience.
What Leaders Should Start and Stop Doing Now
Agentic AI raises the bar for enterprise readiness. Leaders do not need to solve everything at once, but they do need to move with more discipline.
Start Doing
- Define outcome-based use cases.
- Design AI-enabled workflows, not just tool deployments.
- Establish governance early.
- Invest in enablement and change management.
- Treat AI as part of operating model evolution.
Stop Doing
- Running disconnected pilots with no path to scale.
- Over-focusing on tools instead of business outcomes.
- Assuming adoption will happen naturally.
- Treating governance as something to add later.
- Moving forward before data, process, and ownership are ready.
The most successful organizations will not be the ones that experiment endlessly. They will be the ones that build the foundation to scale responsibly.
Readiness Is the Foundation for Execution
Execution only works when the foundation is strong enough to support it. This is why BCforward builds a structured AI readiness assessment into the discovery process. The assessment evaluates whether an organization is prepared to move forward with AI across several critical areas, including data readiness, process readiness, technology alignment, and organizational readiness.
Data readiness includes completeness, structure, accessibility, and governance. Process readiness looks at whether workflows are stable enough to support automation or AI-enabled action. Technology alignment examines whether systems can integrate in a practical way. Organizational readiness evaluates ownership, adoption capacity, and the ability to support change. This can materially change the direction of a project.
For example, a client may want to automate reporting with AI. At first, the focus may be on the model. But a readiness assessment may reveal that the data is fragmented, inconsistent, or poorly governed. In that case, the smarter move is to standardize and govern the data first, so the eventual AI solution can deliver the expected insights.
This is the difference between chasing the tool and preparing the business to use it well.
The Next Phase of AI Belongs to Organizations That Can Execute
The shift from generative AI to agentic AI is not just a technology shift. It is an operating model shift.
As AI systems become more action-oriented, leaders will need to think differently about governance, ownership, workflow design, adoption, and measurement. The opportunity is significant, but so is the responsibility.
Enterprise AI will not scale because a company launches more pilots. It will scale when organizations define the right use cases, prepare the foundation, support their people, and connect the work to measurable business outcomes. For leaders, the next step is not simply asking what AI can do. It is asking whether the organization is ready to put AI to work responsibly, effectively, and at scale.
Ready to Move From AI Planning to Execution?
BCforward helps organizations assess AI readiness, define outcome-based use cases, and build the delivery structure needed to move from experimentation to measurable impact.
If your organization is exploring generative AI, agentic AI, or AI-enabled workflow transformation, reach out to learn more about BCforward’s AI Readiness Assessment to identify where you are ready to move, where risk may exist, and what needs to be in place before you scale.
