This article is based on a recent conversation with Joe David, Vice President of Professional Services at BCforward, about what AI readiness really means for enterprise organizations.
Watch the full interview below for Joe’s perspective on how client conversations are changing, where companies are getting stuck, and what leaders should consider before moving AI initiatives into production.
AI is no longer just a topic of exploration for enterprise leaders. It has quickly moved from an emerging opportunity to a business expectation.
A year ago, many organizations were asking where they should pilot AI, how they should experiment, and which tools they should test first. Today, the conversation has changed. Leaders are asking harder questions: Where is the return on investment? Why are we not further along? How do we move AI from an isolated pilot into something that actually changes how work gets done?
That shift matters.
According to Joe David, Vice President of Professional Services at BCforward, client conversations around AI have moved from curiosity to accountability. Organizations are under growing pressure to show that AI can drive cost savings, efficiency, productivity, and measurable business value. But getting there requires more than access to a model, platform, or technical resource.
It requires AI readiness.
AI Has Become a Business Capability
One of the biggest changes in the enterprise AI conversation is that AI is no longer being treated as a standalone technology initiative. It is becoming a core business capability.
That means organizations need to think beyond tools and talent alone. While both are important, they are only part of the equation. Companies also need a practical delivery methodology, a clear operating model, and the discipline to embed AI into business processes in a repeatable and governed way.
For professional services teams, this changes the nature of the support clients need. Organizations are not simply asking for AI developers, data scientists, or technical resources. They need partners who can bring a clear point of view, assess readiness, identify realistic use cases, and help move initiatives from experimentation into execution.
The real value is helping organizations move from isolated pilots to measurable AI use cases that can scale.
That is where many companies are getting stuck.
A pilot can look impressive in a controlled environment. But if it is not connected to a real business process, supported by reliable data, governed appropriately, and measured against a defined outcome, it may never make it into production.
AI Readiness Is Not the Same as Tool Access
Many companies already have access to AI tools. They may be evaluating multiple providers, testing different platforms, or allowing teams to experiment with generative AI in limited ways.
But access does not equal readiness.
At the enterprise level, AI readiness means having the ability to deploy and scale AI in a way that delivers business value. That requires several foundational elements to be in place, including data, governance, workflows, talent, and alignment to business outcomes.
Each of these areas needs to be assessed before implementation begins. Each one also needs a plan for closing gaps.
Without that foundation, companies risk building AI solutions on unstable ground.
For example, a company may want to build an AI solution that helps a finance team answer common operational questions, such as:
- How much budget do I have left?
- How much did we spend last month?
- Where are we tracking against plan?
On the surface, that can be a strong use case. It supports a shared service function, reduces repetitive requests, and helps a resource-constrained team respond faster.
But the implementation is not as simple as building an AI agent and turning it loose.
“Organizations often think the challenge is the model or the tool itself. But once they start applying AI, they realize the real issue is the data: it’s fragmented, inconsistent, and not governed.”
– Joe David, Vice President of Professional Services, BCforward
If the finance team has to reconcile two separate systems, use a master spreadsheet, and rely on custom formulas just to answer those questions today, the AI solution will inherit those same problems.
The issue is not the intelligence of the model.
The issue is the condition of the underlying data and process.
Governance also becomes critical. Should everyone in the organization be able to query budget information? Should access be based on role, department, region, or approval level? What data can the AI system retrieve, and what should remain restricted?
These are readiness questions that need to be answered before implementation.
Start With the Business Problem, Not the Tool
For many organizations, AI conversations still start with the technology. Which platform should we buy? Which model should we use? Do we need an AI agent? Should we be using generative AI, retrieval-based AI, workflow automation, or analytics?
Those questions matter, but they should not come first.
The more important question is: What problem are we trying to solve?
“The real value is helping clients move from isolated pilots to repeatable, governed, measurable use cases that change how work gets done.”
– Joe David, Vice President of Professional Services, BCforward
Tool selection is often the easiest part of the process. The harder and more important work is defining the business use case, understanding the workflow, identifying the data required, and determining how AI will create a better outcome.
Once the business problem is clear, the tool conversation becomes much more focused.
If employees are spending too much time searching across policies, contracts, knowledge articles, or internal documentation, the organization may need enterprise search or retrieval-based AI.
If the issue is repetitive back-office work, such as intake, routing, status updates, or document review, the better fit may be workflow automation, intelligent document processing, or agentic AI.
If the goal is to help leaders analyze trends or surface insights from operational data, the organization may need analytics, data science, or decision-support capabilities.
The use case should narrow the field.
Instead of asking, “What AI tool should we buy?” leaders should ask, “Given this business problem, what capabilities do we actually need?”
That shift helps prevent scattered experimentation and reduces the risk of investing in tools that do not match the organization’s real operational needs.
In the full interview, Joe explains how leaders can move beyond the question of “What AI tool should we buy?” and instead focus on the capabilities required to solve a specific business problem.
The Biggest AI Barrier Is Often Data and Process
One of the most common patterns BCforward sees in client conversations is that organizations assume the biggest barrier will be the AI model or tool. But once they begin applying AI to real business environments, they often discover the deeper issue is their data.
The data may be fragmented across systems. It may be inconsistent from one department to another. It may lack clear ownership. It may not be governed in a way that supports secure, accurate, role-based AI use.
Processes can create similar challenges.
If the current workflow depends on manual handoffs, spreadsheets, tribal knowledge, or undocumented exceptions, AI will not magically fix the issue. In many cases, it will expose the problem faster.
This is why readiness work matters. Before an organization can scale AI, it needs to understand whether the process is stable enough, whether the data is reliable enough, and whether the governance structure is clear enough to support implementation.
AI can accelerate good processes. It can also amplify broken ones.
Questions Executives Should Ask Before Greenlighting AI
Before approving an AI initiative, enterprise leaders should pause and ask a practical set of readiness questions.
- What business problem are we trying to solve?
- What measurable outcome do we expect from this initiative?
- Which workflow will AI support or change?
- What data does the solution need to access?
- Is that data accurate, consistent, governed, and accessible?
- Who should be allowed to use the AI solution, and what information should they be allowed to see?
- Who owns the process today, and who will own the AI-enabled version of it?
- How will we measure success?
- What risks need to be addressed before this moves into production?
These questions help separate promising AI initiatives from ideas that are not ready to scale.
They also help leaders avoid one of the biggest traps in enterprise AI: launching a pilot before the organization understands what needs to happen for that pilot to become operational.
One Sign an Organization May Not Be Ready for AI
A clear warning sign is when an organization cannot define the use case, the data source, the workflow owner, and the success metric.
If teams are excited about AI but cannot explain what business problem it solves, who will use it, what data it will rely on, or how success will be measured, the initiative is likely not ready for implementation.
Another warning sign is heavy dependence on manual data reconciliation. If the answers AI is expected to provide currently require multiple systems, spreadsheets, manual formulas, and human interpretation, the organization may need to address the data and process foundation first.
This does not mean the company should stop pursuing AI. It means the first phase of the work should focus on readiness.
Moving From AI Planning to AI Execution
The organizations that succeed with AI will not be the ones that simply adopt the most tools. They will be the ones that connect AI to meaningful business problems, prepare their data and workflows, establish governance, and measure outcomes clearly.
AI readiness is not about slowing innovation down. It is about making innovation more likely to work.
For enterprise leaders, the opportunity is significant. AI can improve productivity, reduce manual effort, support better decisions, and create new ways of working. But those outcomes do not happen by accident.
They require a strong foundation.
As companies move from AI experimentation to accountability, the focus has to shift from “What can we test?” to “What can we operationalize?”
That is where AI becomes more than a technology initiative.
It becomes a business capability.
For more of Joe’s perspective on AI readiness, data governance, workflow alignment, and moving AI from pilot to production, listen to the full interview.
Need help assessing where your organization stands? BCforward helps enterprise teams evaluate AI readiness, identify practical use cases, and move from planning to governed, measurable execution.
