Moving from AI Experiments to Company-Wide Process Integration

Are you struggling to move beyond random AI experiments? Wondering how to truly integrate AI into your core business processes? 

In this article, you'll discover a structured approach to scaling AI adoption and driving real transformation in your organization.

Why Moving Beyond AI Experimentation Is Necessary

Many companies have taken their first steps into AI implementation by providing employees access to ChatGPT or conducting initial experiments. While this represents important progress, it can also trap your company in experimentation that doesn't lead to meaningful change or progress.

To shift from random acts of AI to true integration into business processes, you must view AI as a core enabler of business growth and integrate it into core business processes.

“It's not about convincing your team to try AI,” Lauren Schiavone emphasizes. “It's about redesigning workflows so AI just naturally enhances what they already do. When AI is built into standard processes, that's when you will see real adoption and transformation.”

The journey to AI transformation requires systematic effort and careful attention to technical and human elements. Success comes from moving beyond random experimentation to structured implementation, supported by strong leadership, clear processes, and cultural adaptation.

The companies that do that best will leapfrog those that do not.

#1: How to Set Up an Effective AI Council

Establishing a well-functioning AI council is the foundation for successful AI implementation. This group drives AI transformation across the company, sets goals, defines pilots, creates roadmaps, and establishes policies.

First, ensure you have the right people in the room. An AI council filled only with senior leaders will generate strategic dialogue but little action. Instead, balance the team with action-oriented contributors, typically mid-level managers or practitioners who can own experiments and drive progress. Then, your senior leaders can function as executive sponsors to provide guidance and break down barriers across departments.

Next, ensure that council membership is a key part of each member's work plan, tied to performance goals, and supported by access to adequate resources, including necessary tools, budgets, and support.

Finally, make sure your council maintains a regular meeting cadence. Don't allow meetings to be consistently postponed or rescheduled because six months of delay is equivalent to years of lost progress in the rapidly evolving AI landscape.

#2: Ideation: How to Prioritize AI Use Cases

With a functioning AI council in place, the next step is identifying the right processes and use cases for AI implementation. Schiavone recommends starting with a comprehensive brainstorming session to document your key work processes—the essential activities that deliver value to your clients, customers, or consumers.

After identifying these processes, apply two critical filtering questions:

First, is the work process repeatable and completed regularly? Focus on tasks that occur frequently rather than one-off projects. 

Second, is the work process business-critical? The process should directly impact your organization's ability to deliver results and drive growth.

“AI needs to be seen as an enabler of your top priorities and not a distraction from them,” Schiavone emphasizes. “Experimenting in areas that aren't business-critical often leads to stalled efforts because they don't resonate with your leadership or drive meaningful outcomes.”

Once you've filtered your list through these questions, Schiavone recommends using a prioritization matrix to evaluate the remaining use case candidates. The matrix uses two axes: on the Y-axis, consider how much you dislike doing the tasks—look for activities that are repetitive, time-consuming, or drain team morale. On the X-axis, evaluate the potential impact—the ability to save time, reduce costs, or drive business outcomes if AI enhances the task.

For example, writing business proposals often emerges as a prime candidate for AI enhancement. It's a process that occurs regularly, is typically time-consuming, and directly impacts business success. Similarly, creating brand content represents another substantial opportunity, as it's both frequent and crucial to business success.

Schiavone shares her experience implementing AI for contract review: “When I left P&G, I had to learn all these new skills I didn't have before. One of those skills was reviewing contracts. I leveraged ChatGPT, and now I've created a custom GPT to help me review my contracts to help me understand what are areas where I should be concerned. It helps explain to me in high school language why I should be concerned, and then it offers up language that I should suggest instead.” This saves significant time and expense when she has to involve an attorney.

Pro Tip: Schiavone strongly advocates for leveraging existing tools whenever possible. For instance, if your organization already has a ChatGPT enterprise license, focus on maximizing that investment rather than introducing new tools. This approach simplifies both leadership approval and employee training.

#3: Implementation: How to Run AI Pilots

Moving from ideation to implementation requires careful pilot design. Schiavone emphasizes that these aren't casual experiments but strategic pilots intended to prove AI's potential impact and deliver quick wins to build momentum.

Start by assigning a dedicated member of the AI council to each pilot—someone passionate about the project who can drive it forward. Ensure they have the necessary resources, whether that's team support, tools, training, or funding. You should also assign an executive from the council who can advocate for the pilot, remove roadblocks, and communicate results to stakeholders.

Next, set measurable success criteria for your pilot: time saved, accuracy improvements, or increased closure rates. 

“Having these measurable outcomes ensures that you're objectively evaluating the experiment's success,” Schiavone explains. “You really want to make sure that you understand the baseline for the work process that you're trying to measure so, ultimately, you can understand ROI.”

For example, when measuring the success of AI-enhanced proposal writing, start by tracking time savings, then progress to monitoring improvement in proposal acceptance rates. For brand content creation, initial metrics might focus on production speed and volume while maintaining quality standards, then evolve to measuring engagement rates and lead generation.

Schiavone recommends getting pilots up and running within 30 days to avoid getting stuck in planning mode. “It's important to move fast and learn. You can pivot as you learn more.” 

As the pilot proceeds, monitor progress closely throughout a 90-day duration so you can gather feedback and iterate your approach rather than waiting until the end to evaluate results.

#4: How to Scale Successful AI Implementations

After validating success through pilots, the focus shifts to scaling these implementations across the organization. Schiavone outlines three critical steps for effective scaling.

First, understand what needs to be true to scale. This means identifying all requirements for successful expansion: which stakeholders need to be aligned, what resources are required, who needs training, and whether any policy updates are needed. This comprehensive assessment provides a clear roadmap for scaling efforts.

Second, build a compelling case for leadership buy-in. To get leaders on board, you want to focus on demonstrating the pilot's business impact and potential value at scale. This is where careful measurement during the pilot phase becomes crucial—being able to articulate concrete improvements in time, cost savings, or better outcomes strengthens your case significantly.

The final step involves developing comprehensive training materials and optimization processes. Document how your new AI-enhanced processes work and create simple guides and templates that make adoption as straightforward as possible. “The more simple and user-friendly, the easier it's going to be for folks to adopt,” Schiavone emphasizes.

5 Shifts Necessary to Support an AI-Driven Culture

The most challenging aspect of AI transformation often lies in managing cultural change. Schiavone believes that the culture existing in most corporations today won't support success in an AI-driven future. She outlines several essential cultural shifts organizations must embrace.

Letting Go of Ego

“The pace of AI advancement simply means no one can be the expert at everything,” Schiavone explains. Leaders must abandon the expectation of having all the answers and instead embrace humility and curiosity. This shift creates an environment where learning and adaptation become continuous processes rather than occasional events.

Shifting from Competition to Collaboration

Traditional workplace competition can hinder AI adoption. Success comes when teams openly share their discoveries, learnings, and insights. This collective intelligence becomes a powerful driver of innovation and improvement.

Fostering Continuous Learning

Organizations need to embed continuous learning into their operational DNA. With AI advancing rapidly, staying current requires ongoing education and adaptation. This means creating structured knowledge-sharing opportunities and dedicating time for experimentation and learning.

Developing an AI-First Mindset

Schiavone advocates looking at every task through the lens of how AI could enhance it. This perspective shift helps drive habit change and enables people to naturally discover new opportunities for AI integration in their daily work.

Communication and Accountability

Clear, consistent communication proves essential for successful transformation. Schiavone recommends transparent sharing of AI council work, vision, goals, and ongoing initiatives. 

“You're going to want to address [fears and concerns] often,” she advises. “You're also going to want to celebrate wins, so publicly highlight success stories from teams using AI effectively.”

Accountability must extend beyond leadership to every employee. Schiavone emphasizes that 

“AI accountability cannot just live with leaders or AI champions. It needs to be integrated with all employees.” This means incorporating AI-related responsibilities into everyone's work plans, tailored to their role and level.

She recommends focusing on outcome-based metrics rather than activity-based ones. While you might start with metrics like AI tool usage or training completion rates, quickly transition to measuring business impacts like cost savings, productivity improvements, or AI-enabled growth.

Other Notes From This Episode

• Connect with Michael Stelzner @Stelzner on Facebook and @Mike_Stelzner on X.
• Watch this interview and other exclusive content from Social Media Examiner on YouTube.

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