A Business Leader's Playbook for AI Process Innovation

AI applications span a wide spectrum: from basic decision tree systems to self-learning models and sophisticated multi-agent architectures capable of autonomous reasoning and task execution. And the technology continues to evolve practically every day.

But what can AI actually do to solve real business problems? The answer is more nuanced than you might expect: it can’t do much; at least not without human involvement.

It Starts with People, then Process

AI isn’t a magic wand; it needs direction, context, and perhaps most importantly, human know-how to create real business outcomes. To generate real business value, it’s imperative to begin by defining a pragmatic strategy and a clear execution plan: both of which demand human insight, domain expertise, and creative ingenuity. The strategy sets the context and objectives, and the execution plan outlines the what, how, who and when.

Execution almost always involves improving processes, whether for employees, partners, or customers. And, a scalable, competitive business requires optimal processes that are adaptable.

AI Process Automation is Now

Business Process Reengineering (BPR), a major trend in the early 90s, followed a structured, top-down approach to improvement. It entailed the following core steps:

1. Identify a Process to Improve
2. Map the Current (“As-Is”) Process
3. Analyze for Inefficiencies
4. Design the Future State (“To-Be”)
5. Implement Changes
6. Monitor & Optimize

While BPR delivered some results, it struggled to scale across dynamic, knowledge-driven environments. It couldn’t easily accommodate processes with unstructured data, judgment-based decisions or rapid change. This rigidity led to workforce backlash and a decline in adoption through the 2000s, though many of its principles live on in Digital Transformation, Lean Six Sigma, Business Process Management, and, most recently, AI Process Automation.

The AI-enabled process improvement model is fundamentally different. Instead of relying on a one-time “redesign,” AI plays well with complexity, ambiguity, and iteration. Technologies like machine learning, natural language processing, and computer vision go beyond explicit programming and can process both structured and unstructured data in multiple modalities: text, audio, images, and even video to make contextual decisions and learn from feedback.

Unlike traditional BPR, which typically requires costly, enterprise-wide implementations up front, AI allows you to test, learn, and refine in lightweight, low-risk cycles. What’s especially transformative in a very disruptive way is the recent advent of Generative AI. Generative AI makes it possible for almost anyone, not just engineers or data scientists, to rapidly experiment and prototype solutions using natural language. This unprecedented democratization of development lowers the cost of experimentation and accelerates time-to-value. This transition from deterministic design to rapid, adaptive experimentation marks a massive paradigm shift, an unprecedented opportunity for every business.

Let’s take a look at how to leverage AI for automating business processes.

How to Prepare for AI-Enabled Automation

Before you take a deep dive into enterprise AI process automation, identify areas where business processes could be streamlined and improved to deliver quantifiable value by following these steps (see diagram: Lean AI Innovation Model):

1. Readiness and Strategy: Assess your organization’s data maturity, cultural preparedness, and data and AI technical capabilities. Define what “value” means in your business and set strategic objectives accordingly.
2. Inventory: List your core business processes across business functions: Finance & Accounting, Product Development, Sales, Marketing, Talent Management, Operations, IT and more.
3. Prioritize: Choose processes that directly ladder up to your strategic objectives, significantly impact your finances or risk tolerance, and require large amounts of manual work today. These are likely areas where some level of automation will deliver substantial return on investment (ROI).
4. Feasibility: Analyze the data and systems underlying each process to determine the short and long-term feasibility of AI automation. Ask yourself:  a) How clean and comprehensive is your data? b) How complex are the supporting systems where you collect, store and process data? c) What resources and effort are required to establish a strong AI foundation, encompassing essential technical expertise, data governance, and data infrastructure?
5. Prototype: Translate your ideas into action by creating a rapid prototype that focuses on validation (not perfection), learning, and improvement. An experiment. A good prototype helps assess whether an AI automation concept has potential to  deliver measurable improvements.

Learning through Prototyping

As part of the prioritization exercise, select your top three candidate processes. A good early candidate has the following attributes:

• Manually intensive today
• Strong potential for measurable productivity gains, revenue growth or risk reduction
• Has a process owner and a minimum of one process doer excited about applying AI
• Requires the least amount of upfront system and data work

The last bullet is key. While you haven’t conducted the Feasibility step yet, “use your gut” to whittle down your list. Then, move into Feasibility to go one level deeper into your assessment of the system and data work. Don’t underestimate the importance of truly assessing technical and data feasibility. Too much tech or data prep work is like starting your journey with your legs stuck in a tar pit.

Select one process to move into the Prototype step. Remember to position the first process automation effort as an experiment, not an enterprise transformation. Don’t try to automate a core (live) process until you, the process owner, and doers feel comfortable that the AI-enabled experiment is a worthy improvement; one that is quantitatively and qualitatively better. Lastly, build a rapid prototype and test it in a controlled, low-risk environment with an AI-friendly process owner and a team of willing users.

Target Quick Wins

Ideally, your first AI-enabled process should be something you can create and deploy in 90  days or less. A tight deadline minimizes risk, forces focus, and enables you to show value fast. Once you demonstrate value with your proponents, it becomes much easier to continue improvements on your first AI automation endeavor or move on to experiment with others in your roadmap.

Productionalizing the Prototype

Once a prototype shows promise, evaluate its performance against baseline metrics to ensure it delivers real value. Then, embark on the effort to transform the prototype into a scalable, secure, and maintainable enterprise-grade solution:

• Start by thoroughly assessing the enterprise changes required to support it.
• Design and build the solution so it fits into existing workflow(s) with a convenient and intuitive user experience.
• Test the enterprise solution in a live environment with real users as a controlled pilot so you can discover and address practical issues.
• Launch the solution across the enterprise with internal marketing, change management and performance monitoring.

Fostering a Culture of AI Readiness

A huge part of AI success and one of the most overlooked areas, is change management. It's critical to plan out your communication strategy and partner with people who do the work today, or rejection may block all your best intentions.

Find and engage AI champions. Look for processes owned by people who are curious about AI and open to experimenting. Partner with executives to message the organization in a strategic and meaningful way. If people feel included, empowered and heard, they are more likely to be your advocates. Even the best technology won’t go far if teams are forced to use it.

I’ve seen too many automation projects fail because they hinged on a traditional top-down implementation. Treat your AI journey as a collaborative evolution. Include other leaders, process owners, doers, and subject matter experts early on and work with them to create a roadmap. Make them co-owners, not sideliners or just doers.

Sounds Easy, Right? It Isn’t

Choosing the right process to automate is deceptively difficult. It’s also one of the most important decisions you’ll make on your AI journey.

• If you pick a process that doesn’t truly generate meaningful value for your business, there will be no ROI, and your journey will likely end abruptly (and typically not start again for years).

• If you choose a complex process with poor system or data support, your AI journey will lose steam as it will take too long or require too many resources to demonstrate value in any reasonable amount of time. Your support will lose the fuel it needs.

• If you pick a process where everyone involved is fearful of AI, you’ll be pushing a boulder uphill that’ll likely roll you over just when you think you’ve completed the hard part.

A Real-World AI Implementation Case Study

While I’ve seen countless examples of applying this playbook across clients, I believe the most meaningful illustration comes from within my own company. I’m a strong believer in practicing what I preach.

I’m going to walkthrough applying the framework to two processes in our business:

• Lead Generation - from Readiness & Strategy through Prototype
• Service Delivery - from Assess to Launch

By detailing these real-world applications, I hope to provide a clear, actionable approach for your own AI transformation journey.

1. Readiness & Strategy

Each year, we assess our company’s current level of AI readiness and refine its internal data and AI strategy. This begins with measuring its maturity across 11 key capabilities, and generating its readiness  — what we call our “AIQ” score.

We then pinpoint capabilities that matter most for our business to mature, and build a roadmap of initiatives to address them, aligning each to the strategic business objectives defined for the year. We also set a target AIQ for the following year. This becomes our north star, it helps us stay focused on long-term progress while prioritizing near-term efforts.

2. Inventory

A typical professional services firm has the following business functions:

• Strategic Management & Governance
• Business Development & Sales
• Service Delivery
• Talent Management
• Finance & Accounting
• Operations & IT

Critical processes within and across these business functions range from Vision & Strategy Development, to Lead Generation, Project Scoping & Planning, Recruitment & Hiring, Invoice and Billing, and Knowledge Management, to name just a few. We documented all of these processes and mapped them to their respective business units in a centralized spreadsheet for evaluation and prioritization.

3. Prioritize

On a regular basis (typically quarterly or annually), we identify the strategic business objectives that will help us better serve clients, strengthen our culture, and support sustainable growth.

In late 2024, the most pressing challenges from a business perspective were concentrated in Business Development & Sales. With that focus in mind, we analyzed the critical processes within that function:

Given the reality that the sales funnel begins with Lead Generation: a process that is both manually intensive and directly impacts our top-line revenue and long-term sustainability, we pegged it as the highest priority for improvement.

To assess whether automation was even viable, we first broke down the Lead Generation process into detailed sequence maps. Each map outlined specific steps (including decision points) our team followed before any improvements were made. We then identified the data sources and underlying systems (such as HubSpot) required to support each step within these sequences. This allowed us to understand not just what was being done, but what enabled it. Finally, we used a stopwatch approach to baseline the time required for each step. We captured 5 iterations per step and averaged the time spent to establish a reliable benchmark.

Armed with this information, we focused on steps that demanded the most manual effort but required minimal system or data changes. These become our “shortlist”: prime candidates for rapid prototyping.

5. Prototype

Focusing on time saving and output quality, we initiated a series of rapid hack, test, learn, and improve cycles with a small, cross-functional team. They quickly identified viable automation candidates that either optimized individual steps or eliminated them altogether.

Prototypes ranged from desktop AI Agents that entered data into systems for us (we beta tested the ChatGPT Agent before it was launched), to LLM-powered Deep Research prompts that compiled company research, to browser plug-ins, mostly developed by Generative AI, that scraped external data we needed to expedite our manually-laborious sequence steps.

Each prototype was evaluated for its efficiency, effectiveness and cost. Those that delivered significant time savings against our baseline and consistently produced high-quality outputs at an affordable price were tagged as candidates for enterprise implementation.

6. Assess

It’s one thing to create a prototype with a small team hacking things together; it’s something else entirely to deploy a data or AI solution in a live operational environment. Production-grade solutions need to be robust, secure, scalable, and cost-effective. While the Lead Generation prototype described above hasn’t made it to production yet, several other process improvements have: most notably in Service Delivery.

To provide context, Service Delivery includes mission-critical project management functions, managing the triple constraints of time, scope, and budget is essential. A few years ago, we identified a major gap:  project teams lacked near-real-time data to make informed resource and budget decisions. After creating and testing several automation prototypes, we landed on one that performed well and earned its place as a potential enterprise standard.

Evolving the prototype into an enterprise solution wasn’t technically difficult (thanks to the engineers on our team), but it did require dedicated time. So we made a calculated decision to temporarily redirect client-billable hours from a few engineers to implement an enterprise solution because “the juice was worth the squeeze”.

7. Design & Build

The first step our engineers took was to architect a sustainable, production-ready solution. Prior to prototyping, project performance was being tracked manually: data had to be downloaded from multiple systems and analyzed in spreadsheets, which was both time-consuming and error-prone.

The successful prototype validated the concept using a primitive data lake and reporting system. While it was effective as a proof of concept, it wasn’t robust, secure, scalable, or cost-effective particularly from a long-term support and maintenance perspective.

With those limitations in mind, our engineers identified the necessary architectural and code changes and spent the next few months building the enterprise-grade version. Regular test cycles were incorporated into the process, with validation led by our lead project manager, who was not only deeply motivated to improve delivery outcomes but also a strong advocate for data and AI.

The alignment between technical execution and business ownership proved instrumental to a successful build.

8. Pilot

Once development was complete, we transitioned into a focused pilot phase. A select group of project managers began using the enterprise solution on live projects over several months. This enabled us to test the pilot in context to surface real-world issues, gather hands-on feedback, and fine-tune the experience before rolling it out more broadly.

9. Launch

With the pilot successfully completed, we moved to operational rollout, focusing on communication, training, and long-term support. Our lead project manager introduced the solution during a company-wide meeting with support from our executives, met with other project managers, and conducted targeted training sessions to ensure confident adoption.

The outputs from the new system were also integrated directly into our weekly Project Management Office (PMO) meetings, embedding the solution into the fabric of our day-to-day operations. Launched last summer, the solution significantly improved company-wide transparency leading to accelerated and more informed decision-making ultimately enhancing our client experience.

Over time, we introduced several enhancements, primarily new data visualizations layered onto the established data lake. Consequently, the team continues to squeeze even more juice from the original investment, reinforcing our aspirations that incremental automation can deliver lasting and compounding returns. Predictive analytics are next.

Conclusion

AI success is often associated with big, splashy transformations. Nothing could be further from the truth when you’re beginning your AI process automation journey. Start with one well-chosen process, a handpicked team, and an approach that generates measurable results.

The most effective and successful AI automation journeys begin with people and processes (and not technology), with a deep understanding of what’s broken, where value can be created, and how teams are expected to react to the change. It’s more of a leadership challenge than a technical one.

It’s time to move past AI hype and apply AI to real problems, in real environments, and with real people.

The first step doesn’t need to be perfect. It needs to be intentional and data-backed.

Choose wisely!

Do you have a process you'd like to improve with AI automation? Contact me, and we’ll talk through it together.

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