Episode 1: Enhance Your Business Processes with AI

3 Steps for Quick ROI

Introduction

Picture this: you're the CEO of a growing SME or the CTO of a large corporation, and you've just spent 45 minutes sifting through endless Excel reports. Meanwhile, your competitors seem to have already embraced AI solutions that provide real-time indicators, predict stock shortages, and even draft automated follow-up emails… The temptation to believe that integrating AI is like strapping a turbo onto the aging machine of your organization is strong. But is it really that simple?

In this first part of our mini-series on Transformation & AI, we go beyond the marketing hype to reveal three concrete steps to leverage AI—without turning your employees into extras in a sci-fi movie. Spoiler: You don’t need to know how to code in Python to get started, nor do you need to hire 15 data scientists. However, having a clear plan and—most importantly—a realistic vision of that famous "return on investment" (ROI) is essential.

1. Identify Your “High-Value” Business Processes

Business Process and Workflow Optimization

A striking statistic from McKinsey reveals that 70% of AI initiatives fail to meet their objectives or fail altogether. Why? Often, AI is deployed indiscriminately—from basic accounting reports to the coffee machine (which supposedly recognizes you by your retina and brews your latte to perfection). In other words, businesses rush to “do AI” before analyzing where time or money is actually being lost.

a) Focus on the Real “Pain Points”

Ask yourself:

What repetitive tasks frustrate your teams daily?
What bottlenecks slow down production or delivery?
Where do you consistently lose quality, time, or money?

In some industries (logistics, manufacturing), 80% of delays and extra costs stem from just one or two key issues: inefficient planning, unreliable stock estimates, manual document validation, etc.

Concrete example: A large e-commerce company found that 40% of customer complaints were due to delivery delays. Upon analyzing their data, they discovered that demand forecasting was unreliable, causing preparation issues. AI provided a solution: a predictive model to anticipate demand, optimize stock levels, and recommend suitable delivery slots

b) Separate Technology from the Real Need

You probably don’t need a "quantum NFT chatbot" if your real issue is field team scheduling. A mismatch between the solution and the problem is the primary cause of failure. As one slightly cynical CEO remarked:

"In trying to deploy AI everywhere, we end up spending a fortune automating tasks that don’t matter."

2. Choose the Right Technology (and Start Small)

Enterprise Optimization Technologies

Once you've identified which process needs optimization, it's time to tackle the "how."

a) The Most Common AI Applications

Chatbots & NLP (Natural Language Processing): Automate customer relations, handle FAQs, and filter initial requests (covering 70–80% of simple cases). Benefit: Frees support teams to focus on complex cases.
Computer Vision: Detects production anomalies, recognizes objects, and analyzes images for predictive maintenance (e.g., identifying defective parts via a production line camera). Benefit: Speeds up quality control and reduces waste.
Predictive Analytics: Uses historical data to forecast demand, adjust logistics, or recommend products to clients. Benefit: Reduces dormant stock, targets offers better, and minimizes waste.
Robotic Process Automation (RPA): Replaces repetitive manual tasks (e.g., data entry into ERP or CRM systems), often combined with AI (computer vision, intelligent OCR). Benefit: Reduces errors and frees up time for higher-value tasks.

b) Prioritize a “Proof of Concept” (PoC) Approach

Many companies embark on grand AI projects, burning through massive budgets without ever validating the feasibility of the concept.

Targeted PoC: Test the solution on a small scale (one production line, a specific customer service team, or one sales channel).
Measure Real Impact: Compare error rates before/after, track time saved, and gauge customer satisfaction.
Iterate Quickly: If successful, expand. If not, adjust or switch technologies.

Historical Anecdote: In the 1980s, General Motors heavily invested in industrial robots to compete with Toyota. The result? Billions spent, with productivity… plummeting. Why? GM hadn’t rethought its processes beforehand. AI (or automation) won't fix a poorly designed process.

3. Measure (and Share) ROI: AI Can’t Do It All

A Deloitte study shows that 60% of decision-makers struggle to justify AI investments without clear, quantifiable results. Defining simple, relevant indicators is crucial.

a) Set KPIs Before Deployment

Time Saved: How many man-hours are freed weekly or monthly?
Quality: Error rate reduction, fewer returns or complaints.
Operational Costs: Lower storage, energy, or other expenses.
Commercial Impact: Customer satisfaction (NPS), conversion rates, average basket size.

These figures should be easy to track—ideally through automated dashboards. ROI isn’t just financial. Improving employee well-being (avoiding burnout, reallocating teams to more fulfilling tasks) can be a major benefit, even if harder to quantify.

b) Don’t Overlook the Human Element

The best AI is worthless if your team views it as an enemy. Resistance to change and fear of replacement are real. A Gallup survey found that 37% of employees believe AI poses a “high risk” to their job.

Train employees to understand AI (even at a basic level).
Communicate the goals and benefits regularly.
Involve teams in co-designing how AI can ease their workload.

ractical Tip: Hold feedback workshops after 3 to 6 months to identify friction points and celebrate successes. Involving employees strengthens buy-in.

Conclusion

Comparison of Two Companies: One Without Optimization and One With

AI is neither the magic potion some claim nor a gadget reserved for Silicon Valley startups. By targeting key processes, selecting the right technology, and clearly measuring ROI, it can truly boost your competitiveness—without neglecting the human aspect. You now have a three-step action plan. There’s no need for a massive budget or to hire 50 engineers—opt for a gradual approach, following the “Proof of Concept” method. After all, no one trains for a marathon without first jogging on Sundays.

What now? You're ready to kick off your first AI deployment to streamline your business processes. But practically speaking, how do you ensure team collaboration in a world where hybrid work has become the norm? The challenge isn’t just about “plugging in” AI—it’s about rethinking work organization to make the most of these new digital tools. This is precisely the focus of Episode 2 of our mini-series:

“Digital Workplace: Successfully Transitioning to Hybrid Work While Staying in Control”

Between remote work, endless video calls, and sometimes finicky document-sharing systems, AI may only be the tip of the iceberg. Stay tuned to discover how to avoid digital chaos and keep your teams engaged—without losing efficiency or camaraderie. And who knows, we might even share a few extra tips on how to brief your chatbots smartly…

Until then, remember:

Focus on relevance, not trends.
Start small before scaling up.
Talk to your teams—transparency is better than a silent algorithm.

See you soon for the next episode!

Written by: Ludovic Pennont