Category: Business

Artificial Intelligence (AI) is now a reality. It’s no longer just a theory. What truly matters today are the outcomes. Here’s a crucial question to ponder: Can AI systems enhance efficiency and coordination without disrupting trust, stability, or existing cost structures? Can these systems coexist with human workers and lead to cost savings without causing disruption?

Let’s explore this through a structured plan involving simulation models and operational architecture to achieve practical outcomes.

The Promise of AI in Streamlining Operations

When designed for real-world environments, AI can offer first-year labor cost savings from 0.5% to 1.5%. These savings arise from smarter shift planning, avoiding costly rehires, optimizing routing, reducing rework, and minimizing overtime. Post-optimization, AI systems can continue to provide an annual efficiency gain of about 0.5%. These are not derived from cost cuts but from improved systemic planning and forecasting.

Understanding the Importance

The inspiration behind these strategies stems from the book Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work, and Life by Pascal Bornet, Jochen Wirtz, Thomas Davenport, and others. The book outlines how AI agents can collaborate with humans to optimize outcomes. However, the real test comes when implementing these ideas in an actual workplace.

Many work environments today are complex, where networks are not always online and labor costs make up 40-50% of expenses. In such settings, improved decision-making is pointless if trust is compromised.

Key Steps for Implementing Agentic AI

1. Begin with Data

Every AI implementation must start with data. Without data, there can be no AI. This includes capturing:

• Task-level durations and delays
• Load patterns over time and location
• Escalation triggers
• Shift coverage and absenteeism details
• Handoffs, rework loops, and idle time

Observing and mapping workflow is the essential first step. Automation only follows after gaining a clear understanding.

2. Build the Foundation Models

Before deploying agents, three model types need to be built:

• Forecasting models to predict demand, load, and shift timing.
• Classification models to classify tasks by complexity, urgency, and effort required.
• Optimization models to assign tasks based on real-world constraints.

A lack of these models can lead to random reactions or rigid systems, rather than ones that effectively mirror operating environments.

3. Full-Stack System Design

An AI system must be designed as a full-stack architecture with the following layers:

• Data layer: A standard schema for tasks, events, shifts, and outcomes integrated with workforce systems.
• Execution layer: Agents process inputs, call models, and route tasks with built-in overrides and feedback channels.
• Interface layer: Tablets, voice instructions, and visual boards for field staff; dashboards for analysts.
• Infrastructure layer: Capable of running on edge or hybrid networks. It must operate without relying solely on cloud services and fail gracefully.

4. Pilot Through Simulation

Deployment should not be the first step. Start with shadow mode, allowing agents to run silently alongside the current system:

• Compare agent decisions with human supervisors’ choices.
• Measure actual gains and identify where logic needs refinement.

Building confidence and credibility is key to successful implementation as real teams seek proof, not just presentations.

5. Clarify the Savings Curve

Be transparent about the savings curve:

• Initial savings of 0.5% to 1.5% are achieved in the first year through optimized planning and routing.
• These savings are not repetitive; ongoing efficiency gains come from improved forecasting and coordination.

The focus is not on reducing headcount but on enhancing productivity under pressure.

6. Prepare for Resistance and System Failures

Successful AI deployments account for human behavior, trust dynamics, and system downtimes. Ensure your design includes:

• Mechanisms to counter frontline skepticism and provide supervisor overrides.
• Offline modes and visual explainability.
• Progressive rollout strategies to avoid abrupt layoffs.

It’s crucial for systems to continue functioning seamlessly even during downtimes, as no AI system should halt operations.

7. Rethinking Agentic AI

Agentic AI is more than just a dashboard. It is an invisible coordination layer that doesn’t replace people but eliminates uncertainty, misalignment, and waste. Rather than just deciding or optimizing, it collaborates effectively.

Building for Today and the Future

This isn’t just about AI transformation. It’s about execution, and creating systems that are buildable today. We must have honest conversations about what AI can achieve, and what it needs to function. Leaders in operations, systems, data, or workforce design are key to identifying areas where the current model might fail or need adjustments.

AI is a powerful tool that can streamline operations, save costs, and coexist with human efforts when implemented thoughtfully. It’s not an overnight transformation, but a journey towards efficiency that requires careful planning and execution.

As we continue to develop AI solutions, let’s switch our focus from theoretical possibilities to practical applications and prepare for real-world scenarios.

What are your thoughts? How would this model fit within your operation? Let’s engage in a grounded discussion about the next steps towards making agentic AI a practical reality in our workplaces.

Artificial intelligence is reshaping the world, and the travel industry is no exception. As we move into 2025 and beyond, AI has the potential to quietly change everything in travel, one step at a time. The transformation is not about making big, flashy changes but about enhancing the small things that really matter to travelers.

The Power of Subtle Improvements

I recently came across an insightful case study from Booking.com and OpenAI. What struck me was not how futuristic or extravagant it sounded, but how practical and real it felt. The key lies in using AI to provide a new level of personalization throughout the traveler journey. This approach does not aim to replace people or create something overly complicated. Instead, it focuses on gradually improving the travel experience in meaningful ways.

Step by step, AI can make travel more seamless and enjoyable. From planning a trip to booking, receiving help during the trip, and all the way to planning the next adventure, AI can create experiences that feel naturally smooth. Such improvements have the potential to drive customer experience (CX) and Net Promoter Scores (NPS) through the roof.

Creating Personalized Travel Experiences

Excursion Matchmaking with Real-Time Personalization

Consider how AI can transform excursion bookings. Booking.com already personalizes hotel listings by mixing user reviews, photos, and tags to craft summaries that resonate with different travelers. A solo traveler might see “quiet and safe”, while a family might see “vibrant and walkable”.

Imagine if this personalization was extended to excursions. Rather than seeing generic text and photos, travelers could receive descriptions tailored to their preferences. For instance, a solo traveler and a family could view the same tour but with different highlights emphasized. This personalization can increase bookings without requiring more inventory.

Digital Concierge for Seamless Service

AI can play a significant role as a digital concierge. Booking is testing an AI assistant that helps customers after bookings, answering questions and rescheduling activities. Such an assistant would be ideal for handling travel-related issues like weather changes, dining reservations, and port reminders. This does not replace human staff; instead, it allows them to focus on providing exceptional service.

Enhancing Trip Discovery and Planning

Natural Language for Trip Discovery

Booking.com has noticed a change when they opened a free text box for search. Instead of typing specific locations like “Paris 5 nights”, users now say things like “somewhere quiet with good wine and warm weather”. This shift indicates that people want to search in a more conversational manner.

Imagine a travel site where customers can type “I want something relaxing with nature in October” and receive tailored itineraries. This approach makes discovery feel more human and enjoyable.

Smart Filters Driven by Intent

Gone are the days of checkboxes. AI can interpret vague intentions like “sunset views” or “great breakfast” and convert them into search filters. Travelers can find the perfect stateroom or excursion based on these personalized filters, even if they are not sure how to articulate what they want.

Accelerating Decisions and Responses

AI-Driven Review Summaries

Few travelers want to sift through thousands of reviews. Booking.com’s AI summarizes key review themes, helping customers quickly decide based on crucial factors like cleanliness or staff friendliness. This boosts confidence and speeds up decision-making, especially for new customers comparing options.

Faster Service with AI Responses

AI enables partners on Booking.com to auto-reply to common questions, monitoring the quality of responses. This approach reduces response times, increases customer satisfaction, and allows teams to focus on unique requests. Such efficiency could be extended across support teams, onboard services, or excursion teams.

Personalizing Content and Managing Trips

Tailored Content Based on Profiles

Booking.com modifies property descriptions based on who is browsing. For example, a solo traveler might see different information compared to a family with children. This personalization can be applied to activity descriptions, dining options, and more, making the experience feel personal to each traveler.

Evolving Post-Booking Tools

The focus on post-booking has led to the development of AI assistants that help manage reservations and travel issues. These tools can reduce pressure on support teams and ensure that travelers have a smooth journey without missed moments.

Embracing AI and Breaking Barriers

Building Trust in AI

Research indicates that nearly half of travelers already trust AI for planning trips, with many using it to discover hidden experiences. This shift presents a golden opportunity for companies to align with traveler expectations by offering smarter planning tools that require less effort from users.

Eliminating Language Barriers

AI’s ability to translate content into multiple languages opens new markets and enhances accessibility. This capability is crucial for things like menus, excursion details, and signage, allowing travelers to interact with content in their native language effortlessly.

Conclusion

The future of AI in travel is bright and within reach. Booking.com’s efforts illustrate how AI can quietly revolutionize travel through subtle enhancements rather than disruptive overhauls. These behind-the-scenes upgrades have the potential to completely transform how travel feels.

As we move forward, the travel industry should embrace these AI-driven innovations. They can enhance the travel experience in meaningful ways, making trips more enjoyable, seamless, and personal. What innovations are you excited to implement? We’d love to hear your thoughts on how AI could elevate the travel experience in your organization.

Technology is designed to make things better. It should streamline operations, improve efficiency, and boost revenue. However, in many industries, technology often hits a snag. Systems are underutilized, digital solutions break with no easy fix, and tools promising great returns can disappoint.

Surprisingly, technology failures often are not due to bad tech. They stem from poor adoption, lack of real-time insights, and small issues growing into major inefficiencies. The good news is these problems can be fixed. Today, we’ll uncover a practical approach for leaders to ensure technology runs smoothly using just their workforce, a phone, email, and weekly meetings.

The Cost of Ignoring Small Technology Issues

Imagine a reporting system that no one fully understands. Or a mobile app that crashes frequently, but employees work around it instead of addressing the problem. A content management system might go unupdated, leading to outdated information reaching customers.

While these seem like minor inconveniences, they can silently diminish profitability, efficiency, and customer satisfaction when ignored for long. Here’s what typically happens:

• Small inefficiencies build up over time, creating expensive problems.
• Workarounds become routine, masking deeper issues.
• Leadership often notices too late, making fixes exponentially harder.

Successful leaders identify and address these problems early before they demand urgent attention.

Identifying and Fixing Technology Problems Before They Escalate

Organizations often use reports, analytics, and high-level meetings to assess performance, but issues may simmer unnoticed for weeks or months. Proactive leaders take a more direct approach by engaging with their teams to gather real insights.

Start with Direct, Open Questions to Your Workforce

Teams are often the first to notice technology failures. Do they have a straightforward way to report these issues efficiently? Instead of relying solely on reports, asking one simple question can be enlightening:

“What’s one daily tech issue that slows you down?”

This question can be posed through:

• Informal walks around different departments.
• Casual one-on-one calls with department leads.
• Setting aside a few minutes in weekly managers’ meetings.

Most workers already know what’s broken, outdated, or causing delays. They just need to be heard.

Real-World Impact:
In a past project, a self-service kiosk system had become unreliable. Employees had workarounds, so they didn’t formally report it. When customer complaints surged, management finally addressed it with a simple refresh script that automated a daily reset. This could have been resolved months earlier through one conversation.

Use the One-Call-Per-Day Method

You don’t need a complex system to stay on top of tech performance. The One-Call-Per-Day Rule empowers leaders to gather valuable insights by:

• Making a daily call to a department manager and asking, “What’s one recurring tech or process issue we should address?”

Why does this work?

• It empowers staff and makes room for their insights.
• Frontline input is prioritized, where inefficiencies are often most visible.
• It allows leadership to spot patterns early and act before issues escalate.

If the same problem comes up multiple times, it signals a bigger underlying issue.

Real-World Impact:
In another project, regular one-call check-ins revealed a content management system issue. Frontline teams reported being unable to update prices due to permissions issues. With leadership unaware, this willingly continued. A permissions fix and better workflow resolved it swiftly, saving weeks of unnoticed lost revenue.

Implement the “Stoplight Fix” for Faster Problem Resolution

Many tech problems persist because employees assume someone else is handling them. Workers need clarity on reporting issues promptly. The Stoplight Fix Method can simplify this:

• GREEN: Small issues the team can fix themselves. For example, if a screen isn’t updating, employees have a basic reset guide.
• YELLOW: Issues needing leadership attention like CMS permission barriers preventing updates.
• RED: Urgent issues requiring immediate IT or vendor support. Example: A complete payment processing failure.

With a clear process for classifying and escalating issues, they’re likely solved faster instead of being ignored.

Example from Real Life:
In a project involving real-time digital content displays, frequent system crashes puzzled the team. Initially thought to be hardware issues, it was due to the content management system generating oversized files. After optimizing file sizes, there was:

• 99.9% uptime stability.
• Fewer tech support calls.
• An 80% faster update process.

The takeaway? Tech problems might seem complex but often only need simple fixes if identified early.

What You Can Do This Week

If you’re leading operations and tech teams, these actions can keep technology efficient:

• Ask: “What’s one small recurring tech issue slowing us down?”
• Make one call per day: A quick check-in can reveal hidden inefficiencies.
• Clarify reporting: Ensure everyone knows how to escalate issues before they become disruptions.

Great operational challenges often aren’t solved through expensive upgrades. Listening to the right people and making small adjustments can compound into significant improvements.

Conclusion

The best organizations don’t just rely on technology. They focus on maintaining, optimizing, and troubleshooting continuously. Ignoring small issues can grow them into crises, but proactively finding and fixing them early is what sets efficient operations apart.

What’s a small operational issue you fixed that made a big difference?

Every business has hidden revenue opportunities waiting to be discovered. The big question is how many are being overlooked. Across various industries, businesses often miss out on revenue simply because:

• Customers request things not available, but no one tracks this demand.
• Employees notice inefficiencies reducing profitability, yet no one asks them how to address them.
• Minor process changes that could save time and increase revenue keep getting delayed.

Revenue loss isn’t always due to significant failures. Often, small and unnoticed gaps add up over time. The best leaders proactively search for untapped potential, minor process optimizations, and unmet customer demands.

Let’s explore how to identify and capitalize on revenue opportunities quickly in any operation without needing massive system changes or budget approvals.

Why Hidden Revenue Often Goes Unnoticed

The most significant reason revenue opportunities are missed is that they aren’t tracked. Most organizations have systems for managing sales and finances but lack systems for surfacing untapped revenue opportunities.

• Employees hear customer requests, but no one encourages collecting this feedback.
• Leaders focus on known issues while unseen inefficiencies quietly eat into profits.
• Opportunities to optimize operations pass by because no one looks for them.

Businesses that consistently grow profitability don’t just increase prices or cut costs. They find hidden revenue by fine-tuning inefficiencies and responding to unmet customer demand.

Three Steps to Unlocking Hidden Revenue Opportunities

You don’t need consultants, complex analytics tools, or large-scale operational overhauls. These three simple tactics will surface revenue ideas quickly using conversations, simple tracking, and rapid testing.

1. Ask the Workforce Directly

Operations teams interact with customers daily and hear demand signals before data does. Yet, most organizations don’t capture these insights effectively.

How to Apply This:

• Ask employees: “What do customers frequently ask for that we don’t offer?”
• Track responses: If multiple employees mention the same request, it’s a potential revenue stream.
• Encourage proactive suggestions: Employees often know the quickest fixes for revenue problems but may not feel empowered to share them.

Example from Real Life:

In e-commerce operations, customers frequently asked for digital gift card options. Although employees heard the requests, the demand was never tracked. Once leadership recognized the opportunity, launching a simple digital product captured the untapped revenue stream, leading to effortless sales.

2. Create a Quick “Idea Box”

Most companies rely on strategic meetings for revenue ideas while employees on the ground generate ideas daily with no outlet to submit them. An Idea Box system ensures smart revenue ideas don’t go unnoticed.

How to Apply This:

• Set up a dedicated input channel: Whether it’s an inbox, Slack channel, or form, employees need a way to submit revenue ideas anytime.
• Establish a vetting process: If an idea comes up more than once, it’s worth testing.
• Reward impactful insights: Employees are more likely to contribute if their ideas are valued.

Example from Real Life:

In digital media systems, an internal idea-sharing system tracked efficiency suggestions from employees. They reported that manual media file uploads slowed transactions. Testing a small automation adjustment sped up file processing, resulting in faster service delivery and increased revenue without hiring or system overhauls.

3. Test Small, Then Scale

Many revenue ideas falter because organizations assume testing requires large-scale rollouts or fear that small changes won’t impact significantly. However, rapid testing often unlocks hidden profits without large-scale risk.

How to Apply This:

• Identify small tests: If a new offer or operational change can be tested for a few weeks, it’s worth trying.
• Use minimal resources: The best pilots use existing resources before budget commitments.
• Track results: Measure the revenue impact before full implementation.

Example from Real Life:

While managing digital content, a new approach for organizing media assets was tested with a small group. This improved productivity, reduced media searching time, and led to smoother customer interactions and increased conversion rates.

Key Takeaways: Building a Revenue-Optimized Operation

Organizations often focus on selling more or expanding services, but revenue is frequently lost due to inefficiencies and slow processes. The fastest way to recover that revenue is to systematically look for it.

• Ask frontline employees: They notice demand patterns first.
• Create a system for small ideas: Turn them into real revenue opportunities.
• Run low-risk tests: Before committing resources.

Leadership that seeks hidden revenue potential proactively will always outperform teams waiting for problems to become urgent.

What You Can Do This Week

• Ask employees: “What do customers regularly ask for that we don’t provide?” Track patterns.
• Set up a submission system: Google Form, Intranet, or Email.
• Run a small test: Pick one in the next 30 days and measure its impact.

Revenue optimization doesn’t require guesswork—it just requires looking in the right places. What’s one small operational change you’ve tested that made a big financial impact? Let’s discuss in the comments!