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Vizible Results turns 10 this year. After a decade of consulting engagements across dozens of companies, here are the patterns that keep showing up: Revenue problems are data problems. Every company that struggled with growth had the data to see it coming, they just weren’t looking at it the right way. Customer concentration, cohort decay, pipeline inflation, etc. these patterns are visible in the data months before they hit the P&L. Speed matters more than precision. A dire

If you’re a portfolio company CEO or CTO, you’ve probably gotten this call from your PE sponsor recently: "What’s our AI strategy?" It’s not an idle question. PE firms are under pressure from LPs to demonstrate that their portfolio companies are AI-ready — or at least AI-aware. And with AI disruption fears hammering software valuations, firms need concrete answers. Here’s a practical framework: Step 1: Assess your AI exposure — which parts of your product or service could AI

In the last 6 months, I’ve seen a dramatic shift in how PE portfolio companies approach AI. The conversation has moved from "should we use AI?" to "how fast can we deploy it?" Here are the three use cases driving the most value: (1) Revenue Operations AI — agents that analyze CRM data, predict churn, and prioritize sales activities. One portfolio company reduced time-to-insight from weeks to hours. (2) Customer Success Automation — AI agents that monitor usage patterns and fl

Analyzing data on a laptop, showcasing the transition from Business Intelligence (BI) to Artificial Intelligence (AI) through dynamic graphs and advanced analytics. For a decade, Vizible Results has helped enterprises make sense of their data. We’ve built dashboards, analyzed revenue patterns, and delivered insights that drove growth. But here’s what we’ve learned: the companies that win aren’t the ones with the best dashboards. They’re the ones that use data to predict what

When I first dipped my toes into the world of artificial intelligence, it felt like stepping into a sci-fi novel. The promise of machines that could learn, adapt, and even anticipate our needs was thrilling. Fast forward to today, and AI is no longer a distant dream—it's a powerful tool reshaping how enterprises operate. If you’re wondering how to harness this force, you’re in the right place. Let’s explore how AI strategies for enterprises can transform your business from th

Most AI projects die in pilot purgatory. Most AI projects die in pilot purgatory. They launch with excitement, demonstrate promising results in controlled conditions, generate enthusiastic presentation and then stall. Months pass. Resources remain allocated to pilots that never progress. The organization learns nothing about what it takes to deploy AI in production. This pattern is so common that it's become the expected outcome rather than the exception. Breaking the pattern

Deploying AI agents without thorough preparation leads to predictable failures that damage trust and slow future progress. Many organizations rush to implement AI solutions, eager to capture value, but skip essential steps that clarify risks and measure success. Before you launch an AI agent, pause and ask yourself five critical questions. If you cannot answer them clearly, your deployment is not ready. Dashboard displaying AI agent performance metrics What Can This Agent Act

In 2025, software vendors introduced consumption-based pricing for AI features, charging companies per token, API call, or compute unit. This model quickly frustrated CFOs. The unpredictable costs made budgeting difficult, turning what should have been successful AI deployments into financial headaches. Worse, the pricing structure encouraged organizations to limit AI use to control expenses, reducing the benefits AI was supposed to bring. By 2026, the licensing landscape is

Large Language Models (LLMs) have transformed how businesses interact with technology. They understand natural language, generate detailed responses, and can reason across many topics. Yet, despite their impressive abilities, they face a critical challenge: they do not know your business. This gap often causes AI agents to confidently provide incorrect or irrelevant information, a problem that can be avoided by integrating a knowledge graph. A digital network map illustrating

Artificial intelligence is no longer just a tool for suggestions or insights. As AI agents gain autonomy and take independent actions, the challenge shifts from technology to trust. The difference between successful AI deployment in 2025 and 2026 will not be about new algorithms or faster processors. It will be about operational discipline and the ability to build systems that organizations and users can rely on. Trust has become the currency of AI deployment. Without it, eve

Gartner forecasts a sharp rise in enterprise applications embedding AI agents, jumping from less than 5% in 2025 to 40% by the end of 2026. This rapid growth means AI will no longer be a separate tool but a built-in feature of the software employees use daily. This shift will change how work gets done across industries. The question is, how prepared is your organization for this change? What Embedded AI Agents Mean for Your Business Embedded AI agents are not just chatbots or

The agentic AI market is set to expand dramatically, growing from $7.8 billion today to more than $52 billion by 2030. This nearly sevenfold increase in just five years marks one of the fastest-growing areas in enterprise technology. Yet, for business leaders, the critical question is not just about the market size but where to focus investments to gain the most value. Understanding the different segments within the agentic AI market helps companies make informed decisions ab

Nearly 80% of companies report no significant bottom-line impact from their investments in generative AI. This is a striking figure given the billions spent and the countless pilots launched. Despite the excitement around AI, four out of five organizations struggle to turn their AI efforts into real business value. So, what sets the successful 20% apart from the rest? After working with enterprises across various industries on AI strategy and implementation, clear patterns em

A recent survey revealed a striking statistic: 76% of executives now see agentic AI as a coworker rather than just a tool. This shift is more than a change in wording. It signals a new way organizations think about AI’s role in the workplace. This change affects how managers lead their teams and interact with AI systems. AI system collaborating with a human worker The Difference Between Tools and Coworkers Understanding the difference between tools and coworkers is essential

If you have worked in enterprise technology for over ten years, you likely remember the shift from monolithic applications to microservices. This change transformed how software systems are built, deployed, and scaled. Now, a similar transformation is underway in the world of AI agents, reshaping how organizations approach intelligent automation. The Limitations of Monolithic AI Early AI systems often followed a simple idea: create one powerful agent that can handle everythin

The gap between AI experimentation and real business value has never been clearer. Recent research shows that 65% of organizations are actively experimenting with AI agents, yet fewer than 25% have successfully scaled them into production. This gap highlights a major challenge in enterprise technology today. If 2025 was the year when everyone talked about AI agents, 2026 is the year businesses began asking a tougher question: Is any of this actually working? The Shift from Pr

Network Protocols The rise of AI agents is reshaping how software systems interact, but it also creates a familiar challenge: how do we enable different AI systems to communicate effectively? In the early internet days, HTTP became the standard that allowed web servers and browsers to exchange information smoothly. Today, as AI agents multiply across industries, similar standards are essential to avoid chaos and fragmentation. Two protocols are emerging as key players in this

Navigating Common Pitfalls In our data-driven era, Business Intelligence (BI) promises the allure of sharper decision-making and strategic insights. Yet, a significant number of BI initiatives don't quite deliver on their promise. Why do some in-house Business Intelligence programs falter where others flourish? Let's delve deep into the challenges that often stand in the way of BI success. 1. The Data Quality Conundrum BI is only as good as the data fed into it. A common stum

Data > Insights > Decision > Action > Outcome > Value It almost seems like an eternity when I told myself I'd steer clear of any more analytics tools. Those days data was stored on laptops or on a server under someone's desk. Manually cleaning data, checking for duplicates was such a chore as was the frustration with missing data Yet, my passion for data analysis kept me anchored. Thinking back, the challenge wasn't in the actual analysis, especially when our data sources we

Understanding Business Intelligence Maturity Roadmap: Stages, Implications, and Growth Business Intelligence (BI) is not a one-size-fits-all solution. Just as businesses evolve, so too does their handling, interpretation, and application of data. Recognizing where a company stands on the BI maturity curve is vital, as it shapes the organization's current capabilities and sets the trajectory for future growth. This article delves into the various stages of BI maturity, their i