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Small and medium-sized businesses (SMBs) are experiencing a transformative moment as artificial intelligence becomes more accessible and affordable than ever. While enterprise-level companies dominated early AI adoption, the democratization of these technologies has opened new opportunities for smaller players to compete effectively.

One of the most common questions I get from business leaders is about AI strategy, when to buy vs. build AI? Building AI from scratch gives you full control and customization, but it requires technical expertise, time, and investment. On the other hand, buying AI solutions allows for faster implementation but may come with limitations in flexibility and integration.

85% of AI projects fail, here is how to make sure yours is not one of them. One of my clients was looking into applying an AI assistant to improve customer engagement. However, they had several manual processes for building, deploying and monitoring their applications. Here is how we helped them:

After all the AI hype, many businesses struggle to measure Generative AI’s profitability. Insights from KPMG, Gartner, and BCG reveal why this is a challenge and how to overcome it. KPMG has revealed that most organizations are not yet ready to measure the success of their generative AI projects, due to the PoC and experimental stage of the projects. Even among the 44% of the businesses that applied GenAI and went beyond the experimentation phase, only 15% have established metrics to measure returns of ROI. When measuring ROI, what metrics should be considered, and how should they be done?  

When it comes to implementing AI in business, many businesses overlook the foundational steps needed to ensure a successful AI strategy. It is important to not get caught up in the GenAI boom, focus on actionable steps to drive meaningful outcomes.

Recently one of our clients initially adopted a low-code/no-code platform to create AI workflows for their processes. It was a quick and easy way to validate the PoC and implement automation without heavy investment up front. But when they needed to scale and customize, they hit a wall; the platform’s limitations restricted their ability to expand AI capabilities. Now, they’re planning to rebuild an in-house solution. This decision can make the difference between AI that transforms your business and AI that becomes a costly distraction.</p><p id="">This example highlights a key decision every business leader must make when implementing AI: Should you build AI in-house or buy it from a vendor? The answer isn’t just about cost, it’s about things you need to consider such as data privacy, security, and scalability.

AI is no longer just a buzzword; it’s a key tool for businesses looking to grow revenue, reduce costs, and stay ahead of competitors. Yet, when I talk to business leaders, many struggle with identifying AI opportunities in their organizations. Some assume they are already leveraging AI just because they have integrated ChatGPT into their operations, while others don’t know where to start.

Thinking about integrating AI into your business? You’re not alone. But while most companies are with AI, very few are strategically implementing it. What’s missing? A clear, end-to-end AI strategy that links ambition with execution, and results.

Everyone’s talking about building with AI. But here’s what’s missing from most conversations: Product-Market Fit.

After talking to many business leaders over the past few months, I've noticed two very different approaches to AI adoption. Some companies are rushing to implement AI only to keep up with competitors to stay relevant. And some organizations use AI to go beyond automation, find new markets, and create new opportunities. In this blog post we explore red ocean and blue ocean AI strategy for sustainable businesses.

Most companies want to use AI. Very few know how to implement it in a way that delivers real business value. The result? Endless pilots, experiments that go nowhere, and wasted budgets. If you’re a business leader, COO, or Chief Strategy Officer asking: “How do we make AI work for us?” this guide is for you.

How could we fully transform into an AI-powered company? A business leader asked me this recently, and it’s a question I’m hearing more often.

The most successful businesses I’ve worked with have one thing in common: they know exactly what they want from AI. While many still debate whether to build AI in-house or buy from vendors, the real answer isn’t that simple. The companies that win don’t pick a side, they combine the best of both.

One of the concerns I hear from business leaders is their team’s hesitation about AI automation. That hesitation often stops them from transitioning to become AI-native. Is that happening with your team too?</p><p id="">The good news is, AI isn’t just about automation. There’s a better way to introduce AI into your business. Instead of replacing jobs, AI can augment your team’s capabilities, enhancing customer experience, improving productivity, and freeing up time for more valuable work. With AI-driven augmentation, employees can upskill, achieve better work-life balance, and focus on impactful tasks, while leaving repetitive, manual processes to AI.

Over the past six months, I’ve had in-depth conversations with business leaders across HR, and operations, CHROs, CFOs, COOs, and Heads of Talent.

Most AI conversations today revolve around speed, automation, and capability. But in 2025, one truth is becoming impossible to ignore: AI without security isn’t strategy. It’s risk.

A search AI agent is an intelligent system that leverages technologies like Natural Language Processing (NLP), Machine Learning (ML), and Generative AI to process user queries, understand intent and context, and provide highly relevant, accurate, and personalized search results.</p><p id="">Unlike traditional keyword-based search engines, a search AI agent focuses on delivering <strong id="">contextual answers</strong> rather than just matching keywords. It uses advanced algorithms to predict user needs, recommend actions, and even proactively provide suggestions based on user behavior, preferences, and real-time data.

AI has transitioned from an “in the future” concept to a “must have now” driver for innovation and efficiency across industries. Many businesses are investing in AI strategies with the hope of unlocking new opportunities and enhancing operations to gain a competitive advantage. However, many struggle to translate their AI ambitions into tangible results. The gap between strategy and execution often comes from a lack of clarity, misaligned priorities, or outdated infrastructure. In this blog, we share how businesses can bridge this gap and turn their AI strategies into measurable outcomes similar to one of our fintech clients.

In today's rapidly evolving business landscape, artificial intelligence (AI) has emerged as a transformative force that's no longer exclusive to large enterprises. Small and medium-sized businesses (SMBs) are increasingly recognizing AI as a critical tool for maintaining competitiveness and driving growth. According to McKinsey's 2023 State of AI report, organizations using AI reported revenue increases of 10-20% across various business functions.

A data analysis ai agent in banking and finance is an AI-powered system that processes and analyzes vast amounts of structured and unstructured data to extract insights, identify patterns, and generate actionable recommendations. These agents use <strong id="">Machine Learning</strong>, <strong id="">NLP</strong>, and <strong id="">data analytics</strong> techniques to support decision-making and improve efficiency in financial operations.

As artificial intelligence continues to revolutionize industries, finance and banking are at the forefront of adopting cutting-edge technologies to enhance customer experiences and improve operational efficiency. One of the most exciting developments is the rise of generative AI content generators, which have the potential to transform how financial institutions communicate, engage with customers, and drive data-driven decision-making. In this blog we explore business use cases for Gen&nbsp;AI Content Generators in Finance and Banking industry.

AI is transforming industries at a rapid pace, and the finance and banking sector is at the forefront of this revolution. In this blog, we’ll explore 12 business use cases for Generative AI chatbots in finance and banking, showcasing how these advanced tools drive efficiency, enhance customer experiences, and streamline operations in a competitive market.