Latest news with #AsharSamdani
Forbes
2 days ago
- Business
- Forbes
The Rise Of AI Agents: The Next Step In AI Transformation
Ashar Samdani: CEO of Code District, a Washington-based software development firm that focuses on helping businesses incorporate modern tech Out of all the cinematic representations of intelligent agents, my favorite remains Tony Stark's JARVIS. It wasn't just a voice assistant—it was a thinking, learning, proactive system that made decisions, predicted needs and worked seamlessly across tools and interfaces. Back then, it felt like science fiction. But fast-forward to now, and we're closer than ever. We've already lived through the first phase of AI transformation—where businesses experimented with automation, language models and machine learning to improve tasks, productivity and outcomes. It showed us what was possible. In just three years since AI became widely accessible, we've entered a new era. One where AI drives decision-making. One where agentic AI doesn't wait for instructions—it takes initiative. And that, for every forward-thinking business leader, opens an entirely new frontier. As someone who's spent nearly two decades in the tech industry—and has had a front-row seat to multiple AI implementations over the last four years—I can tell you this: every new wave of innovation brings a learning curve. But agentic AI? It's not as complex as it sounds. In fact, it's surprisingly intuitive once you break it down. This is my attempt to do exactly that—to demystify agentic AI, explain the business value it can unlock and offer a practical guide to help leaders like you take the next step in your AI transformation journey. What Exactly Is An AI Agent? An AI agent is a system that can perceive its environment, make decisions and take actions autonomously to achieve specific goals—like a human assistant, but powered by AI. Think of Siri: you ask it to set a timer or play a song, and it responds. But imagine if Siri could proactively schedule meetings, follow up with colleagues and update your calendar—all without you asking. A true AI agent won't take you through interfaces of different apps—it will abstract away the complexity. You'd simply say, 'Plan a business trip to Europe,' and the tool would check your calendar, compare deals based on your preferences and deliver an itinerary, without showing you the tools it used. What powers this shift is a combination of technologies: natural language processing (to understand and generate human-like responses), large language models (for reasoning and contextual understanding), machine learning (to improve from past interactions) and orchestration tools (to connect with apps and perform tasks). When these components work together in a closed-loop system, the result is an AI agent that's capable of taking initiative, reasoning independently and completing complex tasks. The Business Benefits Here's how companies are already benefiting: • 24/7 task execution: Agents can autonomously handle routine operations—like report generation, scheduling or customer support—without human intervention or downtime. • Faster decision-making: Agents analyze data in real time and act on it immediately, reducing delays caused by manual reviews and approval chains. • Cross-app efficiency: Instead of jumping between dashboards, an AI agent can retrieve info from a CRM, update an ERP and send out an email—all from a single command. • Reduced operational costs: By automating low-value, repetitive tasks, businesses can free up human talent for strategic work while cutting overhead. • Hyper-personalized experiences: From sales outreach to supply chain adjustments, agents can tailor responses and actions based on user behavior, preferences or context. Industry Application: Manufacturing Here's how different types of agents could apply to various business functions in manufacturing: • Reactive agents for quality control: These agents respond to immediate inputs, learning from the past. Think of a visual inspection system that flags defects on the assembly line based on fixed criteria—fast, rule-based and focused on the present moment. • Proactive agents for maintenance scheduling: Proactive agents analyze real-time machine data and usage patterns to predict when equipment will need servicing. • Hybrid agents for inventory and supply chain: By blending real-time responsiveness and historical insight, hybrid agents can optimize restocking decisions. They respond to order spikes while using trends to anticipate future demands and supplier constraints. • Utility-based agents for resource allocation: Utility-based agents evaluate competing priorities to find the best outcome. In manufacturing, they might assign tasks across machines or teams based on efficiency, output targets and cost. • Learning agents for process optimization: These agents learn and improve over time. For instance, they might analyze operator techniques to optimize workflows or identify energy-saving patterns—continually improving plant performance using reinforcement learning. Common Challenges For many businesses, the biggest hurdle with agentic AI is knowing where to begin. It sounds powerful (and let's be honest, a bit magical), but without a clear understanding of how and where it fits into your operations, it becomes just another buzzword. Even when companies do take the plunge, they sometimes fall into the trap of the 'bolt-on' approach—slapping AI on top of flawed or outdated processes in hopes of a quick fix. But agentic systems amplify what's already there. If the underlying workflow is broken or inefficient, AI won't solve it—in fact, it can make things worse by reinforcing bad habits at scale. And perhaps the most underestimated challenge? Getting people on board. New tech can trigger resistance. Without proper onboarding, training and internal advocacy, even the most promising systems risk being ignored, misused or rejected by the very teams they're supposed to empower. Integrating AI Agents Integrating AI agents into your business is about solving real operational problems. To make AI agents actually deliver value, approach implementation like a business transformation. Pinpoint where things consistently break down—be it slow approvals, clunky data entry or decision-making delays. Don't automate noise. Eliminate it. Lay out each step of the process you want to improve. Flag areas where agents can make decisions or take action with little human input. Match tools to tasks. A reactive agent might be great for handling support tickets; a learning agent might be better for optimizing demand forecasts. Test the agent with live scenarios—not ideal ones. Measure responsiveness, accuracy and user friction. Treat the agent like a new hire. Gather user insights, monitor performance and tweak continuously for better results. Smart implementation starts with purpose—not with hype. Focus on impact, and the ROI will follow. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
28-05-2025
- Business
- Forbes
De-Risking Digitization: How To Transition To Advanced Systems
Ashar Samdani: CEO of Code District, a Washington-based software development firm that focuses on helping businesses incorporate modern tech getty We live in a time when AI agents are being used to help draft legal documents, detect diseases and even write breakup texts. And yet, despite the whirlwind of progress, it might surprise you to know that many small and mid-sized businesses are still buried under spreadsheets, whiteboards and sticky notes. No, they're not behind because they're lazy or unaware. I've found it's quite the opposite. They're cautious—and have every reason to be. The business world is full of modernization horror stories. Companies tried to 'go fully advanced" and ended up crashing their operations. The new system didn't talk to the old one. The staff hated it. Customers got frustrated. Deadlines were missed. Reputations were bruised. Somewhere between good intentions and poor execution, things unraveled. And this fear? It's what I've found fuels organizational inertia—the subtle but powerful resistance to change. It's easier to stick with what you know than risk the unknown, even if what you know is painfully outdated. I have had the opportunity to help businesses break free from manual chaos and make the leap to intelligent, advanced systems—without the drama. I've learned not only how to 'go advanced' but also how to do it right. Here's how you can de-risk the journey from low-tech to next-gen so your evolution doesn't become a cautionary tale. In my 15-plus years as a consultant, if there's one principle I believe in almost as much as I believe in gravity, it's this: Start with the problem. Not the tool, not the shiny platform—the problem. Too many businesses rush into digitization by asking, 'What software should we use?' when the better question is, 'What's actually broken?' Before you decide what to automate or implement, take time to map out the specific operational problems dragging down your business. • Where is the manual process slowing things down? • What bottlenecks are costing you customers or revenue? • Which inefficiencies are causing the most employee frustration? Once you've documented those, prioritize by urgency, return on investment (ROI) and strategic alignment. Fix the fire before upgrading the fireplace. For example, if your manual invoicing process is creating payment delays and cash flow issues, don't start with a customer relationship management (CRM) integration; automate billing first. This kind of problem-first thinking ensures your digitization strategy is rooted in real business needs. And, more importantly, it keeps you from spending time, money and sanity on solutions that solve nothing. Once you've mapped out the key business problems, don't jump straight into implementation. Conduct a proper discovery workshop—one that's focused less on deliverables and more on aligning your vision with execution. Think of it as building the blueprint before pouring concrete. A good discovery workshop helps clarify the why, what and how of your transformation. It ensures you don't just digitize for the sake of it—you solve real issues with the right tools and expectations. What should that workshop cover? • Define clear, SMART goals (specific, measurable, achievable, relevant and time-bound). • Understand the project's purpose. What business outcome are you targeting? • Identify user personas and map their experience with your processes today. • Outline key functionalities. What must be improved and what's a nice-to-have? • Establish your value proposition—why this matters internally and externally. • Estimate a realistic timeline and budget, including staffing and risk buffers. • Decide upfront what success looks like and how you'll measure it. Too often, mid-sized companies skip this step because it feels like an 'extra.' But it's this workshop that prevents misaligned expectations, scope creep or over-engineered solutions that don't actually help. Once the roadmap is clear, don't scale up overnight. A focused pilot program is your best bet to test assumptions, manage risk and build internal confidence. Start small by choosing a department, workflow or location where the impact can be measured and lessons quickly gathered. Set clear goals for the pilot: What will success look like in 30, 60 or 90 days? Define metrics, assign ownership and ensure you have feedback loops in place. The goal here isn't perfection. It's about stress-testing the process in a real-world setting without disrupting the entire business. It helps you surface edge cases, technical challenges and cultural resistance before they become costly blockers. Pilots give you data, not just opinions. And they give your team time to adapt. They can turn skepticism into buy-in and lessons into strategy for a smarter, safer full rollout. Even the best-laid plans can falter if the transition itself causes more chaos than progress. That's why managing change is as critical as designing it. Start with a phased rollout by running legacy and new systems in parallel where possible. Communicate clearly across departments. Train early, test often. And build feedback into every stage so frontline teams feel heard. The goal is continuity and making sure operations don't grind to a halt during adoption. Smooth transitions are less about technology and more about alignment. Get your people and processes ready, and the systems will follow. Whether you're deploying a custom-built platform or an off-the-shelf solution, if you're working with a partner for help, the success of your digitization journey depends heavily on who you choose. Look for a team that understands your industry, not just the tech. They should provide more than just installation; they should offer strategic guidance, ongoing support and tailored training to ensure long-term success. Ask about their post-launch services, how they handle change requests and how well they integrate with your internal teams. A good partner won't just deliver a system—they'll help your people master it and evolve it as your business grows. Digitization doesn't have to be disruptive or risky. By starting with the right problems, planning thoroughly, piloting smartly and partnering wisely, small to medium-sized businesses (SMBs) and small to medium-sized enterprises (SMEs) can transition safely from manual to modern systems. When done right, it's not just an upgrade—it's a competitive advantage that can pay off for years to come. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
24-04-2025
- Business
- Forbes
The AI-First Playbook: Seven Steps To Smarter Digital Transformation
Ashar Samdani: CEO of Code District, a Washington-based software development firm that focuses on helping businesses incorporate modern tech Plenty has been said, written and preached about when it comes to putting AI at the core of digital transformation. And most businesses agree. According to Gartner, 91% of organizations are pursuing "some form of digital initiative," and 87% of senior leaders say digitalization is of top importance. But as with any transformation effort—where only 30% of initiatives are considered successful—the challenge is execution, not intent. The difference now is that the economics of getting it right have fundamentally changed. The biggest shift with AI-first transformation is that it's no longer a slow, costly endeavor. Thanks to advances in generative AI and AI agents, efforts that once required months of manual work and tens of millions of dollars can now be achieved in a fraction of the time and cost. AI-first transformation is not just cheaper and faster—it's more aligned, more actionable and more scalable. But to harness it fully, a structured approach is essential. This article will walk you through practical steps to successfully make AI the center of your digital transformation. One of the most common missteps in AI-led digital transformation is starting with a tool and then looking for a reason to use it. The allure of emerging technologies like generative AI, machine learning and autonomous automation like AI agents can tempt organizations to dive in without a clear understanding of what problem they're trying to solve. But transformation should never begin with a product pitch. It should begin with a pain point. To drive real impact, leaders should identify high-friction areas in the business: where time is wasted, customer experiences suffer or operational inefficiencies linger. These pain points are the foundation of a meaningful AI transformation initiative. I find the best AI use cases are: • Business-critical: Tied to revenue, operations or customer satisfaction • Quantifiable: Capable of demonstrating measurable ROI • Scalable: Useful beyond one department • Achievable: Deliver value without taking years to implement The goal of AI-first digital transformation is not to deploy AI everywhere all at once. It's to find that one use case that could fundamentally improve the business if scaled. Look for high-value opportunities that: • Address a strategic challenge or unlock a new opportunity • Can be tested quickly in a low-risk environment • Offer clear metrics to track impact The objective isn't experimentation for its own sake. It's about creating a blueprint that can be scaled across the enterprise. Data is the foundation of AI—but not just any data. For AI systems to be effective, the data must be relevant, structured and trustworthy. I recommend you start with comprehensive data collection across departments: CRM systems, transaction records, web analytics, ERP systems, support tickets and external data feeds. Then move to cleaning: Remove duplicates, correct errors, resolve inconsistencies and handle missing values. Clean data not only improves model performance but also builds trust across stakeholders. Once cleaned, explore and profile the data to detect patterns, outliers and anomalies. This exploratory analysis helps lay the groundwork for effective feature engineering—an essential process to train your AI models. Once your data is structured and ready, the next step is selecting the right model. Not all models are created equal—and the right choice depends on the nature of the problem and the data at hand. For instance: • Predictive maintenance in manufacturing might rely on time-series forecasting or regression. • E-commerce personalization may benefit more from collaborative filtering or recommendation engines. • Document processing may be best tackled using NLP models fine-tuned for domain-specific language. This is also where technical strategy intersects with business goals. Choosing the wrong model can mean poor predictions, slow performance and wasted resources. There's no one-size-fits-all AI strategy. Businesses must choose between off-the-shelf tools, low-code platforms, custom-built solutions or hybrid approaches. Off-the-shelf AI tools can be appealing because they are fast to implement and cost-effective, though they typically offer limited customization options. In contrast, low-code and no-code platforms enable faster deployment while providing a moderate degree of flexibility. For those seeking maximum control and long-term value, I think custom solutions are ideal—especially when paired with proprietary data to create a true competitive advantage. However, hybrid models are also gaining traction, as they combine the strengths of both pre-built and custom approaches. A pragmatic approach is to follow the 80/20 rule: Leverage an off-the-shelf or low-code solution that fulfills around 80% of your needs with minimal tailoring, then customize the remaining 20% to address your unique workflows or differentiators. When selecting a solution, consider: • Integration with existing systems • Long-term scalability • Security and compliance requirements • User experience and ease of adoption • Cost versus long-term ROI Technology is only half the equation. The other half is the team that helps implement it. Selecting a vendor or development partner who understands your business context is critical. I recommend you look for partners who: • Have proven experience in your industry • Offer robust support and training • Demonstrate flexibility and technical expertise • Have a transparent road map and realistic timelines Avoid red flags like hidden fees, poor support or overpromising on capabilities and deliveries. Without clear metrics, AI initiatives often suffer from scope creep and unclear ROI. Before launching any AI initiative, define what success looks like. This could mean improvements in operational efficiency, such as time saved, reduced errors or increased throughput. Financial impact is another key indicator, whether through cost savings, revenue growth or margin improvements. Finally, customer outcomes should be considered, including higher satisfaction scores, improved net promoter scores (NPS) and stronger retention rates. The future of digital transformation isn't just digital—it's intelligent. AI-first transformation holds the potential to reduce costs, accelerate innovation and transform decision making. But only when approached strategically. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
