AI? Yes, Let’s Do It! But… Where Do We Start?
AI is way more than just using ChatGPT or buying a license for a cloud service.
Artificial Intelligence (AI) has been around for decades, but only in recent years has it truly broken into the mainstream. Nowadays, it feels like everything is labeled as “AI-powered.” From smartphones to cars, even freezers — there’s hardly anything left untouched by AI. But with all the hype, we’re also feeling a bit overwhelmed. Is there anything that isn’t AI-powered anymore?
On one hand, AI has quietly permeated our daily lives more than we realize. It’s embedded in our devices and services, often functioning in the background. On the other hand, the rise of AI in everything we see — especially stories of competitors embracing it for manufacturing or creating innovative customer experiences — creates a sense of urgency. The fear of missing out on AI-driven opportunities is real, and businesses can feel like they’re falling behind if they don’t jump on the AI bandwagon. This fear can lead to hasty decisions and shortcuts that may not serve the long-term goals of the organization.
Misconceptions About AI Adoption
While tech communities might seem well-versed in AI’s capabilities, the reality on the ground is quite different. Walk into any factory floor or corporate office, and you’ll quickly find that many organizations still have more questions than answers about AI. Misconceptions abound, and many companies struggle to turn AI hype into real-world success. This is partly due to the overwhelming impact of large language models like ChatGPT, which have taken the world by storm.
ChatGPT has become a revelation for industries by showing what AI can actually do. Chances are, everyone reading this has used it at least once — for generating social media posts, summarizing documents, or even just crafting emails. It’s become a tool that consumers and businesses alike have quickly embraced. But therein lies a problem: many organizations mistakenly believe that “AI in my company” simply means subscribing to a cloud service or ChatGPT, learning a few prompts, and letting the IT department take it from there.
Yes, tools like ChatGPT are valuable. They offer real benefits, but they are just one piece of a much larger AI puzzle. If you’re relying solely on these solutions and leaving the AI journey to the IT department, you’re doing it wrong.
AI is About Your Data, Not Just Technology
AI adoption should not be an IT-driven process. Although AI is undeniably an IT technology, it goes far beyond just licensing software or using automation tools. The heart of AI for your business is transforming your data into actionable insights that work within your specific environment. So, what exactly is this data? It’s the collective knowledge that resides within your teams — knowledge about your products, services, customers, operations, and processes across every department, from customer support to the factory floor.
Yes, the IT department plays a role, but it’s just one part of the equation. AI, when done right, is about collaboration across departments. If your goal is to improve operational efficiency, reduce costs, enhance processes, or enable automation that delivers tangible value, you need more than a chatbot summarizing emails or streamlining a single workflow. You need a holistic approach that starts with your people and their expertise.
Starting with AI: Look for the Right Use Case
Many companies fall into the trap of deploying AI for the sake of it. They buy a service or infrastructure, collect data, and assume they’re set. But successful AI deployment should always start with finding the right use case. That means identifying the areas where AI can make a real difference — whether it’s solving recurring issues, improving efficiency, or automating tasks that are currently done manually.
Start by asking your teams where they see problems, bottlenecks, or risks. For example:
- Are there too many equipment failures or unexpected downtimes?
- Are certain tasks taking too long compared to competitors?
- Is there too much data for humans to process in real-time?
- Are jobs repetitive, complex, or even dangerous, leading to high employee turnover?
These pain points offer opportunities for AI to shine. The answers to these questions often exist long before they make it to the executive agenda. AI gives businesses the ability to solve problems before they escalate, but only if the right use case is identified.
The AI Deployment Journey
Once you’ve identified the use case, your AI journey can begin. Don’t be intimidated by the technical steps, such as data management and AI training. At byteLAKE, we work closely with clients to navigate these stages, gathering the right data and fine-tuning AI models to ensure they deliver on the intended use cases.
For example, we recently worked with a utility company that struggled with infrastructure management inefficiencies, frequent equipment failures, and too many distracting alerts. We deployed AI for predictive maintenance, optimized energy usage, and enhanced alarm management. The results were clear: less energy waste, fewer equipment failures, and improved decision-making during peak demand times.
The process can seem complex, but from an end-user perspective, it’s largely about providing knowledge and data. In many cases, it’s as simple as having your team describe their processes and share relevant data. Once the data is collected, the AI model is trained and ready for testing. Common examples include:
- Automated quality control through visual analytics
- Predictive maintenance using sound analytics
- Reducing energy waste with real-time data processing
Edge AI vs. Cloud AI: What’s the Best Fit?
When it comes to AI deployment, there’s often debate between using cloud-based AI or Edge AI. Cloud AI has a reputation for offering well-maintained models, but in reality, Edge AI is becoming more popular for industrial applications due to privacy, security, and latency concerns. By processing data locally, Edge AI offers faster, more reliable results with fewer dependencies on external infrastructure.
That said, cloud solutions still have their place, particularly for cross-site dashboards, backups, or general-purpose AI applications like ChatGPT. The trend is moving towards a hybrid approach, where Edge AI handles sensitive or real-time tasks locally, while the cloud provides supplementary services.
Wrapping It All Up: Your AI Journey Starts Now
To reiterate, ChatGPT and other AI tools can add value, but they’re just the tip of the iceberg. Your AI journey should be centered around solving real business challenges by leveraging your unique data and expertise. Once you identify the right use cases, you’ll be well on your way to implementing AI solutions that deliver tangible improvements — like enhanced productivity, reduced costs, and faster decision-making.
So, where do you begin? Talk to your teams. Find those use cases. And if you need help navigating the process, we at byteLAKE are here to guide you. After all, AI is more than just a buzzword — it’s the future of innovation for every business.
AI in Action: Key Takeaways from the IDC CIO Summit Adriatic
Earlier this week, on October 14th, I had the pleasure of joining the IDC CIO Summit Adriatic in Croatia alongside my partners from Lenovo and Intel®. For those who couldn’t attend, we shared key lessons from our AI deployments and demonstrated how byteLAKE, working with Lenovo Edge Servers and Intel® Distribution of OpenVINO™ toolkit, has delivered measurable business outcomes through AI-powered software.
In our first presentation, we focused on real-world AI solutions driving Industry 4.0 and Smart Factories:
- Visual Inspection: AI-based image and video analysis to automate defect detection, ensuring consistent quality control.
- Sound Analytics: AI interprets sound patterns to diagnose equipment issues and predict failures.
- Data Insights: Extract actionable insights from IoT devices, documents, and other data to improve performance and forecast trends.
- Papermaking Monitoring: AI optimizes production by monitoring the wet line, enhancing efficiency in papermaking.
Our second session focused on AI-driven predictive maintenance and energy optimization for utility companies, showcasing how AI enhances operational efficiency and performance:
- Energy Optimization: AI optimizes energy distribution and consumption, increasing efficiency while reducing costs.
- Predictive Maintenance: AI predicts equipment failures, optimizing maintenance schedules to minimize downtime and lower costs.
- Data Analytics for Decision-Making: AI transforms data streams into actionable insights, enabling smarter, faster decisions with human-like interactions powered by Generative AI (GenAI).
Afterward, we engaged in a roundtable discussion on leveraging AI for operational excellence in areas like quality control, predictive maintenance, and energy optimization. For those who joined us, thank you for your insights and comments. These conversations inspired me to write this article, as I believe that while industries understand AI’s value, many still struggle with where to start and how to develop tailored solutions rather than relying on generic tools.
You can download our presentation here: https://www.bytelake.com/en/CIOSummit-Adriatic-2024
For more details or to discuss AI deployment in your company, feel free to contact byteLAKE’s founding team at:
