In today’s data-driven world, artificial intelligence (AI) and machine learning (ML) are not just ideas from the future. They are powerful tools that are about to change how businesses compete, come up with new ideas, and run their operations. However, the shift from pilot projects to broad use of AI/ML can be complicated. A fragmented strategy could lead to unrealised potential and stopped activities. That is why businesses need to build a strategic “playbook” to get the most out of AI and ML.
Why put AI and ML together? The Profitable Return on Investment
Before getting into the “how,” it’s important to go over the “why” again. There are many benefits of combining AI with ML:
- Better Decision-Making ML algorithms look at large datasets to find useful information that may be used to make judgements in many areas. Think about how predictive analytics can help you improve your supply chain or how you can make your marketing initiatives more effective.
- Operational Efficiency Make the best use of resources, simplify processes, and automate tasks that are done again and over again. AI-powered chatbots can answer customer questions, which lets human operators deal with more complicated issues.
- Innovation and The Development of New Products Speed up the creation of new and better products and services, make each customer’s experience unique, and spot new trends.
- Competitive Advantage Get a strategic edge by predicting changes in the market, understanding how customers act, and improving processes in ways that your competitors can’t.
The way Argos Labs works: The Playbook for Easy Integration
Argos Labs knows what problems businesses have when they try to use AI and ML together. Our method is meant to be strategic, focused on results, and easy to use. Here is a whole playbook:
1. Set clear business goals
- Start with the “Why” Don’t go for AI/ML just for the sake of it. Find out what specific business problems or opportunities you want to work on.
- Set Measurable Goals Use Key Performance Indicators (KPIs) to keep track of how well your AI/ML projects are doing. Are you trying to make customers happier, make more money, cut costs, or do something else?
- Focus on Impact: Give priority to projects that have the best chance of success.
2. Check to see if the data is ready
- Why Data is the King AI and ML algorithms do best when they have access to high-quality, relevant data. Look at the health of your data infrastructure right now, find any problems, and set up systems for gathering, cleaning, and preparing data.
- Data Governance Set strict rules for data governance to make sure that data is safe, of good quality, and follows rules like GDPR.
- Data Accessibility Make sure that your AI/ML development teams can easily get to the data they need.
3. Build a Team with a Lot of Skills:
- Talent Acquisition Find AI experts, ML architects, and data scientists who have the right mix of business and technical skills.
- Upskilling and Training Pay for training programs to provide your present personnel the skills they need to use AI/ML technologies.
- Cross-Functional Collaboration Get businesspeople, IT pros, and data scientists to work together.
4. Choose the Right Technologies:
- Choosing a platform Look at several AI/ML platforms and tools and choose the ones that best meet your needs and budget. You should think about how easy it is to use, how well it works with other systems, and how well it can grow.
- Open Source vs. Proprietary Compare the pros and cons of open-source frameworks like TensorFlow and PyTorch with proprietary solutions from companies like AWS, Google, and Microsoft.
- Argos Labs’ expertise Use Argos Labs’ knowledge of many AI and machine learning technologies to find the best solutions for your business.
5. Do it in steps:
Start with little projects to see if they are possible and get some practice.
- Agile Development Use agile methods to quickly make changes and iterate as requirements change.
- Continuous Monitoring: Keep an eye on how well your AI/ML models are doing and make changes as needed.
6. Stress the importance of ethics:
- Bias Detection You should be aware of any biases that may be present in your data and algorithms. Use strategies to reduce bias and make sure things are fair.
- Clarity and Openness: Try to make your AI/ML models clear and open. Understand how your models make choices and be able to explain those choices to anyone who are interested.
- Responsible AI: Follow moral rules and norms when making and using AI.
7. Work well with the systems that are already in place:
- API Integration Use APIs to connect your AI/ML models to your existing apps and workflows without any problems.
- Data Pipelines Set up strong data pipelines to make sure that data flows smoothly between your AI/ML models and data sources.
- Updating Infrastructure It is best to update your IT infrastructure to meet the needs of AI/ML workloads.
8. Measure, iterate, and scale:
- Keep an eye on key performance indicators (KPIs) Keep an eye on your KPIs often to see how well your AI/ML projects are paying off.
- Get Feedback Get feedback from users and other people who are interested to find out what needs to be better.
- Scale Strategically As you gain experience and see good results, move your AI/ML projects to other parts of your business.
Handling Multiple Files (Alternative Design)
While the drag-and-drop bot handles one file at a time, running the sequence of operations repeatedly for multiple files, a different design is needed to convert many PDFs in one action. This alternative design involves using a “folder monitor” operation to grab all files inside a specified folder. The list of files is typically stored in an array variable. A “repeat” function is then used to loop through this array variable, processing each file within the folder. The number of times the repeat loop runs can be dynamically set using the count of files in the array variable obtained from the folder monitor. This approach allows for bulk conversion. It was noted that this part of the discussion involved programmers and might require their expertise for citizens needing clarification.
Preparing for Deployment
Before deploying the bot, some final touches are demonstrated. If the bot is intended to run continuously (e.g., monitoring a folder all day), any imposed timers might need to be removed. Additionally, when building and testing, developers often create test steps within the same scenario. To prevent these test steps from running during normal operation, an “end of scenario” operation can be placed after the main bot sequence. This operation tells the runner (Pam, in this context) to ignore everything that follows it.
Deployment: Saving and Packaging
Deployment involves saving the developed scenario. The scenario file is saved to a “supervisor”. The supervisor is where the bot is managed, identified by a unique bot ID. The scenario is saved with a name (e.g., “pdf to docs”) and appears in the supervisor dashboard, ready for deployment.
One deployment method shown is packaging the bot into a standalone executable (.exe) file. This is done via the “file menu” and “make exe” option. The executable file is created, often saved to the desktop, and includes the bot ID in its name. Double-clicking this .exe file prepares the running environment (which happens quickly after the first run) and starts the bot.
Conclusion
A deliberate and clearly defined plan is needed for AI/ML to be used in business. Businesses may use AI/ML to its full potential and get a big edge over their competitors by focussing on clear business goals, data preparedness, talented teams, the correct technologies, and ethical issues. Argos Labs is your partner in this effort. They will give you the help and knowledge you need to smoothly integrate AI/ML into your organisation and reach your goals. Visit www.argos-labs.com for more information about our AI/ML solutions and how we can help you change your business.
