Artificial intelligence and machine learning (ML) are fast becoming a part of our lives, professional or domestic. Virtual assistants, chatbots, data-driven models for better decision-making, data analytics, and much more result from implementing AI and enterprise ML in the business.
AI-powered tools streamline the processes and help enterprises improve quality, increase productivity, enhance customer satisfaction, and get more returns. SMEs can either build an internal team to integrate AI into their business systems or hire AI services offered by the leading consulting companies.
Though AI and ML improve the performance of an enterprise, things can quickly go wrong if you don’t have a proper plan to integrate AI into your business. In this blog, we’ll read how to implement ML models in your organization successfully.
We know about artificial intelligence and are aware that machine learning is a part of it. But what exactly does a machine learning algorithm do?
What is Machine Learning?
A machine learning algorithm is software that uses historical and real-time data to help enterprises trace patterns, predict trends, detect fraud, analyze customer behavior, and provide personalized suggestions. The algorithm is written in a way that uses the feedback in the system to improve and deliver better results. Over time, enterprise ML algorithms can perfect themselves and provide accurate predictions.
Of course, ML is much more than a simplified explanation. There are different types of ML based on the kind of algorithm used (or how the software approaches learning)
Types of Machine Learning
Data is a common factor for any machine learning.
- Unsupervised Learning: The data sets used here are unlabeled. The algorithm will process the data sets to identify if there’s any connection between them. The data and the final results are both predetermined. Ex- Anomaly detection, clustering, etc.
- Supervised Learning: Data scientists input labeled data and define the variables/ parameters that need to be considered when the algorithm processes the data sets to find correlations. Ex- Regression modeling, binary classification.
- Semi-Supervised Learning: This is a mix of the above two types. Data scientists feed labeled data but do not define the parameters. The algorithm can create its own set of parameters to come up with a result. Ex- Data labeling, fraud detection.
- Reinforcement Learning: RL is where data scientists train the algorithm to complete a process based on the given set of rules. The algorithm can create its own steps during the process. Ex- Resource management, robotics.
Reasons to Invest in Machine Learning
With AI and ML making great headway in the market, it has become essential for several enterprises to invest in the technology. The following are some reasons you should invest in enterprise machine learning and how it can help achieve your business’s short-term and long-term goals.
Inventory management and maintenance is a labor-intensive and time-consuming process. It is even harder for large-scale enterprises with vast production. Machine learning simplifies the process by automating inventory maintenance to minimize the need for human intervention.
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Reduce Work Pressure
When repetitive tasks are automated, employees do not have to spend most of their time doing the same thing repeatedly. Therefore, employees have more time to devote to their projects and less stress completing them on time. Also, with virtual assistants and chatbots enabling self-servicing within the Enterprise, employees can be empowered to become more productive without feeling the pressure.
Machine learning algorithms can process data in real-time and detect the latest trends in the market. Suggestions about changing the product’s price, reaching out to a new target audience, managing demand with supply, etc., are possible.
Data Sorting and Analytics
AI and ML models are an inherent part of data analytics. From collecting data to cleaning and sorting it and data labeling, machine learning can make things easier and complete the task in less time. The data science teams can use enterprise ML models to analyze data faster than before.
AI and ML software provide accurate insights and predictions that help make the right decisions for the Enterprise. The reports produced are easy to read and can be presented in any format (graphical, tabular, textual, etc.) so that you can understand the insights and know where things stand.
Data and System Security
Data forms a vital part of every business and has to be protected from external forces. The machine learning algorithm can help enterprises enhance the overall security system in the business. The latest antivirus and spamware software is built using AI and machine learning to identify and prevent cybercrime before affecting the business.
Fraud Detection and Prevention
When discussing cybercrime, we should also mention fraudulent transactions commonly seen in the eCommerce, insurance, and banking industries. The machine learning algorithm can detect such transactions and alert employees. Many insurance companies and banking institutions have invested in ML-based fraud detection tools. Retailers and eCommerce business owners also integrate AI solutions with their business systems for fake transaction and refund abuse prevention.
How to Ensure the Success of Enterprise ML
Let us look at the steps for a successful enterprise ML implementation and getting the expected results for your business.
1. Understanding AI and ML and Becoming Familiar with Them
The first step to implementing anything would be to know what it is. Unless you and your employees understand how artificial intelligence and machine learning can help you improve, adopting new technology will not help much.
Several ML consulting companies assist right from the start and continue to offer support even afterward. They help in training your employees to work on the ML models and increase their work efficiency. Read the use cases shared by the consulting companies and download the whitepapers and understand them. Join online crash courses or training programs to gain a comprehensive idea about AI and ML.
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2. Knowing Why You Need ML- Identify the Problems ML Will Solve in the Enterprise
Why do you want to invest in Enterprise AI? Which issues do you want to tackle by integrating AI software into the business systems? Saying AI and enterprise ML will simplify the workflow is not enough. You must know where and how to use machine learning algorithms.
Start by making a list of problems and gaps in your business system that can be solved using ML tools. Which problems can be solved using NLP, ML, DL, computer vision, etc., and what kind of AI tools will you need to integrate into the business?
3. Prioritizing What You Want from ML and Acknowledging the Talent Gap
Investing in ML is a costly affair, whether you own a startup or a large-scale enterprise. You have to prioritize the areas where ML will first be implemented to scale it throughout the organization later.
It would help if you also considered your existing talent pool. Can your employees adapt to the changing systems? How many will have to be trained in the first batch? How many new employees should you hire? Even if you choose a Machine Learning consulting company to help you with the transition, you will need to train your employees to use the new technology. How much will it cost, and what sort of training programs do you need?
4. Building the Data Fabric in the Enterprise
It is only recently that we’ve started considering data as a valuable asset. There is no point in storing data in the warehouse and silos when you hardly use it for decision-making. To implement the data-driven model in the Enterprise, you will need to build a data fabric where important data is processed for insights and predictions. Having a high-end enterprise ML model will be of little use if the data you feed is of inferior quality.
5. Hiring Experts to Assist You with the Project
It is always better to take the help of experts when getting into AI-based projects. Find a reliable and experienced offshore consulting company. Hire an AI team to design and develop a machine learning model for your business. This team will work with your internal teams and streamline the business processes for increased efficiency and productivity. Though you can hire individual professionals and freelancers, a team that comes from a well-known consulting company will be a better choice for SMEs.
6. Building a Lab Environment for the Team to Work
Once your expert team has been finalized, you need to set up a lab for them to work. The team needs an environment conducive to develop successful prototypes that will later be scaled to suit the Enterprise’s needs. You need to provide quality data sets, advanced tools, and the necessary setup to build, test, and run the prototypes. A lab environment makes it easy for the team to record the developments, assess errors, and rebuild better models.
7. Starting with a Pilot Project and Creating Successful Pilot Models
Going for an enterprise-wide AI implementation is not recommended. It is safer to start with a pilot project and then scale it for each department. Start small and make sure that the Project is successful. You will also need to invest only a fraction of the entire amount required for it. Moreover, pilot projects don’t take too much time. The team will also consist of 4- 5 experts. The other departments can continue working with the existing systems.
There will be minimal disruption to your business processes when working on pilot projects compared to going for full-level revamping and upgrades.
8. Focusing on Data Storage Requirements and Planning for the Long Term
Machine learning relies entirely on data. So, where are you going to store such vast amounts of data? Should you opt for a data warehouse or a data lake? If it’s the former, how can you choose the right data warehouse for your business? Will it be on-premises or in the cloud?
You will need definite answers to these questions. A fast, cost-effective, and optimized data storage facility with flexible and scalable services would be a suitable choice for most SMEs. Furthermore, you need to consider data security and privacy and have a long-term plan to ensure that enterprise ML will be an inherent part of your business system.
9. Scaling the Pilot Project to Enterprise-Level Adoption
Once your pilot project is successful, and you have a proper data storage facility, you can start with the enterprise-wide adoption of the ML model. However, this is not as easy as it sounds. The same model cannot be used in every department. The sales team needs a different ML algorithm than the finance department and so on. Experts will help you define the parameters for each requirement and develop an enterprise ML model accordingly. Simultaneously, you will need to incorporate AI tools as part of the day-to-day business operations. That will make it easy for employees to adopt AI.
10. Balancing the Business Systems and Gradually Driving a Cultural Change in the Enterprise
Two factors crucial for the successful implementation of enterprise ML are balancing the use of technology and how your employees accept the changes in the work culture. Overarching the AI system is a mistake many enterprises make. Make sure to align the team goals, requirements, and resources to use AI tools in the business.
And finally, have a detailed plan for work culture transformation in the Enterprise. Try to keep it simple and stress-free for the employees. Don’t rush the changes. Create an environment where digital transformation is seen as a part of the job and a continuous process.
With an increasing demand for AI services in the market, you might be tempted to adopt the latest technology as soon as possible. Although it is a time-consuming process and requires a particular investment, it is worthwhile. You will have more chances of success if you educate yourself about machine learning before making any significant changes. Talk to the AI consulting companies for more information. Hire their experts to guide you through every step of the process.
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