Here is how to get started with AI and ML for your business
Who could possibly blame the IT manager or the CEO of a company, who has watched Spike Jonze’s 2013 film Her and concluded that artificial intelligence and machine learning signal something ominous?
While most news headlines either glamorize or sensationalize artificial intelligence, the reality is much more nuanced. Artificial intelligence has already begun to revolutionize businesses all over the world, and it is only a matter of time before everyone else will have to play catch-up. Delaying the adoption of artificial intelligence and machine learning comes at the cost of being left behind, and eventually having to hurriedly implement AI and ML.
The time is ripe now to adopt artificial intelligence and machine learning in small and incremental phases, using agile methodology. Let us take a look at why and how every business in any industry vertical should implement AI and ML, without affecting one’s core business.
Understanding machine learning and artificial intelligence in simple terms
Artificial intelligence, in layman’s terms, is a machine’s (computer’s) ability to mimic human cognition, such as learning and problem-solving. Its applications and scope range from building autonomous cars, advanced medical treatments, Internet of Things (IoT), advanced statistics and computational intelligence, etc.
Machine learning is the ability of computers to learn without explicit programming. In other words, machines can be programmed to learn independently, without human supervision or intervention. While machine learning has many applications, the most popular application is in the form of deep learning, which is about building mathematical algorithms that process large amounts of data. These algorithms are often called neural networks, as they mimic human information processing.
Artificial intelligence and machine learning will be used by almost every business to detect anomalies, recommend products, improve services, or predict trends in the very near future. More than 90% of the 100 early-stage startups we met in the last six months plan to use machine learning to improve customer experience.
Currently, businesses that have already implemented AI and ML can be classified into two groups: those that use AI/ML in their applications and services, and those that build and develop AI and ML middleware for others.
Get Started with Artificial Intelligence and Machine Learning Now
If you feel intimidated by the quick advent of AI and ML, allow us to put your worries to rest. Instead, make the best use of AI and ML by keeping aside technicalities, and by identifying simple and non-business critical areas. Look at AI and ML as an innovation project that can be started small, and built upon gradually as the technology evolves.
It is not necessary to bring dramatic and disruptive changes to your business. Practically, you can enhance every area of your business in small and non-critical ways using AI and ML. Let us look at possible scenarios in each department.
Here are some of the areas where you can introduce AI/ML without affecting your core business:
- Business intelligence
Predictive analysis helps you access trends, predict outcomes, and provide better solutions. Use old databases for rich insights, or merge AI with your BI tools to access insights and predict outcomes.
- Customer service
Choose from improving search results, boost sales, retargeting potential customers, personalizing content, engaging visitors with a chatbot that answers queries related to products, etc. The sky is the limit.
- Enterprise asset management
Use AI to manage contracts and assets, predictive behavior analysis and alarms, automated intelligent processing and standardization of data. Etc.
- Customer Experience
Provide AI chatbot assistants to your customers, or use deep insights to enhance customer experience. Use machine learning to improve search results based on user behavior.
- Human resource
Surprise your employees with accurate appraisals, incentives, and payouts. AI does not understand human concepts of prejudice and bias.
- Sales and marketing
Design and implement sales & marketing campaigns based on deep insights derived from machine learning and AI. Predictive analysis can help you develop marketing campaigns that are prophetic in nature. Manage your social media without having to be online 24/7.
- Product development and production
Machine learning is most often used in product development and service enhancements. You can use machine learning in the most innocuous manner to improve your product development and production.
- Procurement and inventory management
Track your raw materials, predict when you might need to replenish, and intelligently procure the best at the lowest rates.
Monitor fleets, track packages, and provide shipping and delivery services that are on the dot. There’s a storm approaching? Predict it before it happens, and have a contingency plan.
Strategize AI/ML implementation
Now that you know AI and ML can be implemented incrementally in almost all business areas, it is time to strategize the entire implementation process. In 6 easy steps, you can implement and use artificial intelligence and/or machine learning as part of your innovation project, without interfering with your core business.
- Identify safe targets to innovate
Think about the problems you currently have in your business, and which one is the least critical to everyday operations, if changes were brought. Choose from the list above, and identify targets that could be safely innovated with AI or ML.
- Look around for inspiration
If you are unable to decide what might be a safe target, look at what your competitors are doing. If none of your competitors are doing anything related to AI or ML, you might even want to find inspiration in adjacent industries.
- Assess your budget for innovation
It is advisable to start small, as that helps you remain confident throughout your innovation project. Assess how much you are willing to spend on bringing innovation to your chosen area. Choosing open source technologies is another tried and tested method to innovate, as it costs lesser.
- Look for agile vendors
Once you identify the area you wish to innovate, look for vendors with similar agile mindsets who believe in adopting and implementing technology incrementally.
- Implement AI innovation slowly but steadily
As discussed earlier, it is important to adopt artificial intelligence slowly, as it gives you time to adjust to the changes that are taking place in the fields of AI and ML.
- Use your innovation projects as PR exercise
Once you implement artificial intelligence or deep learning, use it as a public relations exercise. It might even help others to adopt these technologies, along with you looking like an innovator yourself.
Do not delay
Starting right now will help you to gain a competitive edge over your peers, who are probably still debating the efficacy of artificial intelligence and machine learning. In a couple of years, both these technologies will be used literally in every aspect of a business, that you might find yourself arriving too late to the scene. While artificial intelligence and machine learning may make certain jobs redundant, employees can be encouraged to focus more on delivering better customer care using rich insights derived from AI and ML.
Nuance requires you to keep humans in the loop
Both Stephen Hawking and Elon Musk, those venerable seers of modern technology, have warned against artificial intelligence, especially if its development is restricted to few large tech companies that could misuse the power they have. Thankfully, AI and ML are democratized at the moment and are not restricted to a few large companies. Also, it is important to remember that any technology can be used for both constructive and destructive purposes, just like a knife.
The onus is on us to create trust and dispel myths regarding AI/ML in our society. One way to do this is by normalizing artificial intelligence and machine learning in our lives. Chatbots have already become a normal feature in customer support, while predictive analysis is being adopted at the breakneck pace by businesses of all sizes.
For a more nuanced approach, we believe in using AI and ML where necessary, so that humans can provide better end-to-end customer services. After all, human touch, both literally and figuratively, can’t be replaced by machines. Let’s look towards the future, shall we?
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