What is pragmatic AI?
As we know it today, artificial intelligence is what pragmatic AI is all about. It’s talking about how AI is used in real life, not how it works in theory. The goal of theoretical AI is to build intelligence that is similar to human intelligence. The goal of pragmatic AI is to improve human abilities.
Here are some examples of sensible AI uses:
- ChatGPT, a chatbot that can do everything from the map you a marketing strategy to search Google for the best lasagna recipe based on your dietary preferences
- Turn-by-turn navigation apps that use AI to find the most efficient route
- Spam filters that automatically sort through and organize emails
- Personal virtual assistants like Siri or Alexa, which can perform tasks and answer questions based on voice commands,
- AI-enabled robots that perform tasks in hazardous environments, such as exploring volcanic mountains or deep-sea diving
- Smart home devices can learn and adapt to your habits and preferences for improved efficiency and convenience.
Synonyms
- Artificial intelligence
- Machine learning
- Deep learning
- Neural networks
How to Understand Pragmatic AI
Putting AI to use in real-life
The goal of pragmatic AI is to create and use AI in real-world situations to make people more productive and effective. One of the leading causes behind the Fourth Industrial Revolution is this link between the natural and digital worlds.
AI-driven apps use advanced algorithms, machine learning, and data analysis to automate complex or dangerous jobs. A business must look at terabytes of data to find patterns and trends for a new marketing strategy.
If you do things the old way, this job could take weeks or months. You can finish the same job in minutes with AI.
Here is a short, step-by-step example of how AI could analyze this data:
1. Many data, like customer information and campaign data from the past, is fed to the AI software.
2. The info is run through machine learning algorithms. They look for trends, patterns, and connections.
3. The AI suggests a new marketing strategy that targets specific groups of people and uses specific keywords in ads based on this analysis.
4. The company starts a new campaign and tracks its performance. This information can then be used to make the AI program’s formulas even better.
In this way, AI is the next step that makes sense after regular computer software. It is a direct effect of the fact that computing power and data availability are growing exponentially.
Finding the right balance between real-world use and technical feasibility
Pragmatic AI is all about finding a balance between usefulness in the real world and technically being able to do it.
Deep learning algorithms are used in modern AI apps to look at vast amounts of data and do complex tasks, but these apps aren’t really “intelligence” at all. They are still apps for computers. They have a lot more information. Plus, they process it much faster and better.
Indeed, there is no way for AI to copy human intelligence, even though they use artificial neural networks to “replicate” how humans learn. It can’t think outside the box or make choices based on how it feels.
This is something that SaaS makers today have to think about when they come up with ideas for new products, spend money on research and development, and try to get new products on the market. They will waste money on something that doesn’t solve the problem or can’t be made yet if they don’t.
In addition to how the software will be used in the real world, founders should consider the MAYA concept (Most Advanced Yet Acceptable). This idea says that people are more likely to use new technology if they already know how to use it. That’s one reason chatbots and virtual helpers are so popular: they’re based on something we already know how to do (online customer service), but they make it easier and faster.
Why a pragmatic approach is vital for AI development
“Never say never,” but it will be long before any nonliving thing truly understands what it’s like to be human. It can solve calculus problems or proofread an essay much faster than a person, but it can’t stop thinking about its own life.
So that’s the whole point of sensible AI: if it can improve our lives, why not keep improving it to do things people can’t do?
Today, AI developers focus on solving problems for people and companies because they take a practical approach. It also keeps developers in the realm of possible in a field where many of them exist.
Examples of how pragmatic AI can be used in business
Pragmatic AI has been used in almost every field, from healthcare to finance, but its business uses are the most extensive and most well-developed. If you’re reading this, it’s almost certain that you already know many of them.
Customer Service and Help
Chatbots and virtual helpers
You’ll likely talk to a chatbot or virtual helper before a natural person when you’re looking for project management tools for your business, a new jacket for fall, or to dispute a charge on your last phone bill. It will generally appear in the bottom right corner when you visit a website or app.
There are three main goals for AI chatbots:
- Sort and screen you based on your needs (for example, send you to the right area or answer your frequently asked questions).
- We send you the best products, materials, or information based on what you say.
- If necessary, put you in touch with a natural customer service person and give them all of your information.
Reaching all your customers simultaneously is tough since people can’t work 24 hours a day. People who visit your site or app or call your support line at any time can get help from software. Also, it makes sure that everyone gets a reply right away.
Personalized conversation with the customer
What kind of connection you have with a customer depends on how quickly and well you can meet their needs. According to a study by McKinsey, 71% of customers want unique experiences, and 76% get angry when they don’t. According to another report, two-thirds of B2B buyers want the same amount of personalization or more than B2C buyers.
Personalization isn’t that hard. It means these three things:
- Quick and accurate answers
- Content that is tailored to each person’s tastes and actions
- Each problem has its unique answer
Pragmatic AI makes all of this possible. Chatbots, virtual helpers, and autoresponders ensure you can answer your customers immediately. They will learn everything they need to know about what’s happening.
If someone from your help team is free, they don’t have to ask the customer for their name, email address, and problem. They can instead get right to help them solve the problem.
How to Find Fraud
It is challenging for people to spot fraud on their own, but algorithms can spot fraudulent activities in electronic payments almost as soon as they happen. Millions of deals are handled instantly by AI and machine learning algorithms.
They’ve been doing it for years, so it’s easy for them to spot trends that could mean fraud. This helps businesses and their clients lose less money and keep their clients’ private information safe.
Optimization of Operations
Automating processes
When technology takes over a business task that used to be done by people, this is called process automation. For example, the software can run the payroll numbers for 100 workers simultaneously instead of doing it by hand every two weeks. They can be looked over by your finance team every two weeks before they are sent in.
Businesses work better when they use automation. They don’t have to do the same low-level jobs repeatedly, so they have more time to focus on driving growth.
When it comes to some tools, like billing or subscription management software, it also saves them a lot of money by finding mistakes early on before they cause lost income or problems with taxes.
Managing the supply chain
Thanks to monitors that can connect to the Internet of Things (IoT), pragmatic AI is changing logistics significantly. These sensors track how much inventory is on hand, guess when trucks will need repair, and find the best routes for transporting goods.
Some of the perks are:
- Less wait time for deliveries
- Fewer mistakes
- Lower costs (since you’re not using as many tools and taking less time on manual tasks)
They work with ERP software, which helps companies better predict demand and keep track of their stock. These systems can also find trends in how much is used and determine the best time to restock through machine learning.
Sales and marketing
Personalized ads
Because it has been around for a long time, advertising is one of the most exciting ways functional AI is used. Because it’s so subtle, we’re used to seeing ads on every website and social media site and don’t think anything of them.
How does it work, though?
1. Based on cookies and algorithms, look at what you do online.
2. They put you in a group based on location, hobbies, and preferences.
3. They’ll show ads once they find a trend.
Let’s say you looked at homes on Zillow for an hour while in Los Angeles to see a friend. A few hours later, you might see ads for apartment buildings and homebuying apps in the Los Angeles area as you scroll through Instagram or watch Hulu with ads.
Scores for Leads
Lead scoring is a way to sort sales-ready leads into groups based on their value to your business. This helps sales teams focus on high-value leads and set priorities.
- A lead is more likely to buy your goods if they fit into certain groups (for example, budget, MRR).
- People who have already interacted with your content or know your company are more likely to buy.
AI analyzes data from many sources, such as website visits, email opens, social media exchanges, and CRM data, to help businesses find leads. It looks at this information and past sales data to describe the perfect lead.
With Dynamic Deal Scoring, McKinsey’s Periscope technology goes one step further. It helps salespeople by showing them how to do their jobs and giving them real-time prices based on the type and quality of leads.
How to Predict Sales and Divide Customers Into Groups
Forecasting sales and dividing customers into groups are essential to effective sales strategies.
Pragmatic AI looks at past and present data to figure out trends in customer behavior that can help it make decisions in the future. Among these are:
- Chances to upsell
- Predicting customer drops and finding those who are likely to leave
- Figuring out the best time to get in touch with certain people
- Getting the best price
It can make more accurate sales forecasts and financial models by combining and studying customer data from multiple sources at once. Businesses can put much more faith in today’s practical AI apps than in their old Excel-based predictions.
What are the pros and cons of using pragmatic AI?
AI has a lot of benefits for almost everyone, like automation, optimization, and accuracy. However, putting it to use in business comes with its own set of challenges.
The pros
More productivity and efficiency
Pragmatic AI automates jobs that are done over and over again, giving workers more time to do more complex work that requires creativity and critical thinking. This makes both individuals and businesses more productive.
Better experience for customers
AI can help businesses give people a more personalized experience because it improves personalization and prediction. Some examples are personalized suggestions, tailored ads, and better customer service. Firms can tailor their online and offline services to each customer in real-time with customer data systems.
Cutting costs and making the best use of resources
AI can help businesses cut costs and make better use of their resources by handling tasks that used to be done by hand and improving operations. For instance, supply chain management systems that AI drives can lower the amount of inventory needed while keeping service levels the same. This gets rid of the need for extra inventory and frees up capital.
Problems to solve
Concerns about data privacy and ethics
Making sure data is kept private and the technology is used well may be the most challenging part of using AI. People aren’t sure if AI will be used for good, especially now that it’s improving at being helpful.
AI can, for example, make movies and pictures that look exactly like the real thing. People can use this technology to trick others into believing or doing things that aren’t true. For example, marketers might use it to promote their goods on TikTok and make how-to videos.
Problems with Integration and Compatibility
With the progress of technology, merging is getting more accessible, but it’s still hard to use AI. Many of the tools we already have aren’t made to work with AI, and working together could take a lot of time and money.
Maintenance and the chance to grow
Keeping the systems up to date as technology changes is one of the most complex parts of pragmatic AI creation. To stay accurate, AI models must be trained and updated constantly, which can take time and money.
Because they depend on fast technology and software, scaling AI solutions can also be expensive and complicated. Running an AI-powered SaaS with millions of users makes it very hard to keep the system up to date and expand it.
Tips for Businesses on How to Adopt Pragmatic AI Successfully
Make your goals and use cases clear.
Before businesses start implementing AI, they need to be clear on their goals and specific use cases. This will help them figure out which parts of their business could use pragmatic AI the most and which parts might have more problems with process automation.
Spend money on sound data and infrastructure.
The data quality that an AI program can use determines how well it works. Because of this, companies need to spend money gathering, organizing, and studying data to ensure it’s correct and helpful. Proper infrastructure is also essential for storing, processing, and using significant amounts of data.
Set up rules for ethics and government.
To deal with data privacy and ethics worries, businesses need to set clear rules and governance processes for using AI. This includes moral standards, rules for responsible use, and regular checks to ensure everyone follows the rules.
Spend money on training and improving your skills.
Some people don’t know enough about AI to solve its problems. More than half of US and UK businesses say they don’t have the AI ability to follow through, even though 93% say it’s at the top of their priorities and have projects planned.
The best way to deal with this problem is to put money into programs that train and improve the skills of your present employees. That way, you can build a staff that can keep up with the growth of pragmatic AI.
Always keep an eye on and update AI models.
Yes, AI can learn on its own. Not at all. It’s not a one-time fix. It needs to be constantly checked on and updated to meet business goals correctly and efficiently. Businesses need to set aside money for support and updates to ensure their AI applications learn the right things and work at their best.
What the future holds for Pragmatic AI and how it can grow
Pragmatic AI will only significantly affect people’s productivity, efficiency, and ease of life. It could make our lives easier in ways we can’t even think of yet.
I have some ideas about what the future holds for useful AI in business:
- Virtual reality and augmented reality in online shopping (Google’s Project Glass, eBay’s AR View)
- Advanced analytics for making predictions and automatic decision-making in logistics and supply chain management
- Models for predicting sales help companies improve how they sell things and talk to customers in real-time.
- Chatbots that can talk to people like real people for customer service
- Natural language processing is done automatically to make custom news stories, articles, and social media posts
- Robots and drones with AI for finishing deliveries, running warehouses, and helping people after disasters.
