Table of Contents
What is Pragmatic AI?
Pragmatic AI is essentially artificial intelligence as we know it today. It refers to the practical applications of AI, rather than theoretical concepts. Unlike theoretical AI, which aims to recreate human-like intelligence, pragmatic AI is designed to augment and enhance human abilities.
Examples of pragmatic AI applications include:
- ChatGPT, a chatbot that can do everything from map you a marketing strategy to search Google for the best lasagna recipe based on your dietary preferences
- Turn-by-turn navigation apps, which 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, which can learn and adapt to your habits and preferences for improved efficiency and convenience.
In theory, artificial intelligence can replicate human intelligence, creativity, and decision-making capacity. In practice, AI and machine learning computer programs can only learn from past data, progressive learning algorithms, and reinforcement techniques. So, they’re limited by the data they’re given.
- Pragmatic AI = the technology we use today
- Pure artificial intelligence = something from a Sci-Fi film
- Artificial intelligence
- Machine learning
- Deep learning
- Neural networks
Understanding Pragmatic AI
Practical Implementation of AI
Pragmatic AI focuses on developing and implementing practical AI applications that enhance human productivity and effectiveness. It’s one of the largest forces behind the Fourth Industrial Revolution — it bridges the gap between the human experience and its digital counterpart.
AI-driven applications use advanced algorithms, machine learning, and data analysis techniques to automate complex or risky tasks. For example, imagine a company that needs to analyze terabytes of data to identify patterns and trends for a new marketing campaign.
Using traditional methods, this task could take weeks or even months. With AI, you can complete the same task within minutes.
Here’s a brief step-by-step example of how an AI application could analyze this data:
- The AI program is fed a large dataset that includes customer information and past marketing campaigns.
- Machine learning algorithms process the data. They identify patterns, trends, and correlations.
- Based on this analysis, the AI recommends a new marketing campaign that targets specific demographics and uses certain keywords in advertisements.
- The company implements the new campaign and tracks its success, which can then be used to improve the AI program’s algorithms further.
In this sense, AI is essentially the logical next step up from traditional computer software. It directly results from the exponential growth of computing power and data availability.
Balancing Real-World Application and Technical Feasibility
At its core, pragmatic AI is about balancing real-world use cases and technical feasibility.
While today’s AI applications can use deep learning algorithms to analyze massive amounts of data and perform complex tasks, they aren’t really “intelligence” at all. They’re still computer software apps. They just have a lot more data. And they process it a lot faster (and more intuitively).
Even though they use artificial neural networks to “replicate” the human learning process, this means AI cannot truly replicate human intelligence. It lacks the ability to think creatively or make emotional decisions
Today’s SaaS developers have to take this into account when ideating new products, investing in R&D, and attempting to bring new products to market. Otherwise, they’ll overspend on something that doesn’t solve the intended problem or isn’t possible to create yet.
Beyond real-world application, founders need to consider the MAYA principle (Most Advanced Yet Acceptable) of software adoption. This is the idea that people are more likely to adopt new technology if it’s familiar and easy to understand. That’s one reason why chatbots and virtual assistants have become so popular — they’re based on a concept we already know (online customer service) but with added convenience and efficiency.
Importance of Pragmatic Approach in AI Development
“Never say never,” but we’re a long way away from any sort of non-living being having a true understanding of the full human experience. Sure, it can run calculus equations or proofread an essay way faster than any human, but it can’t take a step back and reflect on its own existence.
That’s the whole point of pragmatic AI: If it has the capacity to make our lives better, why not continue evolving it to achieve goals humans can’t accomplish themselves?
A pragmatic approach to AI development ensures today’s innovators focus on solving problems for people and businesses. And it keeps developers within the realm of possibility in a field where the possibilities seem endless.
Use Cases of Pragmatic AI in Business Operations
Although pragmatic AI has touched just about every industry from healthcare to finance, its business use cases are among the most vast and developed. If you’re reading this, there’s almost a 100% chance you’re familiar with many of them.
Customer Service and Support
1. Chatbots and Virtual Assistants
Whether you’re evaluating project management tools for your company, finding a new jacket for autumn, or disputing a charge on your last phone bill, you’ll probably talk to a chatbot or virtual assistant before a human. When you navigate to an application or website, it’ll usually pop up in the bottom right corner.
AI chatbots have three main goals:
- Screen and filter you based on your needs (e.g., route you to the applicable department, answer FAQs)
- Route you to the best products, content, or information based on your input
- Connect you to a human customer service representative if needed (and relay all your information to them)
Reaching every customer at once is incredibly challenging, especially since humans can’t work 24 hours per day. But software can help anyone who visits your site/app or calls your support line, 24/7. It also ensures everyone receives a response right away.
2. Personalized Customer Interaction
A customer’s relationship with your company is largely dependent on how quickly and effectively you can meet their needs. McKinsey research shows that 71% of consumers expect personalized experiences and 76% become frustrated when they don’t receive them. Another report indicates that two-thirds of B2B buyers expect the same (or greater) levels of personalization as their B2C counterparts.
Personalization isn’t that difficult. It means three things:
- Fast, timely responses
- Customized content based on individual preferences and behavior
- Unique solutions to each problem
All of this is feasible through pragmatic AI. Chatbots, virtual assistants, and autoresponders ensure your customers receive immediate replies. And they’ll gather all the information they need about the situation.
When someone from your support team is available, they can skip the part where they ask for the customer’s name, email, and problem. Instead, they can jump straight into helping them resolve the issue.
3. Fraud Detection
Fraud detection is nearly impossible for humans to accomplish on their own, but algorithms identify fraudulent activities in electronic payments almost as soon as they happen. AI and machine learning algorithms process millions of transactions in real time.
Since they’ve been doing it for years, they can easily spot patterns that may indicate fraud. This helps companies and their customers reduce financial losses and protect their customers’ sensitive data.
1. Process Automation
Process automation refers to any business operation where technology replaces human effort. For example, instead of manually calculating payroll for 100 employees every two weeks, software can continuously run the numbers. Every two weeks, your finance team can review them before submission.
Automation makes businesses more efficient. It frees them from repetitive, low-level tasks and gives them more time to focus on driving growth.
In the case of certain tools like billing or subscription management software, it also saves them significant amounts of money by catching discrepancies before they become revenue leakage or tax compliance issues.
2. Supply Chain Management
Logistics is one of the areas in which pragmatic AI is most disruptive, thanks to IoT-enabled sensors. These sensors track inventory levels, predict maintenance needs for trucks, and optimize transportation routes.
The benefits include:
- Faster delivery times
- Fewer errors
- Lower costs (because you’re using fewer resources and spending less time on manual processes)
They integrate with ERP software, so businesses can forecast demand and manage inventory more accurately. Through machine learning, these systems can also recognize patterns in consumption levels and determine the best time for restocking.
Marketing and Sales
1. Targeted Advertising
Advertising is one of the most interesting applications of pragmatic AI because it’s been in place for a long time. It’s so subtle that we’re used to seeing ads on the side of every webpage and social media platform, and accepting them as part of our online experience.
But how does it work?
- Algorithms analyze your internet activity based on cookies
- They segment you into an audience based on your interests, preferences, and location
- Once they’ve identified a pattern, they’ll show you ads for those things
Let’s say you spent an hour looking at apartments on Zillow while you were visiting your friend in Los Angeles. A few hours later, you might scroll through Instagram or watch Hulu with ads and notice ads from apartment complexes and homebuying apps in the LA area.
2. Lead Scoring
Lead scoring is the process of ranking sales-ready leads based on their potential value to your business. This helps sales teams prioritize their efforts and focus on high-value leads.
- If a lead fits into certain categories (e.g., budget, MRR), they’re more likely to afford your product.
- If they’ve interacted with your content before or are familiar with your brand are more likely to convert.
AI helps companies score leads by analyzing data from multiple sources, including website visits, email opens, social media interactions, and CRM data. It compares this with historical sales data to create a profile of the ideal lead.
2. Sales Forecasting and Customer Segmentation
Pragmatic AI uses historical and real-time data to analyze customer behavior and identify patterns that can inform future decisions. This includes:
- Upsell opportunities
- Predicting churn rates and spotting at-risk customers
- Determining the best time to reach out to specific customers
- Price optimization
Its ability to package and analyze customer data from multiple sources at once allows it to create more accurate sales projections and financial models. Businesses can trust today’s pragmatic AI applications much more than their traditional Excel-based forecasts.
Advantages and Challenges of Implementing Pragmatic AI
While AI offers significant advantages for seemingly everyone — automation, optimization, accuracy — implementing it into business operations also brings its own set of challenges.
1. Improved Efficiency and Productivity
Pragmatic AI automates repetitive tasks, freeing up time for employees to focus on higher-level work that requires creativity and critical thinking. This increases both individual and organizational productivity.
2. Enhanced Customer Experience
Since it improves personalization and prediction capabilities, AI can help businesses offer customers a more tailored experience. This includes personalized recommendations, targeted advertising, and improved customer support. With customer data platforms, companies can even personalize their web and service-based experiences in real-time.
3. Cost Reduction and Resource Optimization
By automating manual processes and optimizing operations, AI can help businesses lower costs and allocate resources more efficiently. For example, AI-powered supply chain management systems can reduce inventory levels while maintaining service levels, reducing the need for excess inventory and freeing up capital.
1. Data Privacy and Ethical Concerns
Perhaps the biggest challenge with implementing AI is ensuring data privacy and ethical use of the technology. Particularly with advances in pragmatic AI today, people question whether its forces will be used for good.
AI can create videos and images that are indistinguishable from reality, for example. While marketers might use this to advertise their products on TikTok and make how-tos, people can use this technology to manipulate others into false beliefs or actions.
2. Integration and Compatibility Issues
Although integration has become easier with advancements in technology, it’s still a challenge when implementing AI. Many existing systems are not designed to work with AI and may require significant time and resources to integrate successfully.
3. Maintenance and Scalability
One of the most difficult aspects of pragmatic AI development is maintaining and updating the systems as technology evolves. AI models require continuous training and updating to remain accurate, which can be time-consuming and expensive.
Additionally, scaling AI solutions can be costly and complex due to their reliance on high-performance hardware and software. When you’re running an AI-powered SaaS with millions of users, maintaining and scaling the infrastructure becomes a massive undertaking.
Strategies for Successful Adoption of Pragmatic AI in Business
Define Clear Objectives and Use Cases
Before embarking on an AI implementation journey, businesses need to identify their objectives and specific use cases. This will help them determine which areas of their operations stand to benefit the most from pragmatic AI and where workflow automation might cause additional friction.
Invest in Quality Data and Infrastructure
The success of any pragmatic AI application depends on the quality of data it has access to. Therefore, businesses must invest in collecting, organizing, and analyzing data to ensure it’s accurate and applicable. Additionally, having the right infrastructure in place is crucial for storing, processing, and utilizing large volumes of data.
Establish Ethical Guidelines and Governance
Businesses have to establish clear guidelines and governance protocols for their AI implementations to address data privacy and ethical concerns. This includes ethical principles, responsible use policies, and regular audits to ensure compliance.
Invest in Training and Skill Development
The skills gap is one of the biggest challenges AI faces today. Although 93% of US and UK businesses say it’s at the top of their priority list (with projects already planned), more than half of them admit they don’t have the in-house AI talent to actually follow through.
The best way to address this challenge is to invest in training and skill development programs for your current employees. That way, you can build a workforce that works alongside pragmatic AI’s growth.
Continuously Monitor and Update AI Models
Yes, AI can teach itself. No, it is not a one-and-done solution. It requires continuous monitoring and updating to achieve business objectives accurately and effectively. Businesses need to allocate resources for maintenance and updates to ensure their AI applications learn the right information and perform optimally.
Future Trends and Growth Opportunities for Pragmatic AI
Pragmatic AI’s impact on human productivity, efficiency, and convenience will only increase. It has the power to make our lives easier in ways we can’t even imagine yet.
Here’s a speculative look at what the future holds for pragmatic AI in business:
- VR and augmented reality in online shopping experiences (e.g., Amazon’s AR View)
- Advanced predictive analytics and automated decision-making in supply chain management and logistics
- Sales forecasting models that help businesses optimize their sales activities to interact with customers better in real time.
- Chatbots with human-like conversational capabilities for customer service
- Automated natural language processing to generate personalized news, articles, and social media posts
- AI-powered robots and drones for last-mile delivery, warehouse operations, and disaster response.
People Also Ask
What is the difference between pragmatic AI and pure AI?
Pure AI aims to replicate human-level intelligence and consciousness. It’s what Elon Musk famously says he’s afraid of. On the other hand, pragmatic AI focuses on solving specific tasks and problems without necessarily minimicing human intelligence.
What is an example of pragmatic AI?
An example of pragmatic AI is turn-by-turn navigation apps like Google Maps or Waze. These applications use machine learning and real-time data to provide the most efficient route to a destination based on traffic conditions, user input, and historical data.