Artificial intelligence (AI) is the future of the technology-driven world. With every day passing, AI is revealing its potential bright side in different domains. Meanwhile, all tech giants such as Google, Microsoft, and Amazon deploy and implement intelligent methodologies across all the segments in their tech framework. AI model for business can help businesses streamline their processes and drive growth.

This advancement in AI and Machine Learning (ML) methodologies, along with the support of rich computational power, is transforming our tech world like never before. By unleashing the latent potential of all the data that organizations collect, “AI and ML are indistinguishable from magic”, as C. Clarke said quoted for sufficiently advanced technologies. How is this happening so fast? Because recent breakthroughs in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) put the cup of AI on everyone’s table.

Using AI for Businesses Process

For businesses, AI can be deployed in different organizational processes such as business intelligence (BI) to deliver actionable information, data mining to improve customer engagement, and other automation tools to optimizing supply chain functionality.

Step 1: Understand the difference between AI and ML

People get confused when it comes to choosing between AI and ML. If you’re troubling with the same situation, let’s begin by exploring the differences between Artificial Intelligence and Machine Learning.

Also Read: Data Science Vs Data Analytics Vs Machine Learning: Know the Difference

The two terms are often used interchangeably, but they have some notable differences that impact their manifestation. If you can differentiate between them, you certainly know which technology to implement. Let’s dive into further details.

What is Artificial Intelligence?

Artificial Intelligence (AI) is an area of computer science that deals with developing intelligent machines that can think, respond, and solve problems just like humans can do. To make those machines act intelligently, they must be richly provided with enough information about the surrounding world.

Artificial Intelligence was coined in 1956 when experts started finding ways to improve the problem-solving capability of computers. In the early 2000s, the development in this field started to acquire notable speed. Until now, AI has become a crucial element of every modern technology.

Some renowned examples of AI include autonomous vehicles, speech-recognition personal assistants, face-recognition systems, etc. Several other AI-based modern solutions have primarily used various business processes to automate and streamline repetitive tasks, improve customer engagement, targeted marketing campaigns, and increase operational capacity with less human resources.

What is Machine Learning?

Machine Learning is the methodology in Artificial Intelligence that uses computer algorithms and statistical models to train machines over massive datasets. By learning and extracting insights from data to conclude, make predictions & rational decisions for business-oriented purposes, ML serves as a state-of-the-art aspect of technology.

ML models don’t require human assistance to learn through data patterns. Instead, it’s enough to give some necessary parameters and direction to analyze and compare, and they can figure out how to automatically utilize this information.

In an actual sense, Machine Learning works just like the cognitive process of the human brain to acquire knowledge. Consider a toddler that is learning how to speak. It gathers information from the surrounding, processes this information, and starts making meaningful sounds of its own after some time. This ability and skill began to grow with time. ML works in the same way. A machine gathers data inputs, breaks them down, builds connections, and translates them to deliver an intelligent solution.

Now you can differentiate between AI and ML; let’s answer two essential questions about the ongoing thread before discussing AI implementation.

How can AI improve business effectiveness?

There is no absolute answer to this question as it depends on your specific needs and expectations. But some evident and promising main advantages include:

  • Increase Employee Productivity
  • Improve Marketing Strategies
  • Spare Time and Energy
  • Reduce Human Error Probability
  • Get the best of Businesses Output
  • Maximize Sales
  • Open Opputininity Corridors
  • Boost Revenue

Where is AI ineffective?

Despite being a superpower in the technology domain, AI also has some limitations in certain circumstances. To avoid any trouble and loss of investment, you must acknowledge what you should not expect from AI.

Awareness Limitation

Machines cannot program themselves as they lack awareness regarding our complex real-world problems. Thus, you cannot expect AI to understand our reality and do everything on its own.

Generate creative content

AI can create content using data but cannot maintain creativity in writing content or a blog. You cannot expect an AI to deliver a piece of art.

Ethical decision-making

Machines lack feelings and emotional intelligence because they don’t have consciousness. So, we can’t let them crack the decisive actions and make moral judgments for people that enjoy varying degrees of opinions and mindsets, and even humans fail to do so.

Dependence or Independence

AI can only help humans but cannot replace them. Several job roles are going to end due to machines taking over, but it comes amidst the introduction of several other job roles like data scientists, data analysts, and business analysts, etc. We cannot blindly trust AI to make decisions on our behalf, and we must appreciate this performance gap.

Innovation and invention

AI can extract insights and learn from data, but its ability to build conclusions is limited to some extent as it cannot be creative and comes up with innovative ideas or out-of-the-box solutions.

Step 2: Define your business needs

After acknowledging differences between Artificial Intelligence &Machine Learning, exploring the limitations and capabilities of AI, and filtering fact from fiction. Now you need to consider what you’re looking for and how these two technologies can help you get where you want?

First of all, outline the problems you want AI to solve by trying to answer these five questions:

  • What outcome(s) are you expecting?
  • What are the primary roadblocks in achieving these outcomes?
  • How can AI add value to your business that other measures fail to do?
  • How can you define success?
  • How much data you have readily available, and what additional data do you need to employ?

The answers to these questions effectively define your business goals clearly, then head towards the optimistic solution.

Step 3: Prioritize the main driver(s) of value

Once you’ve defined your business goal, you need to rectify your prioritizations regarding your AI project’s potential business and financial benefits. You should consider all the plausible AI solutions and try to relate each option with dedicated returns. To do so, focus on short-term objectives and encapsulate either the financial or business value that best suits you as per your circumstances and conditions.

While outlining your objectives, refrain from ignoring the value drivers such as increased value for consumers or enhanced employee productivity and feasibility. Must consider the aspects and domain where machines can outperform the human factor, especially regarding iterative or cyclic tasks.

Beware of not implement solutions based on fiction or impractical optimism. Popular opinion is sometimes not a sane idea.

Instead, consider if you can efficiently augment a solution into your work routine, analyze how it sustains your business model, and evaluate whether integrating an AI-based solution to your existing business model would increase your operational capability over the long run.

Step 4: Evaluate your internal capabilities

There’s often a void between what you want to do and what you can acquire within a specific period. Therefore, after aligning and sorting your priorities, it’s required to decide which approach best suits your condition. It can help in:

  • Developing a new solution using internal resources
  • Employing a ready-made product
  • Collaborating with a partner to build an AI-based solution
  • Outsourcing the resources or a team for AI development and integration

Again, choose the option that best suits your objective and available resources. Keep in mind to do in-depth research on existing solutions before jumping into the lengthy process of development. Try to find a product that meets your criteria and integrate it into the current business model. This is one of the most cost-effective practices.

Step 5: Consider consulting a domain specialist

If you already have a highly skilled developer team, they can just maybe build your AI project off their own back. Regardless, it could help to consult with domain specialists before they start.

Developing AI is not the same as building typical software. AI is a hyper-specific specialism that’s difficult to learn. It requires lots of experience and a particular combination of skills to create algorithms that can teach machines to think, improve, and optimize your business workflows.

If you have any doubts, you may simply choose to outsource your AI development to an agency specialized in big data, AI, and machine learning. AI agencies not only have the knowledge and experience to maximize your chance for success, but they also have a process that could help avoid any mistakes, both in planning and production.

Step 6: Prepare your data

AI is a kind of superpower. To make it perform at its best, you need high-quality and clean data. But what does clean mean in this scenario?
The clean dataset is the one that is:

  • Free from inconsistent information
  • Accurate up to the highest degree possible
  • Organized in such a way that it contains all the necessary data points required by an algorithm

Data is the key. Even the most advanced algorithms cannot return you the desired outcomes if you lack high quality and clean data set. The prerequisite to AI is to organize, filter, and enlarge your dataset as much as you can.

Indeed, investing in the quality of data is something you’ll never regret. Integrating an AI-based solution is not the liner or one-time task like mainstream software deployments. The process involves a series of scalable solutions and updates, but you must build their foundations on pure data to adapt to this development and integration environment. Similarly, the more data you have, the better and accurate outcomes your AI solution can provide you.

After getting your data prepared and refined, don’t forget to make it secure and protected. Mainstream security measures like encryption, anti-malware apps, or a VPN are enough to protect your data asset. Don’t hesitate to invest in modern security infrastructure.

Step 7: You’re ready to start – but start small

Approaching this step, you’re almost prepared to start. It’s good to stay a little insecure. Stay attentive and selective while feeding your data to the AI. Refrain from installing all your data in the machine and waiting for ripened fruits.

Start by feeding the algorithm with a sample dataset and use AI to evaluate the results. If the odds turned out to be in favor, launch the solution carefully with necessary preventive measures.

You can track the performance of your newly installed AI against the train set and then gradually start supplying the system with your test set that the machine has never read before.

The strategies above serve as preventive measures and strategic steps to successfully build a dedicated AI solution without any plausible loss or damages.

AI Solutions You Can Implement Today

Here are some practical examples of AI applications that businesses are already using nowadays. Modern companies implement these solutions to automate and enhance business processes, gain a competitive edge over rivals, and boost their investment (ROI).

AI-based Recommendation Engines

AI-based product research tools are among the most trending artificial intelligence applications in the retail and e-commerce industry. They assist companies and business owners in predicting consumer behavior and buying patterns to offer personalized advertisements, enhanced engagement, and uplift revenue via upselling and cross-selling.

According to research, recommendation engines drive around 25% more site traffic and 25% more revenue and boost the probability of confirmed orders by over 10% as an average.

These solutions utilize algorithms gathering historical data such as past purchases, product search, customer demographics, and build recommendations to suggest each client “things they may also like” and propel higher sales. They also make use of content-based filtering and collaborative filtering. The first method considers keywords typed by customers when searching for products online; the second makes shopping predictions based on customer behavior and preferences.

(Chat)bots

Today, it’s becoming ridiculous to be surfing any site without ever falling prey to the chatbot. Bots have penetrated the abyss of the enterprise domain. Many businesses rely on utilities like Google Dialog flow or Motion.ai to develop personalized chatbots and integrate the interactive medium on their sites to engage more customers intelligently and smartly.

Since AI-based bots are generally linked with a communication medium, they can also prove helpful in handling routine tasks. Intelligent bots are becoming human counterparts in scheduling appointments, sending emails, and public dealing up to a great extent in domains like travel bookings, order confirmation, etc.

Business Process AI Automation

Another notable aspect of the deployment of artificial intelligence in business models is business process automation (BPA). This methodology addresses the problems associated with automating iterative business processes and routines that enable an organization to save effort, time, and human resources for core business processes and areas. It allows companies to utilize the full potential and skills of employees for more significant problems and tasks. Irrespective of the nature of the iterative process or routine, we can streamline and automate it by integrating AI and ML solutions.

For instance, consider extending the influence and role of Artificial Intelligence and Machine Learning in airline ticketing services. BPA model can manage all functions related to ticket availabilities, perform text recognition to recognize customer’s interests, prioritize seat availability options by filtering all the data points that reveal customer’s mindsets, and finally inform public service agents about complaints and issues addressed immediately.

Existing Artificial Intelligence solutions also help businesses to streamline human resource departments in the hiring domain. For instance, an AI-driven system can handle the delivery and receipt of recommended documents. It can also notify new employees about the company policies, rules, and regulations and recommend necessary procedures to practice as they come onboard. ML and AI-based solutions can also respond to some FAQs that new employees are likely to ask.

Customer’s Behaviour Prediction

Imagine if you know with high certainty what your customers will most likely buy and use that insight to boost sales by offering packages. AI gives a magical wand that can do it for you. Predicting customer behavior with predictive analysis allows businesses to engage more customers effectively and increase sales by offering relevant products and services.

By mining the data from eCommerce sites and social media, predictive AI algorithms capitalize on the deep insights they extract from every customer’s data and develop an offer with a high probability that customers are more likely to buy. This capability provides retail and eCommerce businesses with the gateway to engage favorable customers at a favorable time, prioritize those who are more likely to purchase products or services resources, and attract them to move forward with a purchase by sending notifications and reminders, social media advertisements, and personalized emails with promotional offers.

AI-based Anomaly Detection

Anomaly detection deals with the capability of detecting abnormal and dubious behavior or patterns within the gathered data pool. This service can be used explicitly in extensive datasets that would be more complex to handle and unlabelled data that are more difficult to analyze for mainstream analytical solutions.

Historically, businesses used to detect problems, errors, and damages after their occurrence. They would indeed have some preventive measures, but those supplements are based on predetermined situations, making them outdated for today’s rapidly evolving, increasingly complex business environment.

AI provides a solution with predictive analytics and intelligent Machine Learning algorithms, uplifting businesses while filtering anomalies and gaps in various business processes. Supervised anomaly detection is the methodology that processes data to separate normal” from “abnormal” based on the categorical variables and labels they were supplied with. The unsupervised approach involves its methods to detect data that appears to be somehow distinct from the rest of the mass. These approaches and methodologies can be used to detect fraud, network intrusions, and any other segment where anomalies can damage efficiency and put stress on cost.

The examples above are a glimpse of what AI can do. Other real-life examples include order predictions, speech and image recognition, and much more.

Conclusion

We have discussed what AI and Machine Learning are capable of doing and how they can assist businesses to enjoy the various benefits and independence, and they will continue to expand up to new horizons.

AI-driven solutions possess some unique features, making them more complex to deploy. Below are few golden rules to keep in mind while going through this extensive exercise.

Firstly, the development depends on the quality and quantity of data available at the moment. If data is corrupted and lacks organization, the project will surely fail, no matter how intelligent your AI model is.

Secondly, the best AI experts are very hard to find and demand high salaries in return for their services. It makes the in-house development process very expensive and complex. Outsourcing is the key here.

Finally, the development of AI-based solutions requires extensive knowledge and expertise to align with the problem you’re trying to curb accurately. If this alignment fails, it may either turn out to be a chaotic scenario or a complete loss of resources, time, and money.

The development of an AI-based solution capable of handling several business processes proceeds in almost similar phases as other software development life cycles but needed to be dealt with extra care. The whole idea is to perform information and resource gathering, followed by research and validation, data cleansing, then move to the development phase and testing with necessary data security protocols and preventive measures that lead to the final product creation, finally release the product and make regular reforms to add more value.

Let us know about your experience or queries in the comments section below about building and using an AI model for business.

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