“The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around 126 billion U.S. dollars by 2025. The overall AI market includes a wide array of applications such as natural language processing, robotic process automation, and machine learning.” – statista.com
The world has accepted Artificial Intelligence (AI) as the modern-day messiah for increasing business revenue and enhancing productivity. Organizations feel it is like a magic wand that generates wonderful results, as never before. It is greatly hyped and hence though AI is versatile and useful, not all organizations may be needing it.
By interrupting every industry worldwide with the ability to self-learn, self-analyze to make human-like choices, AI is the most talked-about and newest development in the tech business.
In 2020, the global total corporate investment in artificial intelligence (AI) reached almost 68 billion U.S. dollars, a significant increase from the previous year. There are vendors and companies looking for deploying AI at all places, irrespective of a detailed analysis if it will turn beneficial or not.
Applying AI on any project without detailed analysis may cost organizations big time in terms of money, efforts, revenue, project timelines, and output. Business owners must think about different criteria that include business value, the willingness of teams, accessibility to training material, skilled resources, budgeted cost, etc. Trying to access the benefits of AI like enhanced decision making, productivity boost, revenue growth, automation of routine activities, lessening human errors is good, but analyzing in detail at first is essential.
Simply implementing AI in organizations without detailed study is not going to turn beneficial. Before we plunge into the different tips that can help in deciding if AI is the right thing to do or not, let us quickly go through the major attractions of AI.
Artificial Intelligence – An Overview
AI is the competence of a computer/robot to perform tasks done by humans, managed through a computer. These tasks need human intelligence and hence AI mimics the main concepts of the same. It offers customized suggestions to all, depending upon historical information and other online parameters.
It is a wide range of computer science techniques that are related to the creation of smart machines that can perform human intelligence activities, with ease and effectiveness. It is widely used in eCommerce activities, logistics, inventory management and planning, self-driving cars, chatbots, facial recognition, translation, smart assistants like Siri, Alexa, etc.
- Near real-time support to customers
- Significant perceptions from cloud-based data
- Exhaustive business analytics with superior decision making
- Increased business automation
- Enhanced user experience with AI chatbots
- Improvement in sales, client satisfaction
- Market automation
Organizations Using AI
Walmart, Apple, Berkshire Hathaway, United Health Group, Amazon, Exxon Mobil, McKesson, CVS Health, AT&T, Chevron, Ford, General Motors, Alphabet (Google), Costco, Walgreens, JP Morgan Chase, etc.
Key Tips to Ascertain If AI is the Right Solution or Not
If you are confused about whether to choose AI for your organization or not, it is better to take a wise call by analyzing each of the below pointers and see how accurate your decision is:
- Check User Requirements Before Taking the Call
Before plunging into the AI fervor, it is wise to check if you and your users really need the AI-based implementation. Identifying, analyzing, and then implementing user requirements is paramount. Based on that, an AI model can be worked upon and analyzed by feeding available data. If the workflow is easily manageable by humans, where is the need for AI? Why unnecessarily disturb the entire flow?
Once sufficient data is collected, it must be seen if you need AI for solving the existing issues rather than creating new hassles. The final aim must be to create better products, take better business decisions, automate processes, and make the most of data. If these are achievable, AI is worth implementing else better to stop and think over again.
- Check If Third-Party Integration Will Work Well
Most programs will need third-party integration support before implementing AI-based solutions. It must be analyzed in advance if it is possible to integrate these systems, along with the AI-related solutions or not. If not, it could lead to a problem later as the data flow will never be complete. Analyzing this will help choose relevant AI software that can easily integrate other systems.
AI may need third-party plug-in support for fulfilling seamless connectivity and hence a detailed walkthrough is suggested prior to implementing the AI solutions. The entire process of integration must be worked upon in advance so that there is no trouble to users while implementing AI techniques. Not doing this may lead to a hassled implementation and unhappy users.
- Check the Goodness of Data and Have an Efficient Data Collection Process
Data and that too good data forms the crux of any AI implementation. In case of bad or unwanted data, it is tough to have a smooth AI solution running. AI and ML algorithms rely on data for any kind of data processing. If data is not enough, contains unnecessary patterns and out-of-range values, has old values, is insecure and not trustworthy – implementation of AI and ML is not likely to succeed.
Checking of data and its accuracy levels is very important to be done prior to having AI in your organization. And, if that is not possible, it better be avoided since having it with misappropriate data is surely not the right thing to happen for any organization’s efficacy, revenue maximization, and profit generation. If you can format data in a consistent manner, keep data accurate and streamlined and fill up missing links, you can go in for AI else surely not.
- Maintain Skilled Expertise in Appropriate Tools and Technologies
There are so many tools and technologies that prevail in the market that offer AI and ML implementation experience. Choosing the right tool and technology while going in for AI is highly crucial and important for a successful project. Not doing so may lead to technical errors and hence a wrong output. Whatever tool or technology you choose, there must be skilled expertise in that arena, either within your organization or you must have the capability to hire experts.
Also, the tool that you choose must be easily integrable with the rest of your setup. If that is not possible, it is better to avoid AI. Go in for AI only if you have proficient resources, a well-integrated setup, data management capabilities, easy integration with third-party platforms with a flexible curve.
- Ensure Maximum Return on Investment
The end game is to get maximum revenue to be it with AI or without AI. That is what business owners must ensure while analyzing for AI implementation. After considering all parameters, if the implementation of AI is not going to render a positive RoI, what use would it be of? A wise business decision must be taken by all involved members if taking such a big step would ensure a revenue increase and if not, why would you take this step at all? If there is no positive output being seen, what advantage would AI or ML get?
Business stakeholders must analyze if having AI or ML-related techniques will generate revenue, has this AI technology has been a successful case study for any other organization, is there accurate and correct data enough to solve the issues pertaining to the organization. Once this analysis is done, it must be decided if having AI in the organization is bringing in relief in terms of revenue, work, profit, learning curve, etc., or getting in more of burden, user dissatisfaction, lesser revenue generation, slower process implementation, etc.
On a Parting Note
“Predicting the future isn’t magic. It’s Artificial Intelligence.” – Dave Waters
So rightly said! AI has charmed businesses across domains, geographies, and sizes. No wonder, organizations are getting lured with implementing AI in their industry segments irrespective of understanding whether it is needed or not. The above guidelines are specific to make users understand that not all organizations need AI.
AI algorithms feed on good and organized data. If you are unable to provide that, you may not be able to get the best output and rather come across unwanted hassles. In a similar fashion, there are many other parameters that must be fulfilled for AI to get implemented. Only then can you get the desired output from AI.
Only after a detailed analysis of the above pointers can stakeholders finalize if artificial intelligence is a must in their work culture or not. Till that time, let AI rule the plethora of business segments the world over!