The Ultimate Guide to Applying AI in Business
Understanding AI, determining your objectives, collecting the right data and engaging the right resources can help you make AI a reality in your business.
AI has already become a reality for many industries, including, but not limited to:
- Health care.
- Oil and Gas.
- Publishing and Media.
- Customer Service.
In other words, to say that artificial intelligence (AI) is the next step in enterprise would be an understatement. Moreover, it’s growing in strength and popularity. But while it is well known that AI is the next step forward, myths and misconceptions about AI and its processes still run rampant. (Also read: Artificial Intelligence: Debunking the Top 10 AI Myths.)
So, here are some practical steps you can take to make AI a reality in your business:
1. Ensure Your Team Understands AI
In order for AI and ML to be used to their maximum potential to help streamline enterprise, reduce costs, reduce risk and increase profits, it needs to be implemented with precision by those with realistic expectations. In 2019, Techopedia ran a two-part survey and quiz to help us examine how well industry executives comprehend AI and machine learning (ML).
The results of our survey supported one clear answer: Business and industry executives do not understand the majority of AI and ML.
However, the drive to incorporate AI and ML into enterprise is there. Of our survey respondents, 44% had ongoing AI or ML projects, 14% were working towards implementing it and 17% were not yet working on it but want to.
Our survey and quiz showed that myths about AI and ML are prevalent among industry executives. Common misconceptions included:
- Twenty-three percent of respondents said they thought AI-programmed computers can exercise free will. They cannot.
- AI will increase unemployment in the long run. While 55% of respondents claimed this belief, it isn't the case. (Also read: Will Robots Take Your Job? It Depends.)
- The belief is that AI will increase the danger of creating sentient machines that threaten humans. Forty-seven percent of respondents claimed this.
So, first thing first, C-Suites and executives need to make sure they are investing in AI for the right reason: because they have a specific problem they want to solve.
Investing in AI out of fear of being left behind is bound to lead to disappointment and further misunderstanding of what AI is truly capable of.
2. Determine Where And How You Want To Adopt AI In Your Business
The first questions you should ask yourself and your company should be: “What’s our problem? What do we want to predict?" This needs to be specific. Simply wanting to grow your business is not the right strategy to implementing AI.
A word of advice here: Don’t go for the moonshot. Instead, look for simple, repetitive tasks that AI and ML can help accomplish. Examine your business’s workflow and start small. (Also read: Robotic Process Automation: What You Need to Know.)
Other questions that can help you determine the business problem you want AI to solve include:
- What measurables will be most affected by your AI/ML project?
- How will you track them?
- When, specifically, do you expect to start seeing results?
- What is the target return on investment (ROI) for your project?
It is also important to understand that implementing an AI project is not simply flipping a switch to create results. AI projects need to be carefully planned and will take time to show results.
3. Collect the Right Data
It is a common misconception that you can start (and finish) an AI project simply by throwing a mass amount of data at it. This is far from the case.
In fact, a smaller, more specific amount of data that is highly relevant to the question being asked is better than a large amount of data where only some are relevant to the question being asked.
The myth that more data is always better goes hand and hand with the myth that AI and ML can solve any problem. Many industry representatives misjudge what it means to implement an AI project. Again, they don’t need to be giant moonshots! Instigating an AI project doesn’t mean replacing human workers with robots or building drones to do extra work.
The best use cases for AI and ML projects will reduce costs, reduce risk and/or improve profits. More often than not, the best results are seen from implementing AI to handle the small, repetitive tasks that businesses do on a daily basis.
Of course, finding these projects is not always the easiest task for C-suite and upper-level executives. Narrowing down an AI or ML project requires having an in-depth knowledge of a company’s workflow.
Now that you have a problem at hand, look at your data. Do you have the data needed to answer this problem? Chances are, examining your data will result in you performing a deep clean of your data and narrowing it down to the data that is truly relevant to the task at hand.
This may mean bringing in an educated data science team that can help you analyze your data and determine how useful it is for the project you want to start. (Also read: Job Role: Data Scientist.)
4. Employ the Right Resources
While AI skills and capabilities are becoming a more common, sought-after skill, it is important to not underestimate the work and time that goes into an AI or ML project. Determine whether you have the in-house talent to start your own AI project, and if you don't, find the right external AI/ML partner.
With the boom in AI and ML, there are many companies out there that specialize in helping your company find its AI potential. But not all AI companies are created equal. Find the right partner with the experience, capabilities and resources to help bring your project to light.
Also, make sure you have the right instruments to address your various needs as they keep arising.
Here is a quick list of the best AI tools for business that you might want to employ:
The Best AI Tools For Business
This useful AI tool analyzes your text with predictive technology to improve your job listings, making it sound more appealing to candidates and removing any subconscious bias from the description. Because you need to hire AI experts at some point!
This AI tool will help you with data management. SAS's platform facilitates ultimate control over your progress by assisting you with risk assessment, customer intelligence, business forecasting and even identity verification. (Also read: Predictive Maintenance: Ensuring Business Continuity with AI.)
This data integration tool employs machine learning to analyze, curate and consolidate your data quickly and efficiently.
Tamr is an asset for making sense of large amounts of disparate and fragmented data, connecting the dots and transforming data into clean and useable format.
Fireflies uses natural language processing (NLP) to record, transcribe and search across your voice conversations easily and intuitively. You just need to "invite" Fireflies to any of your meetings, and this tool will do everything on its own.
An AI-powered time management platform, TimeHero helps teams sort out their schedules, calendars and to-do lists. It will sync individual calendars to team ones, automatically remind employees about their tasks and improve the efficiency of your collaboration efforts. (Also read: How to Ensure Peak Remote Work Productivity.)
This worker tools platform uses AI to extract actionable insights about human activity data. It is an asset for cutting your costs and identifying the areas where your business can improve its productivity, safety and workforce quality.
as the name suggests, Legal Robots is a pretty useful tool that help your business decipher the often complex legalese found in contracts. It can also be used to make your newly created contracts more readable so everyone is on the same page before signing them.
Sage AP Automation
This tool fully automates the accounts payable process, simplifying time-consuming operations such as recording expenses, performing banking reconciliation and managing invoices. This can be especially useful for businesses who work with many external contractors or whose workforce is mostly comprised of freelancers.
Artificial intelligence has become an indispensable tool for customer service. Through AI-powered chatbots, often integrated into company websites, customers can troubleshoot, place orders and get in touch with human customer service representatives.
There are myriad chatbot platforms on the market, including Chatfuel, MobileMonkey and Pandorabots.
Targeted Marketing Platforms
In today's day and age, customers are being marketed to from all angles. That means it's even more difficult for your product or service to stand out -- but AI can help.
Artificial intelligence is integrated into many of today's foremost targeted marketing tools, leveraging consumer browsing data to provide deeper insights into marketing initiatives. In other words, AI can show companies which products to market to which consumers based on the consumers' internet history. (Also read: How Recommender Systems Are Changing E-Commerce.)
There are many AI-powered targeted marketing platforms available today, spanning capabilities from SEO optimization to copywriting. Popular examples include SEMrush and MarketMuse.
Artificial Intelligence in Business by the Numbers
According to McKinsey and Company, 56% of companies have already adopted at least one kind of AI technology as of 2021. The expansion of AI also stands to have a significant impact on the world’s economy and job force.
The hype around AI has also led to a rapid increase in companies investing in AI and big data out of fear of being left behind. According to MIT Sloan Management Review, 92% of Fortune 1000 companies feel an urgency to invest in big data and AI, with 21% of these companies spending more than $50 million on these investments.
McKinsey and Company also predict that AI technologies could lead to a performance gap between companies that fully absorb AI tools across their enterprises over the next five years compared to those that do not by 2030.
But it’s not just about a company’s fear of being left in the dust; 84% of global business organizations believe that AI will give them a competitive advantage, according to MITSloan Management Review.
And of course, revenue and economic gains are major factors in why companies are realizing AI’s worth. PwC believes AI could contribute up to $15.7 trillion to the global economy in 2030, while Tractica predicts the AI software market to reach $100 billion in annual worldwide revenue by 2025.
Finally, it’s easy to understand where AI is sending the tech job landscape. Element AI revealed in its 2020 Global Talent Report that the number of people claiming to have the education and skills profiles to qualify as an AI expert rose to 477,000 professionals from a mere 22,000 between 2017 and 2018.
The long and short? The so-called "AI revolution" is already here. If you aren't on board yet, it's time to start.
When it comes to AI adoption, you should be patient, yet agile. The latter is a big one to remember: AI projects are not sprints, and you have to crawl before you can run. Sometimes moving forward will require rethinking and re-examining of your initial problem.
AI will impact many aspects of your business and the models will require learning time and practice in order to produce results. (Also read: 6 No-Code AI Platforms That Are Accessible to SMBs.)