Why are CEO’s Cautious to Implement AI into Their Organizations?
Even though AI has been around for more than 60 years with Arthur Samuel coining the phrase Machine Learning in 1952 – top executives all around the globe are still confused about how this technology can successfully help improve their business landscape, impact operations or automate processes.
Today, it’s one of the most talked about topics that has everyone intrigued. However, executives need to understand this technology is not a magical solution, but a technology tool to help complicated tasks that can reveal actionable insights, improve decision making and inform businesses with new strategies. There is a huge debate surfacing around AI as people feel it’s the greatest threat to jobs and people. When ML and AI are done correctly it can be incredibly impactful and deliver real business results.
Debunking the Myths
Myth #1: ML is 100% objective, incorporates fairness and bias-free language
Today, researchers are focused on building fairness and equality within algorithmic decision-making systems. Data scientists are working on preventing bias within the dataset collection and the black box device, system or object to assess algorithmic fairness by incorporating the inputs/outputs with moral principles, moral values, and moral code, so in essence, adding ethics into the equation.
Predictions from an algorithm are used to optimize results and drive decisions. So, how can we prevent bias within machine learning? Model developers are working on how they can prevent or remove bias within the data tool and in the model building process by testing prediction accuracy on whether a model is individually fair by using good quality research and assistive tooling. Adding bias-aware algorithms and bias-free language instead of blindness approaches has been useful in removing preconceived notions or prejudices.
Projects already created may have a pre-existing bias from the person that created the dataset. This could result in unknowingly and negatively causing a pre-determined result to happen based on the information available. Whether negative or positive outcomes come true about a certain subject or event, engineers are now using aware algorithms that reshape the outcome for algorithmic fairness.
Some big examples of biases plaguing algorithms that are outputting incorrect data are from high-profile companies such as Google, Facebook, Amazon, and Microsoft.
- We can all learn from the mistakes of these big tech leaders and implement programs that protect and safeguard AI by studying – Google Translate from Google, Memories from Facebook, Twitter Chat Bot named Tray from Microsoft and the Resume Screener by Amazon.
- These AI failures caused risks to these businesses by reinforcing biases and stereotyping people. This resulted in reputations that were tarnished.
Myth #2: Data Cleaning and Preparation is too expensive, too complicated and time-consuming
Data cleaning is the first step in your machine learning workflow. It’s an essential part of the process and will be easier to train if you clean your data before building ML models. By filtering and modifying your data and by detecting missing values and understanding the features you don’t need – it will help to build ML model faster and help process data quickly. Utilizing a standardized format will make data exploration and modeling a lot easier.
The typical cost for data cleaning a database of 10,000 records ranges from $5,000 to $15,000, depending on what your company wants to include. On the surface, data cleaning seems to be a big investment and a big job for a data scientist; however, after doing the numbers, this is not the case.
Cost of Duplicate Records: The average cost is $1,000 to $3,000 depending on your data service provider. There is evidence that 5-10% of businesses have around 500 to 1,000 duplication of records in their database system. By removing or de-duping a database, the company can roughly save around $2,750 or more in by removing errors, duplication of records and fixing repetition data.
Cost of Missing Data: Depending on who you hire to fix data records or append a database that is missing one field can range from $0.50 to $5.00 per record. For example, if 30% of your database is missing one field or more – business can spend $1,500 to $15,000 in data appending costs to fix. The results can be better segmentation, email personalization and better prospect qualifying, reduced operational expenses and improved B2B sales funnel.
Cost of Validating Data I Cleaning Wrong Data: To authenticate and correct data, businesses can spend $0.05 to $1.00 depending on the data provider that is verifying each record. For a database of 10,000 records, it can cost from $500 to $10,000. However the alternative is costly and an example is undelivered emails, loss of sale $10,000 in emails lost or not delivered. Data needs to be accessible, sizable, usable, understandable and maintainable – in order for the ML team to download files quickly.
Myth #3: Hiring a high caliber AL Data Scientist for my ML/AL Development Projects is daunting
Hiring a data scientist that supports all AI+ Machine Learning projects is not as difficult as it may seem. You need to know if they will conduct training sessions and how often, and be able to articulate the business requirements and project status to IT, stakeholders and senior management.
What you should know before hiring a Lead Data Scientist
- They need to have extensive experience with machine learning algorithms and analytical capacity to prepare reports, presentations, dashboards and data visualizations to various audiences.
- Experience with full software development life cycle, project documentation, and Agile methodologies.
- Bachelor degree in science, technology, engineering, or mathematics
- Should have 4-5 years experience with applying Artificial Intelligence/Machine Learning Technologies with a proven track record.
- Knowledge of quantitative discipline and ML/AL projects, mathematics, statistics, computer science and be able to carry out highly technical analytics projects, data analytics or data science work.
Myth # 4: Takes a lot of hard work and unattainable for my small business (and/or department)
Even though artificial intelligence technology is used in every aspect of our lives, it’s still in the very early stages of development. Machine Learning helps detect patterns in data and can drive business value by extracting and translating information quickly, but executives need to take it slow and do their homework.
Start small and build out your business needs, project goals and work with companies that have off the shelf or ready to go AI products. There are out-of-box products designed to be super easy, user-friendly and your team can start quickly, right out of the box. There are tech firms who are experienced in creating tools for small business that incorporated easy-to-use technology that is more affordable and attainable.
Incorporate data visualization software that is designed primarily for the typical user. These products can create charts, graphs, and benchmark visualization. Helpful when exploring and understanding datasets and can help identify patterns, corrupt data, outliers and so much more.
Myth #5: AI is too expensive, too complex and difficult to understand
Self-service business intelligence BI software can build simple reporting, elaborate analytics that can be used for marketing, sales, supply chain, and finance. Designed and configured for users without the help of IT for deployment and users with zero coding knowledge. File uploads, database querying and report building with internal analytics applications. Data modeling, blending and discovery processes can be performed and offer embedded functionality to place dashboards inside applications with analytics.
- Tech companies use applications like Mail Chimp, Salesforce and then build efficiency – and create robust advertising. Even add data security to protect companies from fraud and by including fraud detecting and security applications so your computer systems are safe from malicious activity. Businesses that use online payments by accepting credit or debit cards can leverage AI-security applications by detecting usual behavior.
- Small businesses can also use services and incorporate AI technology similar to Facebook and post advertising and look-alike personalized campaigns based on the data it receives on users and provide a better user experience with advances in AI and Machine Learning.
- Facebook uses an algorithm to create relevant and engaging advertising that results in online enjoyment and personalization. They encourage users to click on tailored-made advertising that is customized for that user by using artificial intelligence and machine learning.
Get familiar with AL and take the time to learn as much as you can and use a tech company with a wide array of resources that have top self-service business intelligence BI tools.
There are free and paid online resources that you can take advantage of from remote workshops, courses offered by organizations to help you increase your AI and ML knowledge and simple ways to get you started with predictive analytics and the basics.
Identify the key problems you need AI to solve and begin exploring how you can add AI to your products, system, and services. Come up with case scenarios and actual examples of past problems that cost the company huge money and what you need AI to solve.
Integrate AI into daily routines, operations and ask users to be transparent about everyday issues and workflows and help employees to visualize AI in their role and all the ways it can improve their day to day tasks. Make sure everyone understands the kinds of data that will be involved in the pilot project, build more bandwidth for storage, cleaning the database and remember to balance your overall budget.
Create a financial overview of various possibilities you have identified and break the project down based on immediate and long term AI implementations. Go slow and review your internal database and your internal team to see if it needs to be cleaned, improved or if more experts need to be brought in.
Start small and bring in outside AI consultants to help set up a pilot project and think about long term elaborate projects. Begin by cleaning your data to make it ready for the AI taskforce and use AI incrementally and then expand accordingly.
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