Have you ever wondered about the hype surrounding AI technology?
Many people have the misconception that AI is like the evil computer in Hollywood movies that tries to take over the world and subjugate humankind.
However, the reality is far from this. Actually, AI is already integrated into many aspects of our daily lives. For example, it is used in Google search to predict what you’re looking for, in online shopping to suggest products, and in albums and apps to recognize faces. AI integration into our lives makes things easier and provides us with more free time to pursue our goals.
Companies are racing to take advantage of this technology, but they need to be aware of how it’s being used and to what extent it can be integrated into their systems in order to maximize the ROI of adopting AI and stay competitive in the market.
How to know when AI is the right solution
Many large companies have already adopted artificial intelligence, especially those that aren’t at risk of falling behind. A recent survey by McKinsey found that 55% of companies are using AI in at least one area, and 27% of earnings before interest and taxes are attributed to AI.
But what about small businesses? which may lack the financial and technical resources to adopt it?
Well, on the one hand, not adopting AI could mean the difference between a thriving business and one that struggles to grow.
AI can automate repetitive tasks, freeing up time and resources for small businesses, which can be especially valuable in those cases where time is a precious commodity.
Additionally, early adopters will have a competitive advantage, particularly in areas such as marketing and lead generation, and will also be able to spend less time on customer support and fine-tuning campaigns, which will prompt various industries, leading vendors, and businesses to look for ways to implement it.
On the other hand, not all businesses can benefit from AI, and using it inappropriately can lead to wasted resources and create confusion among employees, customers, and leads.
So businesses need to be aware of the challenges that may arise with AI and make sure they are using it wisely and efficiently before embarking on an AI project, and it is important to consider whether it meets the criteria of having business value, having access to sufficient trained data, and having a culture that is open to change. Evaluating these factors can help ensure that your AI project will not be a wasted investment.
Before determining if your organization is prepared to adopt AI, various questions need to be considered.
Do we have enough data?
To be able to develop and automate your business with AI, it is crucial to have enough data. The amount of data required will vary depending on the complexity of the problem and the learning algorithm. Utilizing good-quality, unique, and original data is the key factor for AI algorithms to perform well. Therefore, organizations can consider AI implementation if they have a substantial amount of data at their disposal.
Do we have quality data?
One of the major challenges in AI is ensuring the use of high-quality data.
Data obtained from the internet is not tailored to a specific business and its operations.
To guarantee accurate outcomes from the AI algorithm, it is essential to have specific data for the business and its processes.
This can be achieved by regularly updating, adding comments to any modifications, discarding outdated or irrelevant data, creating backups, filling in any missing data, monitoring any changes made, and using standardized data formats. It is vital to identify and address any deficiencies in the system before implementing AI.
Which processes are we considering for automation?
When considering automation in business, identifying processes that are best suited for AI automation is crucial.
Automating the processes that typically require many employees, are repetitive, and take a lot of time to complete manually can save both time and money for any type of business.
To identify the processes and routines that can be automated in an organization, there are quite a number of factors to consider beforehand, such as data requirements, availability of data, common requests, time-consuming tasks, costly manual processes, and the ability to shift employees to other high-priority tasks.
In what areas do we require assistance with decision-making?
AI is a powerful tool for analytics and decision-making, and many leading organizations are using it to gain valuable insights and make better decisions. In the field of marketing, for example, AI can help gather real-time data on customer behavior, create forecasting, and predict trends to make more informed decisions on product placement, marketing strategy, and more.
However, before adopting AI, it’s important to understand the decision-making capabilities of AI and identify the areas in which it can be most beneficial.
Is our workforce equipped with the necessary skills and qualifications?
Before adopting AI, it’s important to ensure that you have skilled, trained, and experienced employees to manage the technology. Without the right employees, AI adoption may not be successful. To overcome this, you can provide online classes to existing employees, create a plan to hire professionals, and invest in training for long-term success. Remember, AI depends on human input and data to function properly, and it’s important to have a team in place that can handle AI and automation tasks.
Is investing in AI worthwhile?
To determine if your organization is ready for AI, perform a cost-benefit analysis. Once you have identified how you want to use AI, consider the following points: Create a checklist of your goals, research industry data, understand the cost of the specific AI technology, consider secondary factors such as licensing, salaries, and risk, and calculate the cost of the current manual process. This will give a clear vision of whether AI will be worth the investment or not. AI can save money in the long run when implemented successfully.
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Begin with the most basic solution available
Zack Fragoso, data science and AI manager at Domino’s Pizza, suggests that data scientists often have a tendency to lean towards an AI-first approach, but AI is not always the best solution. The company has been adopting change during the pandemic, with customers now having 13 digital ways to order pizzas. Domino’s generated over 70% of sales through digital ordering channels in 2020, which has created an opportunity for AI. However, the key to applying AI at Domino’s has been to start with the simplest solution possible. This way, the solution runs faster and performs better, and it is also easier to explain to business partners. The approach is to first look at the simplest, most traditional solution to a business problem, then determine if there is a value-add in the performance of the model if AI is applied.
Use historical data as a basis for AI predictions
Using past data as a basis for predicting future outcomes is a key aspect of AI. However, it’s important to note that AI’s predictions are limited by the quality and relevance of the historical data used. Additionally, external factors that cannot be predicted or controlled can greatly impact the accuracy of AI predictions. It’s also important to consider if the implementation of AI will change the behavior of the system being analyzed. Therefore, it’s crucial to carefully assess the specific problem and potential limitations before committing significant resources to an AI solution.
The COVID-19 pandemic has demonstrated how unforeseen events can greatly impact a company’s revenue, as seen in McKinsey’s state of AI survey, where there was a decline in revenues in various areas.
AI can be useful in situations where history is likely to repeat itself, but its usefulness diminishes when there are unpredictable factors or changes in behavior as a result of its implementation. One example is a consumer goods conglomerate that tried to use AI to forecast financial metrics, but the predictions were inaccurate due to biased data and assumptions. In this case, a simpler solution, such as a financial dashboard, provides the necessary insights without the need for extensive AI implementation.
Obtaining data for your AI projects: The challenge ahead
One of the main challenges of AI projects is having enough high-quality data that is properly labeled and without biases. Collecting this data can be time-consuming and expensive, and companies need to consider whether the data can be reused for other projects. Additionally, businesses need to be clear about what decisions they want to make with their data and ensure that the data collected is representative and captures the questions they want to answer. In some cases, a rules-based system or traditional formulas may be a more efficient solution than using AI. It is important to consider if the improved performance provided by AI is necessary for the project and if it will provide a good return on investment.
AI Recommendations could be worth $300 Million in losses.
The real estate company Zillow has learned the hard way that AI predictions could come with a high cost, having to write down $304 million worth of homes it purchased based on the recommendation of its AI-powered Zillow Offers service. The company may also have to write down another $240 to $265 million next quarter, in addition to laying off a quarter of its workforce.
The CEO of Zillow, Rich Barton, explained that they have been unable to accurately forecast future home prices due to the impact of the pandemic and the supply-demand imbalance that led to a rise in home prices at an unprecedented rate. This serves as a reminder that AI predictions can be affected by unforeseen events and that it’s important to understand the limitations of AI.
In summary, AI can bring many benefits to your business by increasing productivity, reducing workloads, and driving growth. However, it’s important to keep in mind that it’s not a universal solution for everything and should not be considered a magic solution for increasing profits; it’s based on math, and it does require a large amount of data to function properly. If a company does not produce a lot of data, it’s unlikely that AI will bring significant benefits to the business. To adopt AI successfully, a company must have structured data, well-defined business problems, and a flexible strategy in place.