Facebook, Amazon, Apple, Netflix, Google, and thousands of other leading companies manage large teams of machine learning experts, as virtually every digital product or service you use is powered by AI.
Your Google search results, your Spotify and Tinder recommendations, your Facebook and Twitter feeds, and even your phone’s facial recognition are brought to you by AI.
This means that users are now expecting intelligent products. If you’re not using AI, you can no longer compete with industry leaders. Fortunately, it’s now easier than ever to upskill organizations in AI, as you don’t need technical experts to build and deploy AI models. With the help of no-code AI, companies at every level can upskill their teams in AI.
Upskilling is a comprehensive effort to teach practical skills across an organization, including identifying valuable skills, creating training material, and executing on projects with the newly-acquired skills.
The main barrier to using AI is budget constraints, followed by a lack of technical expertise, according to a recent survey of over 1,000 executives. Further, 86% of respondents report that ethics considerations are a priority in their implementation of AI. While there are many AI tools, these concerns eliminate a large swath of them for the purpose of AI upskilling.
With this in mind, AI upskilling initiatives need AI tools with three main characteristics: Affordability, ease-of-use, and transparency.
The AI tool selected needs to be affordable, given that budget constraints are the main barrier to using AI. Traditionally, AI tools have been expensive, and this has been a major barrier to their use. Multi-million-dollar investments are typically required to get started with AI, which has put it out of reach for many organizations. However, recent advances in technology have led to the development of more affordable AI tools.
The AI tool needs to be easy-to-use, given that technical difficulties are the second greatest barrier to using AI.
Finally, the AI tool needs to be transparent and intuitive, to enable greater collaboration and explainability, helping to support the creation of ethical AI.
Bias in AI has become a hot topic in recent years, with high-profile cases such as Microsoft's Tay chatbot and Google's photo-labeling algorithm making headlines. The potential for AI to discriminate against certain groups of people is a real concern, and one that needs to be addressed.
One way to help combat bias in AI is to make the tools more transparent and easy to use. This way, people from all walks of life can have a hand in shaping how the algorithms work, and can help to ensure that they are fair and just.
More transparent AI tools will also help to build trust between people and machines. As AI becomes more prevalent in our society, it is important that we understand how it works and why it makes the decisions it does.
By making AI more transparent and understandable, we can help to create a more ethical form of artificial intelligence.
These three needs act as a preliminary filter in determining what AI tool to use in an upskilling initiative, and ultimately across an organization. Solutions that require expert users, like Google AutoML, Azure AI, and Amazon SageMaker, are filtered out. Further, highly expensive solutions—like H2O AI and C3 AI—are also filtered out.
This leaves affordable, easy-to-use no-code AI solutions.
Beyond shortlisting potential AI tools, organizations need to be crystal clear on why they want to use AI. Is it to hire the right talent? To detect fraud? To improve retention? To improve supply chains?
Answering this question will allow organizations to finalize what the right tool is for their needs. If the goal is to optimize KPIs—whether it’s churn, attrition, fraud, or any other metric—then a tabular no-code AI tool like Akkio can be used.
If the goal is to build classification models for image or video data, then a tool like Lobe AI can be used, while if the goal is to build classification models for audio data, then a tool like Teachable Machine can be used. Finally, if the goal is to automate document processing, then you might use a tool like Rossum AI.
After selecting the right AI tool for your needs, it’s crucial to ensure that it’s actually used by employees.
To do so, employees need to be incentivized to use the tool, and supported in their journey. This includes promoting a culture of learning, whether it’s investing in a company-wide Udemy subscription or reimbursing educational expenses.
Moreover, a clear roadmap can be made to show employees the available promotion opportunities available after AI upskilling. AI is a valuable skill, and companies can acknowledge that by providing opportunities for greater pay, equity, or bonuses, upon building and deploying valuable AI models.
In the productivity space, there’s a saying that “what gets scheduled gets done.” To bring AI projects to fruition and truly upskill your organization, there needs to be a schedule in place.
This can be tied in with the aforementioned incentive system. For example, employees might brainstorm AI use-cases in one week, gather historical data the next, build and deploy models thereafter, and finally integrate them into their workflows.
The more specific the timeline is, the better. For instance, you might want to build and implement three AI use-cases, such as augmented lead scoring, churn reduction, and sales funnel optimization. Each of these should have dedicated spots in the roadmap, and should be tied to specific employees to ensure completion.
Augmented lead scoring is a process of using algorithms to automatically score and prioritize leads. This can be done by analyzing various factors such as demographic information, engagement data, and previous purchasing behavior.
Churn reduction is the process of using AI to identify customers who are at risk of leaving, and then taking action to prevent them from doing so. This can be done by analyzing customer data and identifying patterns that indicate a high risk of churn.
Sales funnel optimization is the process of using AI to improve the efficiency of a sales funnel. This can be done by analyzing data to identify bottlenecks and then taking steps to improve the flow of leads through the funnel.
As the saying goes, “what gets measured, gets managed.” Perhaps even moreso, what gets rewarded, gets done. However, in order to properly reward employees for upskilling, their efforts need to be tracked.
For example, you could track employees along the four steps to AI: Finding a use-case, finding the relevant data, building an AI model, and deploying an AI model. At each stage, employees could be rewarded, and especially so at the final stage—when a valuable AI model is deployed.
Below is a simple example. Two employees, Ambrose and Jazmin, have gotten started on AI upskilling, while the other two, Negasi and DeAndre, haven’t yet.
These efforts could be manually tracked, such as in weekly meetings, or automatically with tools like Zapier. Employee recognition could even be automated with tools like Bonusly. For instance, you could automatically give employees who integrate an AI model a bonus with the below Zap, which gets triggered when the “Task 4” column above is updated for any employee.
You could even take it a step further, and reward employees based on the success of their deployed AI model(s). Suppose that an employee built a cross-sale prediction model in Akkio with a workflow that automatically sends a cross-sale offer to a user that’s likely to be interested. You could use Zapier to automatically give the employee behind that model a small bonus (or commission) whenever a user accepts that cross-sale offer.
Upskilling your organization in AI is easier than it’s ever been, but you still need a plan and deliberate execution.
As executive surveys indicate, for AI upskilling initiatives to be successful, the AI tooling you use needs to be affordable, easy-to-use, and collaborative. Finally, incentive systems should be in place for employees to actually implement these tools.