8 areas where you can leverage AI today for automation on the web.
Artificial Intelligence (AI) is a hot and highly marketed topic, but the application of AI for automation on the web is still in its infancy. This means those laying the right foundation today will be rewarded with the compounding value of less friction and more success with future automations.
Here are 8 areas where you can begin leveraging AI for automation on the web.
Many automations on the web start by getting through a login page.
AI can be leveraged to simplify the login step by looking up standard login components such as username, password, and “remember me” functionality, minimizing obstacles like MFA (Multi-Factor Authentication) and CAPTCHAs, and increasing the probability of automation success when there are login page changes.
2) Flow selection
A growing number of websites have multiple versions based on region, customer type, M&A (Mergers & Acquisitions), and/or A/B testing.
Using AI you can simplify the support of multiple website versions. This can be achieved by recording multiple automation flows, by website version, then using AI to select and navigate to the correct flow. This approach adds scale and stability to the automation by eliminating the need for a human to micro-manage variability of website versions.
3) Managing website issues
Traditionally, website instability is a key culprit for breaking automations on the web.
AI can help solve the most common instability issues like slow-loading webpages and webpage timeouts. It can do this by intelligently adjusting bot wait times by website, proceeding when critical components of a webpage are available (but noncritical components are not) and automatically reloading webpages with timeouts.
Bot automation flows are guided by data such as dates, account numbers, and SKUs. However, the way this data is presented on websites may vary from how you store it in your database, and even if you harmonize the two, eventual changes to the presentation of data on the web will break your automations.
AI can help solve this problem by recognizing data patterns when websites make changes to the way they present data. Common examples are changes in date formats (i.e. 01/17/2021 vs. 17/01/2021), account numbers (i.e. 001234567 vs. 1234567), and SKUs (i.e. TESLA-S vs. Tesla Model S).
Change on the web is constant as websites evolve to meet their users' expectations. For example, you may download your utility bill each month by clicking on a link that states “Invoice”, but next month that same link may state “Bill”. These types of seemingly obvious changes to the human eye will break traditional bot automations.
By using AI you can better recognize relevant nomenclature changes such as synonyms and increase your probability of success automating on the web.
Website changes go beyond nomenclature changes. Sometimes nomenclature stays the same, but the elements in the User Interface change. A common example is a website modernizing clickable links by transitioning them into buttons. This kind of change will break traditional bot automations.
By storing the goal/intent of your automation rather than just the HTML element location and raw browser interactions you can leverage AI to successfully navigate website changes.
Websites use pop-ups to gain the attention of their users. Unfortunately, this can break brittle, bot automations.
By using AI you can intelligently recognize pop-ups, the content within them and derive whether your automation should fail or close (or acknowledge) the pop-up and proceed with the automation.
Some Bot automations run on a preset schedule based on an understanding the automation can be executed successfully at that time. But, what if your scheduled date is wrong. A common example is you command your bot automation to download a report on a specific date, but continually the report isn’t available until 2-3 days later.
AI can help you solve this problem by learning the optimal schedule based on historical automation success and suggest the appropriate schedule adjustment.
As companies attempt to automate predictably on the web they must consider the web's dynamic nature and look to pattern recognition technologies like AI to help manage this variability. Companies like Weeldi have started laying the groundwork by designing automations that focus on user intent, rather than exclusively on static navigation steps, and then using AI to successfully navigate to the users expected outcome. This approach proves to be a sturdy alternative for automation on the web where change is constant, broad, and unpredictable.
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