February 1, 2022 • 7 min read
What brains to add to your software robot - early 2022 edition.
We want to ease your way to expand the intelligent automation use by listing top cases you can do with Aito. We made remarkable progress in 2021 with our customers and partners on automation platforms like UiPath, Automation Anywhere, Robocorp, Blue Prism, Integromat and Parabola.
Note that we just launched Aito app in Airtable Marketplace. You will hear about us listing top Airtable specific use cases shortly! But why not give it a try with the use cases below to get some Aito intelligence for your Airtable spreadsheets.
I group the use cases in three process areas gaining traction: customer operations, finance operations, and master data improvements. From our experience, the process owners of these areas are the most active in searching and executing intelligent automations.
Ping! Customer service and help desk teams.
Why should they care? Faster and more accurate responses to tickets. Happier and more loyal customers. The automation removes repetitive and dull tasks from the customer service team.
Take the incoming new ticket and assign it with the right category, urgency and other critical information for efficient processing by the right agent. A common problem for many teams dealing with tickets or requests is how to add metadata such as categories and urgencies to the tickets so that responses can be better orchestrated? While your automation or customer service platform takes care of the orchestration, Aito can be the real-time "tagger" that adds labels, categories and urgencies to your tickets based on how your human workers have done in the past.
Note: IT support and internal employee services can also benefit from this use case.
Ping! Customer service and help desk teams.
Why should they care? Your frontline support staff quickly finds the best solutions from the past on their support software, and your company can support the fast-growing user base without linear growth in support staff.
This is the slightly more sophisticated sister case of the previous one. The customer service teams often face a challenge with questions requiring more profound expertise, such as deeply technical questions. These take time from other teams like senior engineering staff. Connect the historical service ticket data to Aito (from Freshdesk, ServiceNow, Zendesk or whatever your weapon of choice happens to be). This allows your senior support staff to control the training material. And as well gives you the ability to integrate the best-suggested responses for the new incoming tickets.
Note: Oh boy, how many different solutions there are where questions are asked and answer given. Aito is best with this kind of data.
Ping! Sales teams.
Why should they care? Offer relevant products and services to customers at the right time and get fast new sales from existing customers.
What should you offer to existing customers? This is a logical continuation of the two first customer service related cases. You get valuable interaction data from the customer dialogue that the sales team can use. But even without that new information, you undoubtedly already have CRM and customer interaction data. Use Aito in helping the sales rep with recommendations about the right actions. On what to offer to the customer based on her profile and past behaviour.
Note: On the use case, you create elaborate rules of what to sell next. Why not take a predictive approach! Let Aito predict the most likely customers who do not have a particular product or service but have a profile that statistically indicates they should have it. Test sell and feed the results back to Aito for increased accuracy.
Note #2: Another sales team related case uses Aito to get the right (experience, skillset, responsibility) sales representative to serve the customer.
Ping! Accounting teams in any larger enterprise.
Why should they care? Faster invoice payments, less repetitive work, fewer errors. Automation can reduce processing times up to 60-80%!
Rule-based automation is a nightmare to maintain. To successfully automate invoices, you'll need to be posting invoices to the accounting system with the correct details. Use Aito to predict the correct GL account for a new invoice line item, maybe added with cost centre and VAT category (that too often goes missing with OCR). Why not even automate to channel the invoice to the right approver.
Note, think of this kind of use case as just a few tasks you can automate in the vast ocean of task automations you can do in entire processes, e.g. purchase to pay, with the SAME base of valuable accounting data you have. Think of multiple use cases, and we guarantee the bottom line appreciates that efficiency in the long term.
Ping! Finance and account receivables teams in any enterprise with a significant volume of sent invoices.
Why should they care? Better prediction accuracy to cash flow planning. Mitigate and act early on likely delayed payments.
This works as a great example of what I meant by saying you should be thinking of a long term and multi-use case approach when selecting the technology you are using. Here we go with the same invoice data we already have in Aito, familiar to you. Create automation to predict the "buckets" when each sales invoice will likely be paid: early, on time, a bit late, a lot late. This allows the accounts receivables team to act promptly.
Ping! Travel and expense management teams.
Why should they care? Because every human being on this planet hates expense claims.
Your workflow can scan the receipts, extract entities, or get details from credit card records. After getting the details for approval from purchase receipts, orders, invoices or employee requests, use them to categorise and label the claims for processing and storing. Categories, cost centres and labels ("internal", "billable", etc.) are needed for claims to be processed and stored, and that is where Aito can help by predicting them based on past entries.
You could even level up the case. Automate the approval of the cases by matching the incoming case against your approval ranges. Or what has been approved previously.
Note: Use the same analogy of categorisation and labelling claims and matching and approving them, based on the history records for different claims such as other employee or customer requests.
Ping! Purchase to Pay process owners
Why should they care? Just ask the CFO. Direct impact on the bottom line of the business to have correct billing.
Here, Aito matches sales invoice data to historical invoice, order, or customer data. Use the customer (such as discounts), order and invoicing data to identify incorrect invoicing. Create an automation that checks the invoices before they are sent out. The automation finds the wrong ranges of product and service prices or invoice record anomalies. It can alert the right person to correct the invoicing or orders and even give recommendations on the right invoicing.
The last of the three process areas is the most generic one. Every company has their unique data and business they are focusing on. Therefore I will keep the title to describe more the technical essence of the use case, giving then some examples of the tangible value-adding use cases. In the context of your organisation, we are happy to help identify the proper use cases with you. Feel free to contact me!
Categorising is quite apparent, but let me use two examples where there certainly is value and business case worth categorising. As a startup investor, you could predict the action category for the new startup you invested in based on the historical actions. Or, as a real estate investor, you could predict what maintenance actions are needed for the new apartment you bought.
The examples are more action-oriented, but you could absorb value from categorising data to be more easily processed, like for the marketing department to categorise tweets from customers. In the Aito console tutorial, you can do this kind of airline tweet categorisation.
A valuable and straightforward example is to tag eCommerce inventory for better findability of products. Ensure the product data is complete and that every product is available for purchase and adequately promoted. Proper tagging of each product is critical for modern retail and eCommerce solutions. Create an automation that looks for new products and missing tags and uses Aito to predict the likely labels based on product details. Use high confidence predictions without human review, and send the rest for validation. This tireless automation learns from every review made by your staff.
Increase data accuracy, and do less manual work fixing the problems. Various data entry types from one system to another is a staple for automation, CRM being one of them. Aito can improve data quality at the entry stage by identifying potential duplicates in CRM entries. Other data to get treatment could be Product Information Management data, HR data or Supply data.
Are you feeling a bit exhausted when thinking about all the opportunities you have? You should! But take a positive spin out of it. These cases are just a start, and we tried to select the relatively simple ones that will bring immediate value. All are part of larger workflows and processes, including hundreds of micro-decisions that the right machine learning tools could automate instead of using the time and nerves of knowledge workers.
Start your journey to identify relevant and valuable cases for you, and it will be fun. I am eager to help and sit down with your company and chat with process owners to identify more opportunities and make an immediate impact on your bottom line. One of the Aito superpowers has allowed users to test new ideas fast from the start. With their REAL DATA. If you have a use case on your mind, go ahead and ramp up the free sandbox - here - and try out. Feel free also to contact our team or drop me a message to check the feasibility of your use case. Your use case might be on our next top cases list!Back to blog list