By luck, as I was trying to solve a different problem that I felt was going to happen in accounting – the commoditization of accounting services. When the COVID-19 pandemic shut down the entire country, I realized that the same strategy I used to commoditized proof my firm is also applicable in revenue maximization. Revenue management was introduced in the 1980s after the 1978 Airline Deregulation Act. It will be applicable for the new business 2.0 (post-COVID) environment.
I know that the AI journey is a marathon but now I have to sprint to get my business restarted. I need to quickly train my staff and have a strategy in place to make this change. The strategic plan that deals with (a) customers, (2) services, and (3) operations.
I will need to address these challenges:
- Jumpstart my practice,
- Protect my staff,
- Change the standard protocol to do business.,
- Change service offerings,
- Generate the same level of revenue (pre-COVID) with fewer clients.
Gap analysis
When I create a strategy and execute our business plan, I did not anticipate a pandemic shutting down most sectors of the economy. As a small business and when market conditions change, I need to have a strategy to stay ahead. I need to do a gap analysis comparison between a desired outcome and the actual business outcome.[1]
Automate your business process
An end-to-end automation strategy will increase staff productivity, better management, and more transparent communication for all stakeholders. Businesses must automate the operations end-to-end. Business process automation will also make your business more efficient. Automation sets the stage for a unified data model.
Data quality
We’ve all heard the saying, “garbage in, garbage out,” but when it comes to AI and machine learning, data quality is critical. As mentioned in the above paragraph, automating your processes will create a standard data model.
With the groundwork set, I need to quickly start collecting new data for the machine to begin the “machine learning” process. I am also cleaning the old data that I have received in the last 30 years.
The “marathon” portion of the data strategy is dependent on the volume and velocity in which your business can generate and clean old data. For my business, I expect a 6-12 months data collection phase. I am expecting to see some segmentation and several itemsets for refinement.
An intelligent agent
For the customer piece, I like to deploy an intelligent agent (chat-bot) to provide a conversational interface between our business and the customer. If you have automated your processes, you can immediately deploy an intelligent agent and provide data and information directly.
You need to think of revenue maximization as a transformative project, and using AI is not a one-time event. Like most journeys, I suggest that you set up milestones, measure your results, adjust as necessary, and continue your journey.
[1] https://www.clearpointstrategy.com/gap-analysis-template/