Small businesses are rapidly implementing artificial intelligence to gain a competitive edge over competitors. Are they doing it right? Let’s talk about the common pitfalls to avoid while adopting machine learning for your SMB.
There has been a lot of excitement about the advancements in machine learning algorithms in the last few years. There are a number of problems that can be solved in sectors such as financial services, healthcare, retail and others.
According to BCG, 84% of businesses believe that investing in AI and ML leads to greater competitive advantages – whether it comes to expanding business offerings or making substantial process improvements.
No wonder, small businesses are rapidly implementing artificial intelligence technologies to maximize their potential. While the benefits of deploying machine learning are numerous, ensure that you stay away from these 5 mistakes when introducing your business to AI or ML.
1. Deploying unnecessarily complicated ML technologies
Given that machine learning is not widely adopted by small businesses, many SMB owners are not fully aware of the different ML technologies out there. They risk ending up with tools that need large volumes of data to understand even the most basic use cases of the technology.
In the language of ML, such a scenario is called overfitting. In other words, you can have the technology without sophisticated algorithms, but without good data, it will give a poor predictive performance.
Therefore, be sure to do your research and choose a machine learning tool that needs only a small quantity of data and can be set up to run in full production in just a few hours. A Data Dilemma Report states that 12.5% of staff time is lost during data collection. That’s five hours a week in a 40-hour workweek.
You can feed your existing data into Datoin, an automated AI-platform. Using machine learning, it helps you predict the customer churn rate, the customers likely to churn, the daily sales and the demand for each product in your inventory. This way, you save valuable time and resources and start gaining much faster returns on the investment you made on the ML technology.
To start leveraging machine learning for free, for your business, sign up for a trial here – https://app.datoin.com/signup.
2. Assuming where to use ML technology
When it comes to organizational process knowledge, most businesses – big and small – are compartmentalized. The top management team is usually not involved in the day-to-day processes, and neither do they have access to process documentation.
As a result, the processes that you start applying machine learning to are not necessarily the most appropriate processes to work with. Business execs are using artificial intelligence to automate repetitive tasks such as timesheets (78%), scheduling (79%), and paperwork (82%).
It is thus crucial to incorporate process intelligence to determine which areas of work are genuinely ready for automation before you go ahead with a project.
A comprehensive understanding – based on facts, not opinion – of where machine learning will work and the kind of value-add and savings these technologies will bring to your business is critical.
3. Forgetting that it is a continuous process
Merely planning out your machine learning activities, deploying digital workers, and training your business algorithms is not enough. It is imperative to monitor every step of the process post-implementation and to assess your digital workforce regularly.
Machine learning is not a self-sufficient process. It calls for continuous measurement of the impact of automation at each stage to:
- Ensure protocol compliance at each stage
- Prevent bottlenecks from arising
- Ensure that automation is not causing any adverse effects at any point along the way
4. Missing out on high-value business cases
Most businesses tend to go for conventional options that center on what has worked before. They are thus likely to apply the technology to the task that recurs most frequently because such a task has the appearance of giving reliable results.
However, this kind of ad hoc approach to process choosing is likely to end up leaving out other business opportunities that might recur less often but which have high ROI. As a business, you should have a clear plan in mind of how you want to “land and expand” with machine learning throughout your company.
While it is understandable that you would want to start with processes that involve minimal disruption to the business or the way you interact with end-users, your ultimate organizational goals should be automating tasks to bring in the maximum ROI.
5. Relying too much on Robotic Process Automation (RPA)
RPA is easy to deploy, and its digital workers can be configured without trouble and can work just like humans once they are in place. Besides, it can considerably boost your company’s efficiencies by connecting with external data sources and legacy systems.
It is crucial to remember that RPA is focused on structured, repetitive work. On the other hand, machine learning can be applied to both structured and unstructured work.
When you add machine learning technology to RPA, the digital workers gain the cognitive skills necessary to extract useful information from different types of content, understand the intent behind and meaning of different kinds of documents, and enhance their decision-making capabilities. Your business should thus avoid relying on RPA alone.
Over to you
Machine learning has immense potential, if used correctly. This includes boosting sales, attracting more customers, forecasting demand and ultimately driving profits.
Luckily, Datoin, an ML-driven AI platform can help you experiment with data, for free, to know how you can use AI and ML for your small business.
Sign up for a trial here, and see how the platform can add value to your business, for free.