Special Feature
Part of a ZDNet Special Feature: The new SMB stack

3 ways SMBs use machine learning to power digital transformation

Companies leverage AI for marketing, document management, and contract lifecycle management.

AI for business: What's going wrong, and how to get it right
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Businesses should start small and fail fast with machine learning (ML) projects to get the best ROI. Some of the most common use cases for small- to medium-sized businesses (SMBs) include fraud detection, sales optimization, marketing, and document analysis.

But the benefits of implementing ML goes even further.

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Steve Tycast, director of data and analytics at AIM Consulting, said ML efforts focused on operational analytics can reduce costs, drive efficiencies, and increase speed to market. And Craig Kelly, vice president of analytics at Syntax, said SMBs can use ML to anticipate the short-term and long-term impact on sales and adjust the strategy accordingly.

Further, the global impact of the recent coronavirus is something that is driving trends in ML usage. 

"A specific example of this would be companies that manufacture PPE can better anticipate fluctuations in demand, and understand specifically where limited materials will be needed most," Kelly said.

Where else are SMBs using machine learning and artificial intelligence (AI)? Here are three more examples of current trends.

Marketing

Kristina Conely, director of data and analytics at AIM Consulting, said AIM worked with a hotel company to improve its marketing programs using machine learning. At the start of the project the company had no centralized data repository, and all the reporting was manual. The first goal was to analyze products and services sold in a particular region and look for new marketing pitches. The company owns hotels and wanted to automate the process of recommending upgrades and add-on experiences for guests. To accomplish this, AIM helped the company create multiple machine learning models.

Conely said she and the client's marketing team refined the algorithm over the course of three weeks to make sure the team trusted the results.

"At first the response was: 'These two things will never sell together.' But as they saw results they came on board," she said. "They also realized that now they had time to do the analyst-type role they were brought on for."

Tycast said part of the process of implementing ML is helping clients understand how to create and modify the algorithms.

"You can underfit and overfit models. Sometimes if you remove lots of variables you can gain very high accuracy scores," he said.

Contract analysis

Another ML project that's a good fit for small- and medium-sized businesses is contract analysis.

AIM Consulting worked with a small law firm that needed help identifying which cases had the highest likelihood of a successful outcome. The company has a small team that spends a significant amount of time reviewing past cases to make these decisions. 

AIM used natural language processing (NLP) to read the historical documentation and legal outcomes, compared that information to the potential case, and then derived a scoring mechanism. Tycast said that using NLP dramatically shortens the research time and decision-making process.

iManage also works with law firms to analyze the contents of contracts via RAVN, a legal knowledge engine. Nick Thomson, general manager for iManage RAVN, said that doing a legal review of a contract is laborious but highly skilled.

"This gives companies a competitive advantage -- it's essentially a tool that can perform the tasks of a junior associate at scale," he said.

However, the  challenge for this ML application is that there is little publicly available information for training the engine. 

"When you don't have that option available because of confidentiality, you must have very trainable technology and train the end users themselves," he said.
 
The company offers a virtual Artificial Intelligence University that allows legal and financial professionals to bring their own real-world data to a workshop to address specific business issues. Thomson said this one-on-one approach allows companies to use confidential information in the exercise and teaches people which processes or queries are a good fit for ML analysis and which are not.

Contract management

Another good use case for ML is contract management, specifically automating the signing process. Software company Conga helps businesses automate contract lifecycle management (CLM) including the need for multiple signatures on a paper document. The platform allows Salesforce users to manage contracts directly in the application, while automating CLM from creation to signature. The software also automates reporting, tracking, and reminders.

Conga's Digital Transformation Officer, Aishling Finnegan said that the best approach to using ML is  to map technology to a company's existing processes and build an individualized road map for digital transformation.
 
"If you have a more programmatic approach, you're more in control, and it feels less overwhelming," she said, adding that demos of AI software are often too complicated.

Finnegan said that automating the contract process is especially important now that entire companies are working remotely.

"Sales teams are able to generate vital important documents at home and get them to clients quickly," she said. 

Automating and analyzing the sales process can spot holes in the pipeline, Finnegan added.
 
"This helps you know where you are in the customer life cycle, and it can automate triggers and reminders without anybody touching it," she said. "This gives the business visibility, and if something has stopped, you can figure out if it's your salespeople or the customer."

Lauryn Haake, managing director of HBR Consulting said that companies don't want to slow down right now, despite the economic disruption.

"It's quite the opposite," she said. "People want to figure out how we make things happen in this new working order with a sense of urgency around initiatives they thought they were going to play for 2020."

Automating the document signing process is a simple thing with a dramatic impact, Haake added. "Electronic signatures will have a direct and measurable impact on how quickly you can achieve revenue," she said.

How should SMBs approach AI?

Syntax's Kelly said that some companies view ML as an unnecessary expense, which prevents them from even exploring it. Tycast agreed, adding that cost is definitely a barrier for SMBs.

To counter this, AIM Consulting developed a data science methodology to allow for the rapid prototyping and testing. 

"This allows companies the ability to focus their attention on the right solutions that demonstrate potential value and ROI incrementally delivered over time," he said.

For businesses that do want to explore ML, costs can include:

  • Training employees to have the required skill sets 
  • The cost of data extraction tools and processes
  • Storage of very large data sets
  • The cost of computation of long-running algorithms

Tycast said that he always asks customers how ML fits into the overall digital transformation strategy.

"If a client doesn't have one, and the ML project is more of a one-off, that's a red flag," he said.

Also See

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The true costs and ROI of implementing AI in the enterprise  

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