3 Ways Small Businesses Can Profit From AI

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Many business leaders are seeking to understand how artificial intelligence technology will create tangible value for their organization. For small and mid-size businesses (SMBs) especially, AI technology may seem inconsequential or out of reach, more applicable to enterprises, as it involves high levels of complexity and requires specialist resources.

AI solutions have been available for some years but have struggled to enter the mainstream. To a large extent, this has been due to a lack of quality connected data, the inevitable result of the landscape of siloed systems that characterize too many businesses. 

In response to the General Data Protection Regulation (GDPR)’s compliance requirements and as the result of digital transformation projects, significant progress has been made in recent years to connect the data that organizations are now collecting at unprecedented levels.

With AI capabilities increasingly being added to CRM, the technology has never been more accessible, but the central question remains: How can AI technology be used practically by SMBs to address the continual challenges of increasing productivity and better serving customers?

Here we’ll highlight three ways in which AI-powered capabilities within CRM can be readily leveraged by sales, service, and marketing teams in businesses of all sizes.

Reducing Processing Time

If users are spending significant portion of their day keying detail into CRM from standard forms such as received invoices or purchase orders, productivity will inevitably suffer. As well as being time-consuming, these labors also raise the risk of error when data is incorrectly transposed.  

By using an AI model, forms can be digitized to extract the key detail from a PDF document or a camera image to populate a new CRM record. By uploading a selection of standard documents, the AI model can be trained to automatically identify the core components that need to be extracted. This can include contact details, dates, invoice numbers, product tables, and amounts.

In evolving these processes, time will be quickly be saved and data quality increased. Job roles are also likely to be more satisfying without the drudgery of repetitive manual data input.

Uncovering Data Insights

As businesses increasingly connect their systems and processes, there has never been more data to manage and analyze. For example, customer surveys contain a wealth of valuable feedback and comments, but individually sifting through these entries to extract insight can prove laborious and susceptible to error.

Through natural language processing, AI models are able to automatically tag text entries stored in CRM that can be used for sentiment analysis. At scale, teams can then identify actionable insights from tagged words and phrases and even use these classifications to trigger processes.

As an example, insights could be quickly gleaned from a satisfaction survey. This would promote responses where positive or negative sentiments are detected, enabling prompt follow-up action. In another instance, trends can be identified from AI-powered natural language processing across support cases where terms are automatically tagged from CRM case records. When recurring service topics are highlighted, the system can react to those insights and take proactive steps to address emerging problems and time-consuming issues.

Predicting Outcomes

With access to the wealth of data stored in CRM, an AI model can be trained to associate historic data patterns with outcomes. These results can then be used to detect similar learned patterns in new data to predict a business outcome in the form of a binary classification.

This can be shown as a yes/no, true/false format, or any other answer that falls into one of two categories. By selecting the CRM fields of influence, this type of AI-driven model can be quickly developed to enable teams to instantly make assessments and answer questions such as “Does this customer qualify for an upgrade?” or “Is this a key contact?”

These AI-generated classifications can also be used as notification alerts for teams and trigger further actions. For example, if a pattern is detected that results in an account-at-risk classification being set to “yes,” an automated process could send a notification to the appropriate account manager.

Conclusion

AI technology is being democratized as it is infused with CRM and other business applications, creating new solutions that have the potential to help businesses of all sizes better manage their data.

But if these tools are to deliver on this promise, they must be easily accessible to end users. Successful solutions will enable all business teams to take ownership in building and managing their own AI models, without the cost and complexity of involving developers or data scientists.

AI value will always be reliant on quality data. Organizations that have already connected their core processes and have an effective data management policy in place are best positioned to profit most from these AI technology innovations.

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This post written by Preact's marketing director, Warren Butler first appeared on destinationcrm.com

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