AI/ML provides a major boost for data quality, cybersecurity, automation and many other transformation targets.
The definition and business value of digital transformation took another leap forward when artificial intelligence (AI) and machine learning (ML) entered the conversation.
A typical digital transformation project will produce dozens or even hundreds of significant deliverables. Unlike the project management deliverables, many technical deliverables can be enhanced by applying AI/ML technology.
Here are some of the major digital transformation deliverables that AI/ML can boost. Consider including these concepts as you plan your digital transformation projects.
Data quality improvements
The success of digital transformation initiatives is highly dependent on quality data. Unfortunately, the data quality in many application datastores is inadequate. Manual analysis and correction of data errors are expensive, time-consuming and tedious.
Engineers can enlist AI/ML to rapidly identify errors and propose corrections with a high degree of accuracy. This capability improves the success of digital transformation initiatives by reducing elapsed time and costs while increasing quality.
The benefits of improved data quality include:
- Higher confidence level in recommendations produced by data analytics.
- Improved understanding of operational performance.
- Higher confidence level in the accuracy of reported KPIs.
Unstructured data to structured data conversion
Achieving value from digital transformation projects is almost always hampered by much more corporate data being stored in unstructured documents rather than structured databases. This table provides an overview of the problem:
Engineers can enlist AI/ML to rapidly process large volumes of unstructured documents to identify search terms and metadata and store that as structured data. Industry-specific business rule repositories can be used as initial training data to develop the required models.
The significant benefits of this conversion include:
- Achieving business value from unstructured data through search terms and metadata for the first time in many organizations. This benefit has eluded organizations in the past.
- Simplifying the data analytic software required to query structured data derived from unstructured data.
- Dramatically increasing the efficiency of querying formerly unstructured data.
Software development acceleration
Almost every digital transformation project will develop some custom software, typically to perform data integration tasks.
Engineers can enlist generative AI to develop draft software code. These drafts still have to be carefully reviewed and tested by software developers. Nonetheless, using generative AI to build custom software reduces effort leading to lower costs and earlier completion.
Automation upgrade with AI/ML
Many organizations have implemented automation for at least some of their processes and are reaping its benefits. However, most automation operates rigidly with no flexibility or adaptability.
Upgrading automation with AI/ML as part of a digital transformation project adds the following capabilities:
Upgrading automation with AI/ML increases the following benefits of automation:
- Product quality consistency.
- Higher capital and labor productivity.
- Lower cost.
- Reduced manual tasks.
- Less unscheduled downtime.
- Less human error.
- Increased workplace safety.
Data analytics enhancements
Organizations often conduct digital transformation projects because the available data analytics isn’t delivering enough value. Usually, opportunities exist to improve data analytics by making more data available via widened application integration.
Engineers can enlist AI/ML to enhance data analytics to achieve the following benefits:
- Reduce elapsed time to process large queries.
- Identify trends and patterns that are difficult to detect using traditional data analysis methods.
- Produce higher confidence forecasts.
- Apply sophisticated algorithms.
- Automate various data analysis tasks to reduce related manual effort.
- Support real-time insights if necessary.
Automation upgrade for IIoT
Many organizations have implemented applications that leverage the Industrial Internet of Things (IIoT) data their SCADA systems generate.
Engineers can enlist AI/ML to increase overall efficiency through real-time orchestration of business processes across the following systems:
- Process control systems (PCS).
- Manufacturing execution systems (MES).
- Enterprise resource planning (ERP).
The benefits are:
- Reduced unit costs.
- Reduced scrap rates.
- Respond to changes in demand or supply chain issues more easily.
Data augmentation
Often existing corporate data is inadequate for training machine learning models. They perform poorly if the training data lacks sufficient variety. Data augmentation advances digital transformation by increasing the number of examples in training datasets to introduce more variety in what models see and learn from.
Augmenting corporate data into a new dataset can add value if your digital transformation project includes producing high-quality training datasets for machine learning.
Cybersecurity defenses
Sometimes digital transformation projects see cybersecurity as out of scope or inadvertently take actions that undermine existing cybersecurity defenses. These situations add cybersecurity risk for the organization.
Instead, engineers can enlist AI/ML to bolster cybersecurity defenses. AI can strengthen cybersecurity defenses by:
- Better recognizing phishing emails.
- Identifying unknown threats through learning.
- Identifying deviations from normal network behavior.
- Scanning log files for intrusion-related network activity, a time-consuming task that humans perform poorly.
- Handling monotonous and repetitive security tasks consistently.
External data source identification
Often digital transformation becomes more valuable when an organization integrates external data sources with internal ones.
Engineers can enlist generative AI to identify the external free, open source and subscription data sources the organization should consider implementing.
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AI/ML technology opens new avenues for engineers to enhance the business value digital transformation projects can deliver for their organizations.
