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ServiceNow’s latest Madrid upgrade has introduced many new, exciting features that promise to create a more connected workplace. It has also improved many of the platform’s existing innovative features. One of those improvements is to their Agent Intelligence product, which brought machine learning to their platform.

Machine learning offers incredible value to enterprises, but it does demand strict data management to train predictive models properly. Without accurate, high-quality data, that value vanishes.

Is it worth it to invest time and money into AI?

Once in place, Agent Intelligence streamlines business processes and improves service delivery. Businesses should do all they can to take advantage of this; those that invest time into its implementation will continue to see a return on that investment well into the future.

Making the Workplace Smarter with Agent Intelligence

ServiceNow debuted their Agent Intelligence product with the release of Kingston. Companies can leverage this new product to increase efficiencies dramatically; Agent Intelligence could categorize and assign high volumes of work much faster than IT professionals. This automation not only reduced the time staff spent on routine tasks, but it also drastically reduced error rates from manual work.

Madrid, ServiceNow’s newest release, expands on their machine learning product by bringing in contextual predictions and natural language processing.

The new Similarity Framework uses deep learning techniques to take context into account and make intelligent recommendations. It does this by linking together similar cases and alerts to identify significant incidents. Then, it can provide staff with recommendations within the context of the customer’s issues.

What Enterprises Need to Consider Before Investing in AI

Machine learning and artificial intelligence models are still relatively new to the platform. You still need to train these models for them to be effective. This requires strict attention to data management across the enterprise. After all, the data used to train predictive models needs to be accurate and organized.

Depending on past management of data and governance, this may be a massive undertaking. We can expect continued, more in-depth machine learning features from ServiceNow that will undoubtedly demand a baseline of continual data management.

Besides the financial investment in a machine learning product like Agent Intelligence, companies need to consider the time and effort it will take to clean up their data house.

Enterprise-Wide Benefits of Machine Learning

Despite the sometimes monumental task of cleaning up massive amounts of data, there are many clear benefits of implementing AI that make it well worth the effort.

ServiceNow’s Agent Intelligence product reduces the time it takes to resolve tasks, reduces the interactions involved in those tasks, and reduces manual processing errors.

Bringing machine learning into IT services has expanded service delivery and improved incident response times. Being able to train these models using historical data helps companies predict major incidents and proactively resolve them, or address smaller issues that could cause significant problems in the future.

With machine learning and artificial intelligence comes automation, which streamlines processes across entire departments. Because Agent Intelligence automatically categorizes and routes huge volumes of requests, staff can spend more time on less tedious tasks. Automation also reduces errors and oversights that are common with manual processing.

Seeing ROI Now and Into the Future with ServiceNow Agent Intelligence

Given Madrid’s latest improvement to Agent Intelligence, it is certain that ServiceNow will expand its machine learning capabilities to other areas within their platform in the future. Business areas across the enterprise would benefit from this, and companies with a solid baseline of data management and governance will allow them to take advantage of this early on.

Based on this prediction, companies leveraging machine learning to build predictive models and automate processes will see a return on their investment right away and well into the future. It will continually improve business processes and eliminate inefficiencies, saving companies money and frustration.

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