Steps for Becoming AI-Ready in Field Service.

Many leaders in service organizations see AI as a boon for providing capabilities that can help them improve operations and serve customers better. After all, these days, high service quality is absolutely critical to driving greater revenue and customer satisfaction. But having a vision for AI and implementing that vision are two different things. Service leaders want to implement AI solutions in the most impactful way possible to ensure that service agents and field technicians.

Enabling quick, first-time resolution of customer issues in the field is critical to ensuring the efficiency of service operations and providing the best possible experience for customers, to help build loyalty and grow revenue. Arming field technicians with intuitive, AI-powered solutions that combine capabilities such as workflow automation, scheduling algorithms, and mobility can significantly enhance service outcomes. Research shows that high performing field service organizations have been early to embrace AI and automation to help improve job performance—and 93% of mobile workers in high performing organizations also report job satisfaction as a significant benefit.

AI has the power to drive new levels of productivity and efficiency in field service, so it’s critical to take the right steps to ensure your organization has the most effective approach to adopting—and gaining the most value from—AI solutions. If you want to learn more about how to turn your AI vision into a reality.... More Details

Promise of AI for field service organizations. Try it now!

5 ways IT departments can get started with low-code and AI

With AI and low-code development tools, IT teams can tackle the problems they know best, without additional assistance from outsider developers. Putting AI-powered low-code development tools in the hands of pro developers is like handing over the keys to a solution machine. With vast, built-in app-building knowledge and conversational language prompts that guide beginners and turbo-boost pros, AI can speed up the time to build solutions for the unique needs of your IT department.

By using AI and low-code development, IT teams can build smart, fast solutions to their own problems—problems exacerbated by growing demands on data. CIOs who can bring AI-powered low-code and no-code solutions to their employees are putting their organizations in prime position to drive innovation.

Use cases for an AI-powered, low-code development platform abound, especially in an organization’s IT department, but we’ll focus on the five things we’d imagine IT teams would want to start doing if they were empowered with those tools today.

Start using AI-powered low-code development today to transform your IT strategy and stay ahead in the digital landscape. To find out how Copilot Studio can help your organization’s IT department, try a demo today...... Read More

AI and low-code tools empower IT teams for faster solution development Experience it now!

GitHub Enterprise Server 3.13 is now generally available.

GitHub Enterprise Server 3.13 is now generally available. It includes many new features for developers, enterprise admins, and operators. All of this is to help your organization build better, more secure software, faster..

Getting the information you need quickly is essential to keeping your workflow efficient. The latest UI updates to GitHub repositories are thoughtfully designed to enhance productivity. It’s easier to find what you need using the improved code search, and special files are simple to spot with a prominent display alongside your README.

Building and maintaining software demands insights into potential vulnerabilities and responses. This GitHub Enterprise Server (GHES) update empowers teams to meet these requirements head-on. With greater accessibility into security reporting and clearer insights into security metrics and trends, it’s easier than ever to identify and address issues proactively, reducing risks down the line. And with insight into the adoption rates of security features at your organization, your teams are not only spotting vulnerabilities, but also helping team members understand their role in keeping your organization secure.

Ready to give your teams the latest and greatest of what GitHub Enterprise Server has to offer? Download GitHub Enterprise Server 3.13 now... Read More

Find the information you need, faster and keep your workflow efficient... Meet once MTC exports

Guidance on Logic App Standard Load Testing and Optimization.

Logic App Standard is a cloud-based service that allows you to create and run automated workflows to integrate apps, data, services, and systems. Load testing is the process of putting demand on a system and measuring its response to ensure it can handle high usage and traffic. Performing load tests on your Logic App Standard workflows is essential to identify performance bottlenecks, ensure scalability, and maintain reliable operation under peak loads.

Always define your performance targets/load profile. Are you measuring throughput (X workflows completed per minute), latency (Y ms workflow run completed), response time, CPU usage? If this is the first time running performance test, capture baseline performance targets observed, iteratively run test whenever there are optimizations done in code or compute configuration, and then compare values to check if there have been any improvements.

In the rest of this blog, we will use throughput as a metric (X workflows completed per minute) to determine and evaluate the outcome of load tests. We will demonstrate how to monitor the load test, analyze potential issues if the outcome does not meet our goals, and how to improve the results based on the collected metrics.

Check Application Insights attached to the Logic App, During your load test, monitor the "online servers" metric to evaluate the scalability of your Logic App..

Compute tab displays compute-related metrics like instance count, average memory working set, CPU, and memory percentage.... Read More

Check the built-in metrics of the upstream event producer… Get in touch MTC! 

The economic impact of migrating to Azure for AI readiness.

As the digital landscape rapidly evolves, AI stands at the forefront, driving significant innovation across industries. However, to fully harness the power of AI, businesses must be AI-ready; this means having defined use-cases for their AI apps, being equipped with modernized databases that seamlessly integrate with AI models, and most importantly, having the right infrastructure in place to power and realize their AI ambitions. When we talk to our customers, many have expressed that traditional on-premises systems often fall short in providing the necessary scalability, stability, and flexibility required for modern AI applications.

When asked whether being on Azure helped with AI-readiness, 75% of survey respondents with Azure infrastructure reported that migrating to the cloud was essential or significantly reduced barriers to AI and ML adoption. Interviewees noted that the AI services are readily available in Azure, and colocation of data and infrastructure that is billed only on consumption helps teams test and deploy faster with less upfront costs. This was summarized well by an interviewee who was the head of cloud and DevOps for a banking company

We didn’t have to go and build an AI capability. It’s up there, and most of our data is in the cloud as well. And from a hardware-specific standpoint, we don’t have to go procure special hardware to run AI models. Azure provides that hardware today.”

They explained that now, “The scalability [of Azure] is unsurpassed, so it adds to that scale and reactiveness we can provide to the organization.” They also said: “When we were running on-prem, AI was not as easily accessible as it is from a cloud perspective. It’s a lot more available, accessible, and easy to start consuming as well. It allowed the business to start thinking outside of the box because the capabilities were there.”

Forrester’s study underscores the significant economic and strategic advantages of migrating to Azure for be AI-ready. Lower costs, increased innovation, better resource allocation, and improved scalability make migration to Azure a clear choice for organizations looking to thrive in the AI-driven future.

Learn more in our e-book and video on how to migrate to innovate..... Read More

Essential or significantly rescues barriers for AI adoption… Talk to MTC experts now!