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Artificial intelligence and machine learning can help make ITSM processes more efficient.

CIOs are now using artificial intelligence (AI) and machine learning (ML) technologies to make IT service management processes more efficient.

A typical use case for artificial intelligence in ITSM involves natural language processing (NLP). User requests for IT services are automated using NLP. IT practitioners get a deeper understanding of their processes by applying machine learning (ML) to ITSM data. The natural language processing technology that powers virtual agents is very often integrated with channels that the employees are familiar with. Many organizations integrate virtual agents with chat services like Slack, where employees can directly communicate with the IT service desks.

ITSM systems generate large volumes of data, so applying machine learning to these systems makes sense. The data collected by these systems is large not only in volume but also in detail. All of this data helps us understand existing IT assets and processes, along with information about their ownership.

These insights help IT understand the real priorities of ITSM issues, work proactively instead of reactively, accelerate time to resolution and enhance employee productivity. In the current age of remote work, enhancing employee experience to ensure business continuity is at the top of every CIOs mind, and artificial intelligence will prove to be just the right technology to use to face this new challenge.

Lets look at the three ways artificial intelligence will help IT MSPs to do better in 2021.

Chatbots integrated with an ITSM environment can easily be used to categorize the problem in employee requests. For example, if an organization has integrated Freshservices Virtual Agent with MS Teams, it creates a channel for employees to raise a service request or resolve their issues. The chat interface is a familiar UI for the employees, and the chatbot will identify whether the employee has a service request or an incident to raise using machine learning.

Another important and time-consuming task normally performed by an agent or complex workflows is routing a ticket to the correct support groups. Chatbots will triage the incoming requests or incidents to the right support group, making the process a lot efficient.

The historical ticket data and an extensive ITSM knowledge base will help agents resolve various requests faster. However, this requires the admins/agents to create an extensive knowledge base covering a wide range of requests and incidents. The ability to directly convert a resolution email to a knowledge base article will help build a rich knowledge base repository. When a similar problem arises, AI and machine learning can be used to dig through this repository and present the closest match to resolve the issue faster.

A well-managed repository will also help with incident resolution throughout the solution. AI can provide advice that is as simple as a related or similar incident along with its history, or a solution article with words that match the current incident/request, thereby shortening the time taken to think through the issue from scratch.

Like employee onboarding, many requests to IT demand human staff hours to perform a series of complex tasks to fulfill the requests. Machine learning models watch and learn how humans carry on and execute them to automate them in the future. By recognizing patterns in the request types and execution methods, machine learning-based models make intelligent suggestions for even the most complex IT processes.

Hemalakshmi is a Product Expert with Freshworks. Her responsibility includes educating and helping industry peers and customers on best practices, tips and tricks, quick guides, and solutions around IT Service Management and its various use cases. In her 6+ years of experience in the core SaaS business applications serving as a product expert, Hema has worked with multiple businesses in helping them with their business needs and setting up their service desk solution Freshservice.Follow her onLinkedIn.

This guest blog is part of a Channel Futures sponsorship.

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3 ways Artificial Intelligence Will Help IT MSPs Do Better in 2021 - Channel Futures

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Conventional wisdom about nonprofit fundraising considers these two statements equally true: 1) Acquiring new donors loses money, and 2) Future gifts from new donors make up for the money lost on acquisition.

Alas, only one of them is true. Acquiring new donors does, indeed, lose money, often estimated at 50 percent of the initial gift. However, according to Blackbaud, fewer than a quarter of those initial donors will renew their gift. The math gets even worse in out-years, as 60 percent of donors lapse year after year.

The reality is that most organizations spend an enormous amount of time frantically trying to refill their leaky bucket of donors. The result is a transactional approach to fundraising that requires constantly asking for donations rather than spending time getting to know donors, particularly donors who arent writing huge checks. Just because its the norm, however, doesnt make it good or effective, particularly during a pandemic when everyone is distracted, scared, and stretched.

We recently released a report funded by the Bill and Melinda Gates Foundation on using artificial intelligence (AI) for fundraising and philanthropy. The report outlines ways that nonprofits are beginning to use AI to increase giving, and while the fact that the most powerful technology in history can help nonprofits raise more money didnt surprise us, we were surprised by how much opportunity nonprofits have to use AI to re-imagine and re-humanize fundraising.

AI automates tasks that previously only humans could do. The field isnt newits been around for decadesbut its recently become much less expensive, making it available for everyday use and by smaller organizations.

AI tools for increasing fundraising currently include:

One example of a nonprofit putting AI tools into action is the 24-hour fundraising marathon Extra Life, a fundraising effort of Childrens Miracle Network Hospitals. Staff members were getting overwhelmed answering the same question from Canadian supporters, who wanted to know what currency Extra Life would use to process their donations. To ameliorate this, Extra Life added a chatbot to its donation page specifically to answer this question, and even used an algorithm to personalize the landing page so that the chatbot appeared only for Canadian donors.

The chatbot on the Extra Life website provides instant answers to common donor questions about things like conversion rates.

Another example is the Cure Alzheimer's Fund, which raised $1.2 million in donations using Gravytys AI-powered fundraising software. Gravyty drafts emails to existing donors based on their preferences and previous actions, and highlights donors who are on the cusp of lapsing. Staff members review the emails and cultivation plan, then send them out the door. Gravyty isnt just automating renewal letters; by helping fundraisers continuously improve the specific content and timing of messages to individual donors, its adding more intelligence into the fundraising system.

Similarly, Rainforest Action Network piloted software from Accessible Intelligence Limited in May 2020. This software recommends the right content to include in fundraising appeals (including writing style, specific ask, and even subject lines to test), as well as the right number and interval of communication touch points. As a result, open rates and signed online petitions increased significantly. More importantly, conversion of one-time donors to monthly donors increased 866 percent. (That is not a typo!)

Since the publication of our report, weve been thinking about the time development staff could save by using AI. What could change? What could staff do differently or better with this precious gift of time? We see a great opportunity for development teams to patch the holes in the leaky bucket of fundraising, and enable their organizations to move from transactional to relational fundraising, starting with these three activities:

1.Add retention rates to dashboards and budgetary calculations. We have served on many boards and cant recall one discussion focused on donor retention. Organizations need to measure and monitor donor retention rates over time. They also need to calculate the net cost of fundraising, as well as the cost of money raised through acquisition and lost through lapsed donors over time.

2.Put time for conversations with donors, clients, and volunteers on the calendar. Activities that arent on the calendar dont get done. Staff and leading volunteers (such as board members) need to spend time listening to donors and stop treating them like ATMs. Instead, they need to find out why the cause is important to each donornot just major donors, but donors at every leveland what makes them feel good or bad when they give.

3.Establish ethical-use guidelines around the use of AI. Its critically important that organizations use the incredible power of AI with great care. We recommend establishing an outside committee of advisors to discuss issues such as the use and storage of data, the need to inform people when they are talking to a robot and not a person, and careful monitoring of AI-powered efforts for racial and other biases.

One person we interviewed for our report said, AI cant fix bad fundraising practices. Our greatest fear is that nonprofit leaders will use the incredible speed and power of AI to supersize existing transactional fundraising practices. We implore them to take the care and time needed to create a new chapter in fundraising, where every person can be heard and where most donors stay with causes for years, not months.

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Re-Humanizing Fundraising With Artificial Intelligence - Stanford Social Innovation Review

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Python and artificial intelligence are the future so learn it all here for less than $5 a course - The Next Web