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When to consider AI during digital transformation projects

When to consider AI during digital transformation

It is exciting to see the potential application of intelligent and decision-making software in so many enterprise verticals and markets. The benefits it could bring to improving medical diagnosis, emergency service routing, production, and IOT augmentation are just to name a few opportunities and applications. While AI today is more of a human decision-making aide than the rise of sentient beings from science fiction movies and books, it still captivates and inspires the next generation of innovators entering the market.

If you look at the computer revolution in general we, for the most part, have replicated the computing methods used by the human brain in our machines. We and ‘they’ use short and long-term memory to store relevant data for easy access. The key to how effective our decision making could be all depends on the amount of good data we have remembered to aid the process and increase the chances of an effective outcome.
If you look at how you make decisions yourself, the more life experiences you apply to your decision making the better of a decision maker you become, which adds to your intellect and credibility among peers.

My main role as a leader in digital transformation services is to help businesses use technology to improve the way their employees and customers interact between each other and with their internal systems to solve key business challenges and improve ROI on IT spend. As a team, we are in real client engagements daily talking to customers about the application of many technologies. A question we get quite often is:

How do AI and Machine learning modules affect deployments right now?
Should I be thinking about AI during my digital change planning?

The answer is yes. But it may not be in the way you think.

AI Software Deployment

The key to success is the right data

The biggest advantage AI can bring to the type of digital transformation my teams provide services around is telling business leaders more about the day to day workings of their business and creating a pool of data to pull information from so that the software can analyse business productivity and your biggest risks.

For any pre-built or custom AI analytics programme to be successful, it needs to have all the right data. Therefore you need to be using tools that pick up the right data. Many productivity tools out there claim to have AI technology built in, such as recommendations for more efficient scheduling or intelligent data sorting. However, very few of these tools are making big differences in how people work today. For any AI enabled application to make a real difference to an organization it needs to know the most it can about your business.

How pre-built AI modules help you collect the right data

I normally recommend IT leaders consider pre-built AI modules as securing AI developers can be difficult and costly. To find out if there are pre-built AI modules out there that can benefit your business you would need to ask yourself three questions.

Key planning considerations for pre-built AI

To give you an idea of the applications let’s look at a specific vertical. In the manufacturing industry AI analytics could tell you if business process inefficiencies are impacting the production of products in key sites by looking at the approval process and time employees spend trying to resolve issues. To do this your AI module would need to pull data from the below sources.

AI module data mining sources

If the AI module has the right data it can be tasked with looking for key production gaps and how your internal teams work to resolve them today. You could also assign it to look at travel expense, staff coverage, and to search for key topics or discussions that can tell you more about your business’s biggest problems. If you have access to the audio discussions that are happing between employees and customers you can have the tool find and play back meetings where your employees or customers are distressed or having difficulties. This data could take months to gather normally. By catching issues like this sooner with AI you can prevent a horrible financial year from ever happening. The AI module can also data mine this information and search keywords or identify key people from your leadership team that are most involved with your business to help you find out what types of issues are occurring most frequently.

Most pre-built AI is not yet sophisticated enough to perform these tasks. However, deeper machine learning and custom AI software can do these tasks even today, and the capabilities of these tools are expanding every day.
So what does this mean for you now? Start deploying the applications that will gather this data for you or your data scientists today.

Key Data Collection Tools

So how do you do this? How do you make it easier to pull data from your business?

Select tools that have AI APIs build in them already and plan your digital transformation strategy to maximize the use of said tools in your everyday business processes. Microsoft makes this very easy with Azure and Microsoft 365. By using the wide array of pre-built and custom AI that is built to work with their productivity and communication tool set which eliminates time-consuming and expensive integration work down the road.

The Microsoft AI platform

Once you deploy the wide set of productivity and analytics tools provided by your Microsoft 365 licencing, you start collecting the key intelligence about your business that your AI tools will use in the years to come. For example: If your internal employees use SharePoint for collateral storage, Yammer for social collaboration, Exchange for email, Power BI for financial and employee tracking, Skype For Business or Teams for Chat and PBX/Contact Centre functionality, and the wider set of Microsoft 365 productivity tools you have created a mechanism for capturing the data you need just by deploying the services correctly.

Products like Microsoft Teams have built-in API’s for you to gather text, data, voice and email; and with the Stream channel, you can gain access to video and media across the business. With the Cognitive Services Pre-Built API modules, you can use the facial recognition API, speech to text tools, translation services, and other Azure integration points to data mine Microsoft Teams information or any of the other tools.

AI, data and… your employees!

With the right tools, you can start gathering data that will help your business use more and more AI assisted technology for years to come. But there is a catch…  Your employees need to use your Microsoft tools correctly to prevent missing key data from your business, and in some cases, you will have to integrate the Microsoft tools with current assets to make that journey easier for your employees. The key to do this is not launching new tools like you would launch a new ‘App’ but to launch a new way of working to your business. Therefore, you have to work with specialists who understand not only the technology deployment best practices but the best practices to enable the workforce appropriately and effectively.

Follow a proven Change Management Process

In summary, AI enabled tools are something all IT decision makers should be thinking about. But when you are looking at digital transformation or technology refreshes… I personally advise my customers to work with my specialists or similar organizations to create a trackable and measurable way of working to create the perfect environment for an AI-enabled future business. Regardless of your strategy for business intelligence AI applications, you will be maximizing the value of the tools you are investing in today!

Read on:
How to successfully implement Microsoft Teams with Organizational Change Management
How AI Revolutionises Team Collaboration [infographic]
Is Teams the answer to your UC problems?

Arkadin Cloud Transformation Services for Digital Transformation Projects

About the author

Charlie is Vice President, Managed Collaboration And Communications for the Cloud Communications division of NTT. He's been in the telecommunications industry for more than 15 years working for providers such as MCI-Verizon, Genesys, West Corporation, and now NTT gaining vast experience across the North American, EMEA, and APAC markets. His primary focus has been professional services related to the adoption of collaborative services and technical integrations for the last 10 years. Now he is focusing on end to end digital transformation services around cloud datacentre and intelligent communications.

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