Artificial intelligence (AI) is transforming workplaces across industries—boosting employee productivity with embedded tools. While AI offers countless use cases, the key to success lies in identifying the right applications and overcoming challenges like data security and integration complexity.
AI is still making waves at work, and with good reason. Across all industries, AI tools are expected to ratchet up employee productivity by an estimated 40%. Embedded tools like Microsoft Copilot or Adobe Firefly are making the biggest and earliest digital splash, injecting new efficiencies without feeling like a heavy lift.
In fact, 77% of us already use AI at work, but because embedded tools can feel so non-invasive, only a third of us realize it. At the same time, the use cases are so widespread that implementing AI with ROI in mind can feel a bit like running through a generative maze.
To help us plot a course, I spoke with two experts in the field: Dan Ortiz, Director of the Digital Workspace Center of Excellence at Connection, and Chi Chung, Connection’s Director of Solution Architects.
A Flood of Use Cases
Creating new policies and directives for something as far-reaching as AI can be resource intensive. AI has literally hundreds, if not thousands, of use cases in business, including:
- AI-powered customer service chatbots and virtual assistants that resolve issues 24/7
- Data analysis algorithms that quickly uncover trends, patterns, and insights
- Automation of routine tasks like data entry scheduling and email management that free up valuable employee time
To double-click just one of those, early adopters are already using AI chatbots to give level-zero responses that solve customer problems at the first touch. These chatbots can be incredibly robust thanks to the wealth of information many orgs have developed over time.
“The takeaway here,” says Ortiz, “is that there are so many use cases, it’s incredibly important to be intentional about how we move forward.”
1. Lay the Groundwork
Chung explains that any organization that wants to increase productivity with AI should start by asking one important question: What use cases will benefit us most?
“That’s really the key,” says Chung.
To get there, Ortiz recommends that organizations workshop their AI goals and benefits, creating an AI task force or committee that represents all the different functions of the company. “That task force should then identify potential benefits and outline the success criteria.”
From there, the task force should define a clear action plan and determine the criteria they’ll need to meet before they implement.
2. Identify the Challenges
The next big step in the shortest path to AI productivity gains is to spotlight the challenges that are in our way. Among the biggest are data security, data silos, and integration complexity.
- Data security: It’s mission-critical to keep your organization’s data security in compliance with privacy regulations and guidelines, even as AI steps in to help.
- Data silos: Data is stored in different systems in every organization. This can make it very hard for AI to see the bigger picture, hampering its insights and analysis.
- Integration complexity: Integrating data from multiple sources is complex and often requires custom connectors or additional software, increasing complexity and the cost of integration.
- Data quality and consistency: If you don’t have good data hygiene, you can’t keep your data quality consistent across all your different platforms. This creates a garbage-in, garbage-out scenario, as Chung points out.
“Start by identifying the data sets that will help you reach your target level of success,” Ortiz says. “Then, thoughtfully align your AI efforts with the benefits you’d like to get.”
3. Choose Between Embedded vs Custom AI Solutions
The next important choice is deciding on the right AI solution. Embedded AI tools like Copilot and Firefly are ready-made to tackle a wide variety of real-world business situations. Other custom-made solutions can cost more and take time to develop, but offer tailored features.
Chung and Ortiz encourage stakeholders to complete a technical readiness assessment before they settle on a tool. Fully evaluate your team’s data hygiene, identity, governance, access posture, technology gaps, and how you’ll prepare your user community to use AI tools. Chung adds, “It’s often best to start with embedded AI, then baby-step your way into a custom solution, using APIs and adding tailored features.”
4. Where to Start
Any new venture as complex as boosting productivity with AI can be daunting. That’s why it’s best to start by talking to an AI expert. Professionals like Ortiz or Chung can help you pinpoint where you are in your AI journey, what you can achieve, and the fastest way to get there.
“Connection has the best SMEs in the industry,” says Ortiz, “and we’d love to talk to you. Let us know what’s on your mind and let us work for you.” With the right team on your side, realizing real productivity gains with AI isn’t as challenging as you might think.
To hear more from Ortiz and Chung about implementing AI solutions, you can watch the full webinar.
Explore our AI solutions and Digital Workspace services today by contacting an expert at 1.800.998.0067
Brian Gatke
Brian Gatke, Vice President of GTM Solutions Strategy and Portfolio at Connection, has over 20 years of experience with top IT service partners. He is recognized for his collaborative leadership, use of customer insights, and data-driven approach in developing and launching Connection’s service portfolio, enhancing brand visibility.
