Automation thoughts

Implementing AI Across Teams

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Do you feel threatened whenever AI is mentioned on your team? Since everyone says “AI is coming to take our jobs”, of course, it is okay to break a sweat. But then, that’s only a saying that can become reality when your team doesn’t use AI for tasks. Imagine a world where AI handle repetitive tasks, freeing us to focus on innovation. Sounds like a utopian future, right? But the future is here already!

How Can AI Help Teams?

The long hours spent on arguing about failed processes, wrong figures and several issues during scrum meetings and other team gatherings are drastically reduced when tasks are automated with AI. I mean punching the keys on a calculator or reading trends on a graph to determine sales outcomes are self-imposed stress knowing fully well that AI does it better. AI can be a game-changer for teams across various departments, boosting efficiency, productivity, and overall performance. From Sales to Marketing to HR and the list goes on. Other ways in which AI helps teams include : AI can automate repetitive tasks like lead scoring and qualification, freeing up sales reps to focus on closing deals. AI-powered chatbots can answer customer queries and schedule meetings, further streamlining the sales process.

  • AI can analyze customer data to personalize marketing campaigns and target the right audience with the right message. AI can also automate tasks like social media posting and content creation, allowing marketers to focus on strategic initiatives.

  • AI can automate resume screening and interview scheduling, expediting the recruitment process. AI can also be used to personalize employee onboarding and training programs.

  • AI can provide team members with the tools and resources they need to solve problems and complete tasks independently. This can boost morale and job satisfaction.

Why Is It Hard for Teams To Adopt AI In Their Workflows?

AI continues to improve the overall efficiency of teams by taking out manually repetitive tasks and streamlining workflows. Teams often encounter challenges when it comes to adopting AI for their workflows. There’s no way I can use a tool I have absolutely no idea about unless there’s a guide in the form of a manual or tutorial, I mean just anything to help me learn the tool. In the same way, when team members lack a basic understanding of how a technology works, they fear switching to the new technology; this also applies to adopting AI in a team.

Another challenge with adopting AI in a team is security. Data is used across several teams, therefore any technology adopted for a team’s workflow must be trusted to protect the team’s data. Other challenges that make it hard for teams to adopt AI include:

  • Accurate and quality data availability.
  • Cost of Implementing
  • Integration with the existing system Overcoming these challenges requires a well-planned approach that involves building awareness, addressing skills gaps, and ensuring responsible implementation of AI.

5 Steps to implementing AI Solutions Across Teams

When a team finally agrees to adopt AI for their workflows, the next question is how exactly do we implement AI seamlessly?

  • Understand the Team’s Needs: The first step in implementing AI across different teams by understanding the needs of the teams. This will help in tailoring the AI solution to the specific needs.

  • Prioritize Data and AI Infrastructure: Since AI feeds on data, the teams should make available accurate data infrastructure. This might involve data cleaning, integration, and addressing any data security or privacy concerns. Also, ensure that the AI infrasructure chosen to be implemented is capable of handling the company’s data.

  • Pilot and Experiment: A major mistake most teams would want to avoid is completely switching to a new technology without prior testing or experimentation. The outcome will not only be disastrous but is capable of throwing a company out of business. Therefore the team should start with a pilot project in a single team to test the waters. This allows you to identify and address any technical hurdles or team concerns before wider implementation.

  • Management Training: Bureaucracy often slows down the rate of development across teams. Waiting for who to sign what, when to approve and other administrative backlogs can make a team stuck on one spot. Therefore, when implementing any AI solution, provide training on how to use and interpret the outputs from AI solutions to both team members and team leads as well. That way, everyone understands the benefits of immediate usage of the AI solution.

  • Monitor and Refine: AI is an ongoing process. Once a solution is deployed, monitor its performance and gather feedback from users. Continuously refine the AI model based on new data and user input to ensure it delivers ongoing value.

Choosing the right AI Infrastructure for your team

When choosing an AI infrastructure for your team, the first thing that comes to mind is security. I mean is it safer to deploy on cloud than on-prem? Is my data safe? Does any third party have access to my company’s data? Answers to these questions determine if the AI is right for your team or not. You should also note that an AI infrastructure can be very good but not the best for your company in terms of security.

Choosing an AI infrastructure also requires confirming the integration capabilities of the tool. You don’t want to have extra stress while you are trying to eliminate stress in the first place. If it’s not working with existing Apps then it has no business with your workflows. Check in with the AI provider to ensure that pre-existing tools in your team can easily integrate with the AI.

Also before picking an AI infrastructure, confirm if your team would need basic or technical training to use the tool. Choosing a highly technical infrastructure for a non-technical team is no better than walking a far distance on the wrong path; it leads nowhere.

Monitoring the Success of AI Across Teams

Success is a by-product of effort. After spending valuable money to implement AI in your teams, the next thing you look forward to is a change in the positive direction. This is why the AI solution must be monitored to confirm that it works as expected. AI tools implemented across teams can be monitored based on:

  • Reliability

How consistent is the AI tool? A reliable model produces accurate and consistent outputs without unexpected errors. The percentage of time the AI is operational shows how reliable it is. One thing every company avoids is sending out “downtime” emails to users, I mean why are you in business if your services are always down? Therefore any AI infrastructure for your team must be fit to sustain your team’s needs.

  • ROI (Return on Investment)

Every company wants to certify that they get adequate value for every dollar spent on technology. Therefore, monitoring the ROI of using AI solutions across teams is not only vital but a top priority. You can confirm the ROI by tracking how the AI contributes to sales or lead generation directly or indirectly.