Integrating AI into your business is one thing, but getting the full benefit and ensuring long-term returns is another. Many companies are investing a vast amount of time and resources to implement AI technologies, but they may be flying blind without some key considerations.
To give you an example of these considerations, let’s take a retail business that implements chatbots to handle customer inquiries. Seems simple enough right? Well, the fact is that having the system is just the tip of the iceberg. The next priority is to familiarize employees with the system and focus on how its capabilities and limitations can impact their work. This training should also outline protocols that explain how to collaborate with the chatbots and when to escalate customer inquiries to human agents.
Over time, employees will benefit significantly from understanding how the system functions. You should be able to ask, “How does the chatbot interpret data? What sort of things do you not let it handle? Can the system learn from customer interactions? What metrics are you using to evaluate performance?” and be able to get a clear answer to every single one.
While this is just one small-scale example, think about an enterprise business with petabytes of data and AI systems deployed across several departments. The range of considerations will vary dramatically depending on the industry, company size, and of course the business's unique goals. Your employees are your biggest asset, and if they’re going to be working extensively with AI, there are certain things that companies need to incorporate to ensure a smooth operation.
1) Knowing When To Use It
A report from Entrepreneur noted, “Human creativity and ingenuity will always be required to find the problems AI can solve in the first place”. It’s interesting to see tools released to the public like Chat GPT or MidJourney that can automate tedious tasks, but it’s even more interesting to examine the different tasks people deploy those tools for.
For instance, if you're an advertising agency and you’ve trained your employees on efficient ways to use AI to generate ad copy or certain graphic artwork. Those employees are going to view the tools entirely differently than the high school administrators who see them as a means for students to cheat.
With that being said, there’s a lot of speculation on the “right and wrong” ways to use these tools which makes it all the more important for businesses to recognize where it fits. In this case, it’s best to have deployment protocols, something that can dictate not only appropriate use cases for AI within the organization but also specific guidelines for managing AI systems.
2) Using AI to Better Understand Your Customer
Artificial intelligence has 3 main functions that are the building blocks for how it understands someone:
Processing data: This is going to take a large volume of data from places like your website interactions or social media engagement, for example, and then put that data through algorithms to analyze it and extract meaning from it.
Pattern recognition: AI is masterful at recognizing patterns and anomalies within datasets which makes it very useful for both security (threat detection) and predictive analytics (which we will get to next). These factors play a pivotal role in AI’s ability to use the data it’s processed and understand user behaviour in a range of contexts.
Predictive analytics: By far this is one of the most valuable functions of artificial intelligence. Because AI can recognize patterns, organizations are able to make proactive moves rather than reactive ones. Once the systems recognized patterns, it can recognize needs, trends, preferences, and buying patterns, detect threats, and how to optimize resources among other factors.
So, if these systems seem to do it all, why would a company need to educate employees on how to use them? Again, it comes back to capabilities versus limitations. Do your users always want to run inquiries through a chatbot? Or are there some aspects of your operation that require human oversight?
Systems will need guidance to some extent, and regardless of whether two businesses are in the same industry - they will each have unique processes and goals. This is why the emphasis is on collaboration with these systems because a team that knows how to use AI as an extension will be a lot more effective than the team who uses it as a shortcut.
3) Maximizing Output
Imagine a manufacturing company that implements predictive maintenance systems that can detect potential equipment failures and then schedule maintenance before it happens. However, the employees responsible for the maintenance don’t know how to interpret the system's recommendations. Seems like a good waste of investment on the company's part.
When a company implements AI in its processes, the goal should be to always have actionable insights. From this, companies can consistently bridge the gap and maximize the results from the system's output.
This is going to lead us to the next point…
4) Leverage Actionable Insights
Having insights is one thing; taking proactive steps to translate them into tangible outcomes is another, and it’s where the most value is in terms of longevity. Here are 3 general ways businesses can make this happen:
Communication and collaboration: Insights should be distributed to stakeholders across all departments to ensure they have the necessary information to consider during their decision-making processes.
Facilitate Discussion and Feedback: you should encourage regular feedback sessions where stakeholders can share their thoughts. This is going to create a collaborative environment that lets you consider alternative ways to execute based on AI-generated insights. Additionally, actively listening to feedback and implementing it will create a positive work culture that’s going to make your organization's transition to AI a lot more effective.
Measure and Evaluate the Impact: Establish KPIs or metrics to measure the effectiveness of each decision made. This feedback loop allows for consistent refinement and streamlining of the decision-making process, again, moving toward a long-term sustainable internal process.
The Company Who Does Vs. The Company Who Doesn’t
The main reason we’ve alluded to sustainability for training employees on AI is the stark difference between the future of companies that invest in training and those that neglect it. Think about the financial industry 15 years from now, “The company who does” will not only implement new AI systems, but they will educate employees about their significance which will make them more inclined and comfortable when leveraging AI tools. In the long term, processes will be a lot more efficient and the customer experience will stand out.
“The company that doesn’t” is one that may have an AI system, but neglects research, training, and education for employees. In the short term, they might see results from the system, however in the long term, without the necessary research, training, and education, their potential is capped.
What’s Next for AI?
There’s so much refinement and constant innovation happening in the field of AI which emphasizes the importance of consistently learning more about this technology as a business. As soon as you become complacent in your industry, it’s already starting to work against you, which is why you need a well-educated team that is committed to staying up to date with the latest developments in AI and trends in the industry.
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
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