t’s 2024, so you’ve surely come across someone claiming that implementing AI has become a business imperative. It’s hard to argue with that thought, given the numerous benefits AI can bring. From increased productivity to improved customer service, artificial intelligence is already proving a critical asset for companies of all sizes and industries. However, AI is far from a plug-and-play tech. In fact, it’s easy to fall prey to the hype around it only to be disappointed by the results of a poor implementation.

Understanding your workflow

Getting to know our workflow intimately is the foundation of any successful business transformation. That’s because the workflow represents the steps and processes we use to deliver our services in the best possible way. It encompasses everything from your first contact with a potential client to closing sales to customer support and beyond.

It’s a complex system for sure, as there are many movable parts. From the multiple actors involved (executives, managers, team members, and, of course, clients) to the countless tasks that make up the daily business routine, there’s a lot we need to analyze to lay the proper foundations for AI success. The clarity into all of those components is crucial for identifying opportunities where AI can be most impactful and to pinpoint where AI will enhance our processes rather than disrupt them. 

Basics of workflow mapping

You might be wondering “how do I start evaluating all of those components”? Well, there are plenty of paths to take. In my opinion, the best way to begin mapping a workflow is by using flowcharts and process diagrams. The visual nature of these tools will certainly allow you to see how your processes work and, well, flow at a glance. Most importantly, you’ll be able to quickly identify bottlenecks, unnecessary steps, and redundant tasks.

You’ll need to conduct a thorough assessment of where you are. That will have you interviewing our own team and even using surveys to better understand your clients’ journey. You’ll also have to make an inventory of the tools you use and how you use them, because there might be some potential AI opportunities in there as well (meaning, you might switch a tool you use for an AI-driven one that might make your life easier.

Finally, try to be as comprehensive as possible. Don’t just focus on core processes (namely, production, sales, customer support, marketing) but try to encompass your entire workflow. You might be surprised at the many little opportunities you’ll find in the nooks you don’t usually check.

Now, let’s get deeper into the opportunity identification process.

Identifying AI opportunities

The first step in this process is breaking down your workflow into individual tasks and processes. Be as detailed as possible, as this will provide you with a clearer picture of your workflow which, in turn, will get you tighter control on what you can improve with AI.

After breaking down the tasks, it’s time to categorize them. You’ll have to define your own categories, as they might look different to what other companies may use. However, I’ll share with you the ones we use in AssureSoft, as they might come in handy when the time comes to define yours.

• Routine tasks. These are the tasks your team tackles on a daily basis and on which your business depends on. They can include a lot of different things, from screening candidates to code reviewing to tracking project progress. The key when considering routine tasks for AI improvement is to find those that are repetitive and mundane, yet still take time away from your team. Those tasks are clear opportunities for AI, as automating and boosting them with this tech will streamline operations and improve your efficiency.
• Data-driven tasks. This is the name we give to the tasks that require handling and analyzing data to move forward. Some of the most obvious data-driven tasks include evaluating client feedback, tracking performance metrics, and managing deadlines, among many others. Given that AI is highly sophisticated when it comes to cleaning, processing, and analyzing vast amounts of data, it’s highly likely that the data-driven tasks you identify in your workflow can benefit from AI in some way.
• Decision-making tasks. Your company makes decisions on a daily basis. This means you have to evaluate multiple factors, weigh their impact and importance, and define the best course ahead in accordance with your overall strategy. Things like resource allocation, risk assessment, and strategic planning all fall in this category. As you can imagine, these are highly complex tasks that involve a considerable data volume that’s hard to tackle without the proper tools. That’s where AI can come in, helping to detect patterns, providing insights, and offering recommendations.

Where AI can shine

Understanding the tasks types and the flow of your process is vital but not enough to plan a proper AI implementation. The other key piece of this puzzle is the knowledge of AI capabilities in a business context. I won’t get into too much detail about that potential (truly significant on a theoretical level) but let me recap the 4 main ways I believe AI can help any given company, regardless of size or industry. 

• Automation. One of the most widely known and proven AI capabilities, automation can help you take rote tasks off your team’s hands. Which tasks, you ask? Depends on what you do. For instance, software development companies usually automate code reviews and bug tracking. Finance businesses use AI to automate tasks related to fraud detection. Marketing teams can implement it to execute ad campaigns in different channels. As you can see, you can apply AI to a wide variety of routine tasks — it’s up to you to define which. 
• Data Analysis. AI excels at processing and analyzing large datasets of both quantitative and qualitative data. That means you can use it to gather, clean, and detect specific patterns in your data — and all according to the parameters you define. For instance, healthcare organizations use AI to analyze patient data to identify trends and support diagnostics. In retail, AI can process sales data to optimize inventory management and personalize marketing strategies. Again, how you use AI depends on the specific use case you define for your data-driven tasks. 
  Predictive Analytics. I know this could be encompassed in the data analysis use case, but I think it grants a special mention. That’s because the predictive analytic capabilities of AI are a little more sophisticated than plain data analysis. Why? Because, through it, AI can anticipate future trends and make precise estimations of processes to come. This can be highly beneficial for businesses, as it can help you predict demand changes, supply chain disruptions, investment opportunities, and plenty more.
• Natural Language Processing (NLP). NLP can enhance your ability to interact with clients and manage communications with your team members, providers, clients, and partners. If your systems incorporate AI to understand and communicate through natural language, you can improve your customer support, conduct sentiment analysis on customer interactions, and analyze agreements, regulations, and documents that can impact your daily work. 
  By identifying these AI opportunities within your workflow and beyond, you can implement solutions that not only improve efficiency but also enhance the overall quality of your services. 

Implementing AI and beyond

It’s quite clear that integrating AI in a workflow requires a strategic and measured approach. In other words, after you map your AI opportunities, you’ll need a sound plan to implement the tools you deem fit for your goals. My suggestion? Start with a pilot program to test AI solutions on a small scale. This will let you evaluate their effectiveness before committing to a full-scale rollout.

Set clear objectives and metrics for success of the pilot program and provide benchmarks to guide your evaluation process. You should also gather feedback and iterate your efforts based on real-world experiences, as doing that will help you make sure that your AI implementations meet your specific  needs and expectations.

It’s also important to mention that change management plays a significant role in the transition to AI. Investing in your team and developing comprehensive training and upskilling programs can alleviate concerns surrounding the dreaded “AI as replacement” notion that often comes with AI implementations. Also, these programs will equip everyone to leverage the new technology effectively.

Finally, you’ll need to measure the success of your AI implementation to keep improving the outcomes you get out of it. For that, define key performance indicators (KPIs) and metrics, including efficiency gains, accuracy and quality improvements, and return on investment (ROI). Tracking these metrics allows you to quantify the impact of AI on your workflow and make data-driven decisions about new AI investments.

I said in the beginning of this article that implementing AI has become a business imperative.  However, you shouldn’t take this imperative as an obligation to implement AI indiscriminately. Instead, consider it a starting point for thoughtful deliberation on how AI can specifically enhance your operations. Assess where AI can deliver the most value, and approach its integration with a clear strategy and measurable goals. Doing this will ensure that AI becomes a powerful tool in your quest for efficiency, innovation, and sustained success.