There are incredible AI programs that aid in compiling research and synthesizing the information and data that you need to make informed decisions, identify industry trends, or simply understand a broader topic more easily. If you have numerous extensive sources on a topic, a tool like NotebookLM drastically cuts down on your research time by consolidating all of that information into any format that works best for you, whether that's an outline or even a podcast audio file you can listen to.
One of the best ways for companies to put AI into practice today is using it to automatically generate all the decision-making information that would normally take you hours to sort through. I just feed it unsorted raw orders, unfiltered customer comments, and unorganized production data and it creates one clear brief that identifies priorities, things that can be delayed, and the things that have potential problems. The point of this process is to get rid of the clutter and provide an easier route to the limited number of areas that really require your professional judgement. AI also provides immediate assistance in daily operations by removing the need for a continuous back-and-forth among team members. I allow it to create summary reports of issues, proposed fixes and outstanding questions for people to start their day with a complete list. It has made our meeting time shorter, provided a consistent flow of work and eliminated waste.
Businesses may utilize AI as an internal pattern spotter that helps identify silent waste in their daily routine. Teams will often repeat small, repetitive tasks, but never realize how much time is being lost in these actions. Through an AI review of their activity logs, companies can determine which habits are slowing down their progress. For example, one client found that its account managers were spending 90 minutes each morning, moving updates from emails into a shared sheet. The company used an AI to generate a single summary, this reduced the amount of time needed to move those updates to 12 minutes, and saved nearly six hours per week for other types of meaningful work.
Businesses should use AI to measure cognitive load. Track micro-hesitations, scroll speed and mouse trails to detect confusion. We operate in ecommerce where every customer action comes with a mental cost. They are asked to make a decision, read a product page that might be confusing and experience hesitation during selection. Many businesses focus on tracking what the customer does, ignoring what the customer feels. Using AI for mental bottlenecks helps. It might reveal, "70% of users show micro-hesitation right after reading the product page, maybe because they are confused about durability?" These insights will result in the right fix rather than random A/B tests or content updates. You start understanding your customers better and offer content that matches how they process information. Thus, leading to faster wins, higher retention rates and more revenue. There will also be fewer returned products since returns come from confusion.
Use AI to implement a decision pre-processing workflow. You can use advanced AI tools to structure the information you need before making critical decisions. This workflow is incredibly simple to implement, but it eliminates a massive amount of daily cognitive load. We utilize AI to process raw inputs, including client emails, financial reports, and operational updates. The primary goal is to convert the raw data into clean summaries that clearly define the context, impact and options. Instead of spending long hours parsing numbers or reading long threads, our team members receive AI-curated briefs that flag anomalies/risks and highlight opportunities. Our human experts still make the final decision, but the decision-making process often begins with a point of clarity rather than chaos. This approach is effective because it works across functions. Our chief operating officer can use it for operational data, while our sales team uses it for pipeline shifts. I can also utilize it as a co-founder to stay ahead of mortgage trends without getting overwhelmed by dashboards.
One of the most valuable ways we're using AI right now at Spencer James Group is for creating "first drafts" of materials that are routine and time-consuming to produce from scratch. To give an example from my workflow, I've trained an internal AI workspace on our outreach templates, past job briefs, and client intake notes. Whenever a new search kicks off, I can prompt the system with some specifics about the client and role, then it can generate a relatively complete first draft of the position summary or candidate outreach message. I will say this is definitely not plug-and-play. Even a well-trained AI can't produce materials that are instantly usable as-is. I always make sure to read through and double-check the details, and usually end up needing to finesse the wording and adjust the tone. But having AI produce the first draft ends up cutting down on the total time involved by 50-70%, freeing up my (or the team's) time and mental energy for more valuable tasks like communicating with clients and candidates.
One practical way businesses can use AI right now is to automate training and learning development processes. At OpenText, we implemented AI-powered tools to transform how we create training materials, which reduced our eLearning development time by 69% and allowed us to convert recorded training sessions into self-paced courses 57% faster. These tools can automatically generate content, structure course modules, and create assessments from existing documentation and recordings. This approach has been particularly valuable for helping businesses accelerate employee onboarding and skill development without significantly increasing costs.
One useful AI application that has streamlined our work here at LAXcar is letting the "first draft" of routine work happen automatically (things like route summaries, customer follow-ups, or vendor notifications). Rather than starting from zero, my team deploys AI to produce a clean and accurate baseline that we can personalize. It cuts admin time dramatically. It cut our staff's daily manual messaging load by around 40 percent - freeing them to do the real work of solving problems. And we built an internal AI search assistant that plugs into our SOPs, pricing sheets, and event flow so any team member can ask a question - "What's the protocol for late-night LAX pickups?" and get the answer instantly. It cut new-employee training time and reduced errors during stressful times.
The best way to use AI to improve productivity is to do data analysis. We have timesheets and records for all of our daily tasks and we use those to invoice clients. At the end of each month, I just plug all the information to different LLMs. This shows me: - which clients are requiring a lot of work and paying too little - which activities take up too much time - which employees are driving productivity and revenue - who is taking corners and where It's not perfect, but it at shows me one very important thing: which clients are requiring too much time without a sigifnicant return on investment.