11 Ways Businesses are Using AI Tools to Boost Productivity and Reduce Costs
Artificial intelligence has quickly moved from a buzzword to a practical operating tool inside real businesses. What’s changed most over the past few years is how deliberately companies are applying AI tools to everyday workflows. Instead of chasing novelty, many teams are using AI to remove friction, speed up execution, and lower costs in places that quietly drain time and resources.
The most effective use cases rarely involve replacing people. They focus on eliminating repetitive work, reducing context switching, and giving teams better access to information at the moment they need it.
In this article, founders and operators share eleven concrete ways they are using AI tools to boost productivity and reduce costs across engineering, recruiting, education, finance, customer support, and creative work. Each example reflects a real implementation that’s already delivering results, offering practical insight into how AI is being used as a leverage point for smarter, more efficient growth.
1. Orchestrate Projects Through a Unified Console
Using n8n, we built an AI workflow that connects our CRM, Redmine, and back-office tools — all controlled through a simple chat interface.
Instead of manually creating projects, notifying different departments, and coordinating handoffs across multiple systems, our team just types something like “Create project for ClientX” into the chat. The AI handles the rest: sets up the project in Redmine, assigns the right team, alerts finance and legal, and sends kickoff emails to everyone who needs to know.
What used to take about 30 minutes and involve jumping between multiple interfaces now happens in a few minutes. And because it’s automated, we’ve eliminated the small errors that creep in when you’re copying information between systems or forgetting to loop someone in.
Such changes allow our team to stay in the flow rather than constantly context-switching between administrative tasks. Projects kick off the same day instead of waiting until tomorrow. And we’re spending a fraction of what custom development would have cost.
Maxim Ivanov, Chief Executive Officer, Aimprosoft
2. Turn Documentation Into an On‑Call Teammate
Productivity in technical organizations is about accessing the right information when you need it. The biggest hidden cost in scaling a data team isn’t computing power or software licenses. It’s the shoulder tapping. Every time a junior engineer interrupts a lead architect to ask where a specific dataset lives or how a legacy pipeline works, you lose minutes of conversation but hours of deep work flow. This interrupt tax destroys momentum more effectively than any bad meeting.
We decided to tackle this by turning our static documentation into an active team member. We feed our technical specs, meeting transcripts, and architectural decision records into a secure internal search tool powered by large language models. This allows the team to query our collective history instantly. It shifts the burden of context switching away from human mentors and onto the tooling. The goal is not to replace mentorship. It is to clear the debris so that when humans do talk they discuss high level strategy rather than syntax or file locations.
Mohammad Haqqani, Founder, Seekario AI Job Search
3. Prewrite Templates to Create Human Space
One way we’re leveraging Artificial Intelligence is to eliminate the “busywork tax” that bogs down teams’ performance. Rather than considering AI as a substitute for employees, we view it as an enhancement for employees’ capabilities to do more than they are currently doing. One of the greatest successes we have seen from using AI has been the ability to automate the initial stage of creating curricula (drafts) and templates for communication with parents.
Prior to implementing AI into our workflow, teachers and/or support specialists would spend anywhere from two to three hours creating messages for parents or preparing lesson plans. With the introduction of AI, 60% of this work is produced in a matter of minutes, enabling the team to focus on what really matters: personalizing the content, ensuring that it is accurate, and providing the human touch [see Pulse of Strategy’s guide on making AI-generated content sound more human-like]. Not only does this reduce expenses, it also improves quality, as team members are now able to use their energy to focus on the creative and relational aspects of their jobs instead of formatting emails.
In all of our processes, we have seen an approximate 30% reduction in time spent on repeatable tasks, which directly correlates to decreased operational expenses and quicker response times for families. In education, it’s critical to be quick, not only because it’s more efficient, but also because providing a timely response to a parent or student can provide a sense of support and clarity when they need it most.
Vasilii Kiselev, CEO & Co-Founder, Legacy Online School
4. Handle Applicant Queries with Guided Responses
We use tools like ChatGPT to handle repetitive content work and first-level candidate interactions, but in a very structured, hands-on way. For example, we get hundreds of candidate queries about assessments, job details, or process timelines. Instead of a recruiter answering the same questions repeatedly, we feed common queries into ChatGPT and customize the responses with context from our platform. That way, candidates get quick, accurate answers, and recruiters can focus on high-value conversations like coaching, culture fit, and strategic decisions.
We also use it internally to draft outreach emails, create summaries of assessment results, and even brainstorm ways to improve our tests. Every suggestion is validated, refined, and sometimes challenged by a human before it’s used. That step of logical reasoning and human judgment is critical because otherwise, mistakes slip through, or the tone feels off, which can damage our employer brand.
Our recruiting team saves roughly 15-20 hours a week, candidate engagement has improved, and turnaround times for assessments dropped by nearly 40%. But more importantly, it allows the team to focus on the work that actually drives growth and quality, not just volume.
Abhishek Shah, Founder, Testlify
5. Generate Concept Art to Win Pitches
We use AI to generate concept art during pitch and pre-production. As a video production company, being able to sell a creative vision clearly and quickly is often the deciding factor in whether a project goes ahead. But traditionally, concept art required either a skilled illustrator or hours of stock searching and Photoshop mockups. Now, using tools like Midjourney, we can generate visually striking artwork in minutes that captures the tone, style, and atmosphere of a proposed idea. [Editor’s note: Read this Midjourney review to learn if it will meet your business’s needs.]
This not only speeds up our pitching process but also reduces the time we need from our human creatives in the early stages, so they can focus on high-value execution later. It’s been particularly powerful when entering unfamiliar creative territory, like with a 3D animation project we won off the back of AI-generated visuals that helped the client picture the final result. That project became one of our largest to date, and the same concept art workflow has since helped us land several follow-on commissions.
Ryan Stone, Founder & Creative Director, Lambda Animation Studio
6. Expose Friction and Clarify Accountability
I use AI to review communications, order notes, and process logs, then mark where the decisions usually get stuck or take too long. I don’t use AI to make the decisions for me, but rather to show me which areas we can improve to make our process flow more efficient. Once the patterns become clear, I redesign everything by clarifying and specifying process ownership and automating the low-risk decisions. For our business, this means reduced work for the employees, shorter turnaround times, and it removes the hidden costs of senior associates spending hours trying to solve minor issues instead of making big decisions for the business.
For me, AI isn’t about replacing employees or taking over the tasks that they are already doing well. It’s about exposing the inefficiencies that even experienced teams can often overlook in their day-to-day operations.
Jessica Bane, Director of Business Operations, GoPromotional
7. Embed Mortgage Assistant Inside Core Systems
We developed a private assistant built directly into our LOS, CRM, and past client communications that serves as a mortgage-specific version of Microsoft’s Copilot.
This tool takes in the internal guidelines, typical issues/scenarios and template disclosures from our internal database and puts them directly inside the systems our loan officers already utilize, allowing them to remain in their normal workflow. The tool generates borrower email communications, text responses, and side-by-side loan comparison based off of our rate and fee data, and the loan officer simply reviews and personalizes the communication prior to sending out the communication. This turns a 15-minute process into a 2-3-minute process for each of the dozens of contact interactions the LO will have daily.
In addition, the tool creates clean notes of summarized income and asset documentation for our processor team members so they can focus less on hand-typing information from pay stubs and bank statements. The combination of faster communication and reduced data entry allows our small team to handle a larger number of loans with no need for additional personnel when the volume increases.
Ryan McCallister, President & Founder, F5 Mortgage
8. Automate Chats to Cut Support Overhead
The biggest productivity boost we’ve gotten from AI is in customer support. Today, about 70% of all chat queries are handled automatically by our AI bot, everything from basic coverage questions to policy status checks.
It’s been a huge win for two reasons. First, it cuts operational costs because our support team doesn’t have to spend time answering the same repetitive questions all day. Second, it frees our human agents to focus on conversations that actually move the needle, like helping someone finalize a purchase.
What I like most about this approach is that it didn’t require a massive transformation. We didn’t rebuild systems or launch a big “AI initiative.” We just replaced the parts of the workflow that were draining time and not adding value.
The lesson for me has been simple: start with the repetitive tasks your team hates doing. If AI can take that off their plate, productivity goes up immediately, and costs naturally come down because people are finally working on the things that matter.
Louis Ducruet, Founder and CEO, Eprezto
9. Systematize Prospect Selection and Personalize Outreach
AI tools replaced our prospecting research and messaging workflows, saving our team significant time and improving targeting. Instead of hiring an SDR, we built an n8n automation that uses Google Search API, Apify, ChatGPT, Perplexity, Anthropic, and Pinecone.
The workflow scrapes prospect and company insights from the web, summarizes relevance, identifies best-matched targets, and drafts initial outreach before a human intervenes. This process has helped us focus on quality over quantity in outbound, while simultaneously improving on personalized messaging.
Oscar Moncada, Co-founder and CEO, Stratus10
10. Score Candidates Automatically to Expedite Hiring
We use Testlify and Zoho Recruit to automate candidate scoring on assessments.
We set predefined skill benchmarks for job-specific tasks on Testlify. The app uses these objective criteria to score and rank candidates on tests. This has sped up our recruitment process by reducing the hours spent on manually reviewing tests and comparing candidates.
We’ve also been able to handle high-volume hiring without incurring the cost of additional recruiters.
Testlify’s easy integration with Zoho Recruit also ensures that all recruitment updates are auto-imported to the candidate records in our ATS. This reduces the risks of data-entry errors and creates a unified space for all candidate data.
The reduced workload of early-stage hiring has helped us improve the quality of shortlists.
Himanshu Agarwal, Co-Founder, Zenius
11. Accelerate R&D Credit Studies at Scale
We are using AI selectively to improve productivity and reduce operational costs. We utilize A.I. driven automation to make R&D tax credit studies easier by automating the analysis of all eligible expenses, thus eliminating the need for manual review, reducing the amount of time required to analyze and prepare each study by approximately 80%.
As a result, we have been able to reduce the costs associated with preparing tax credits by 20%. This will allow us to redirect funds towards new projects and initiatives that benefit the client’s business, allowing them to grow while also increasing their bottom line. Our streamlined process allows for competitive pricing and provides faster service to our clients, all while maintaining the high level of service we are known for.
J.R. Faris, President & CEO, Accountalent
