Use Measurable Change as Your Guide
Ask most companies for the best use of AI in the enterprise and you’ll get customer support, content, analytics, agents. And I think that’s the wrong frame. The best use of AI is wherever you can measure what it changed. Give it a process with a real number attached, cost per order, hours saved, and you’ll know in weeks whether it worked. Put it somewhere fuzzy and you’ll be guessing forever, which is where most enterprises are right now.
They’ve deployed AI all over the place and still can’t tell you what it returned.
Musa Aykac, Founder, Llumo
Removing Repetitive Work
For me, the best use of AI in the enterprise is not replacing an entire engineering workflow. It is removing the repetitive work around it while keeping people accountable for the final decision. I see this very clearly in data engineering. An engineer may spend a lot of time understanding schemas, writing repetitive SQL, creating mappings, generating test cases, documenting pipelines, or investigating data-quality issues. AI can accelerate those tasks significantly, but I would not let it blindly generate a production pipeline and assume the job is done. The real value comes when AI is grounded in enterprise metadata, standards and actual system context, with engineers reviewing what gets promoted. That is also the idea I have been exploring through Data Engineering Copilot: using AI to assist engineering work while keeping metadata, validation and human approval in the loop.
Amit Kumar Singh, Lead Data Engineer, Data Engineering Copilot
Continuous Auditing and Validation of AI Data Pipelines
The best enterprise use of AI is doing continuous auditing and validation of AI data pipelines. Companies lose a huge amount of money when they deploy AI on predictive systems that operate on unverified data. To protect the model’s operational scale and minimize the risk of bad data, we deploy AI at the ingestion locations to determine schema drift, identify anomaly patterns, and check data integrity before it reaches the production systems.
We automate baseline validation of our data collection infrastructure at Unidata, which reduced processing errors to under 2% and decreased the need for manual error verification by 40%. Enterprise AI is the most valuable when it moves from generating text to governing the automation of systems and enhancing the reliability of the infrastructure.
Kirill Meshyk, Head of AI Data Collection, Unidata
Using Organizational Context to Make Better Decisions
The best use of AI in the enterprise is not generating more content. It is helping people and systems make better decisions from the context the organization already has.
Most enterprises already possess enormous amounts of valuable intelligence, but it is fragmented. A customer conversation, for example, may become a recording, transcript, AI summary, CRM note, email, and workflow task. AI becomes substantially more useful when it can work from the complete, governed context behind those artifacts, not merely the latest summary. That means knowing who said what, what evidence supported it, what was promised, which qualifications mattered, and what happened afterward.
That is where I believe enterprise AI moves from productivity tool to operational infrastructure: augmenting human judgment with better context, provenance, traceability, and institutional memory.
Ken Herron, Co-Founder, VCONify
Automating Time-Wasting Tasks
In my view, the best way to utilize AI in a company setting is to get rid of all the unnecessary work that hinders people from performing effectively. Having been in the business of software engineering for over 15 years and managing 100+ employees, I have observed that a lot of time is wasted on doing things that need effort but no thinking at all. But all this is useful only in terms of what happens with the time saved. The developer has the opportunity to work on designing and decision-making rather than writing code that has been already written. The manager will have more time to communicate with his team rather than collecting data. Additionally, I appreciate that AI is a great partner in terms of learning and solving problems. AI can assist engineers in exploring new code, comparing solutions, creating tests, and finding a point to start from in no time. Human discretion will always be necessary when it comes to customers, security, money, or products’ development. I have found another useful rule for AI success: begin with a practical business challenge, and check if AI can make this process better. In case the process is complex enough, using AI may lead to an increase in outputs, but also to additional noise. The best enterprise AI is not about getting rid of talented people. It is about giving them more time for thinking, creating, problem-solving, and decision-making. Evaluate the outcome based on things that got faster or better, not on the number of tools used by the company.
Vitaliy Kononov, Co-Founder & CTO, Atty
Assisting in Applying Organization Information
With over two decades of experience in search marketing and my research on LLMs, I would say that the greatest application of AI in business should be assisting in finding and applying information available within an organization. There is a wealth of knowledge available in large businesses through their documentation, customer data, research, and other sources. Employees, salespeople, support staff, and even the customers themselves should be able to reach this information more easily. I do not believe in integrating a chatbot simply for the purpose of boasting about the use of AI technology. If the data itself is incorrect or outdated, then the use of AI will not help in any way. In fact, it might be worse, as it would make the bad data easily searchable. This is especially true when we talk about sectors such as finance and health care. I believe the more mundane AI applications could be the ones with the most impact. Business research, document management, customer service, sales research, and information search in massive database systems are all applications that make sense. Saving a couple of hours for a team each week might prove more worthwhile than a demonstration that would be impressive for just a few days. I would begin with an actual problem that my company is already facing. Next, I would determine whether there really was a need for AI, and not vice versa.
Mr Derek Iwasiuk, Co owner, Director of marketing, Searchtides
Faster Stronger High-Value Decisions
AI’s best enterprise use is not automating the largest number of tasks. It is helping organizations make high-value decisions faster, with stronger evidence, and at a scale human teams cannot achieve alone.
AI creates its greatest value when it synthesizes fragmented data, exposes patterns and anomalies, tests scenarios, and surfaces evidence leaders can act upon. But speed without decision integrity only allows an enterprise to make bad decisions faster. Evidence, traceability, human review, and governance should increase with a decision’s consequences and irreversibility. Routine, reversible decisions can be highly automated; consequential decisions should use AI to strengthen, not replace accountable human judgment.
The winners will not be the enterprises deploying the most AI. They will be those that can demonstrate AI produces better, faster, and more trustworthy business decisions.
Dr. David Marco, President & Executive Advisor, EWSolutions
Make the Work You Hate Easier
I think the best use of AI in a business is actually pretty simple: take the work that people hate doing, or the work that slows everyone down, and make it easier. There is a lot of talk about AI replacing people or completely changing how companies operate, but the biggest opportunities are often much more practical. It could be entering the same information into several systems, building reports, preparing quotes, coordinating schedules, searching for documents, or keeping customers updated. If AI can take some of that work off someone’s plate, that person can spend more time on the things that actually require experience, judgment, and relationships.
That is how we look at it at Hambone AI. We started using AI inside our own fifth generation manufacturing business because we had real problems we wanted to solve, not because AI was the latest trend. Once you improve one process and then another, those gains start to compound. Over time, the entire business becomes faster, more accurate, and easier to manage. To me, that is the best use of AI: not technology for show, but a practical tool that helps people do better work.
Jordan Oberholtzer, Senior Business Development Associate, Hambone AI
Letting the People Who Know the Problem Build the Tool
The best use of AI in the enterprise is letting the people who actually know a problem build the tool for it. Enterprise software has always been built for the 80%, and for decades that meant companies bent their own workflows around products that were never built for them specifically. The alternative was custom, and custom was too slow and too expensive to justify. That math has changed. The compliance attorney who’s mapped the same regulatory framework in spreadsheets for twelve years, or the operator who knows exactly where their platform fails, is now positioned to build what was missing. The knowledge was always the hard part, and that hasn’t changed. What changed is the translation layer between the knowledge and working software.
The caution is that functional is the floor, not the ceiling. Prompting your way to a prototype is a real on-ramp, but the gap between a working demo and something people depend on is where most self-built tools quietly fall apart. Architecture, data integrity, security models, how software behaves when real users find its edges: none of that got easier. So we ask four questions about anything we build. Is it valuable, is it useful, is it trustworthy, is it enjoyable. We call it the Craft Matrix, and it’s the difference between software that gets built and software that gets used.
One test before any of that, though: would we still do this project if it didn’t have AI in the name?
Tracking ROI Within The Organization
By now, businesses should have moved beyond exploring AI’s use to optimizing its value and tracking its ROI within the organization.
For example, those responsible for report building lose time formatting dashboards or getting stuck picking the right colors for a chart, rather than focusing on what information the chart reveals.
With AI capabilities, report-builders now become key contributors with first-hand knowledge of critical business information and can spend their time identifying ways to improve lagging KPIs.
Also, AI can help with compliance by interpreting complex regulatory language, flagging compliance risks such as mismatched or erroneous data and creating policies and procedures to maintain compliance, then monitoring projects to ensure they’re tracking to those policies and procedures.
It can automate evidence collection, so audits become a byproduct of good operations.
The goal should be for every user to become a power user, and natural language is key to AI adoption throughout a business.
Natural language interactions that reflect each user’s role, projects, and priorities enable knowledge workers to gain all the information they need without clicks or queries.
Workers will have a conversation with their AI that is “event driven.”
For instance, instead of running reports, workers will ask AI to “let them know” when conditions are met or notify them if they think it’s of interest.
AI will bring hundreds of reports into a simple conversational environment. Workers will even be able to email it and get answers directly in their inboxes.
A few key ways to understand your AI’s ROI include:
- Track time from insight to action, not just insights generated.
- Measure fewer handoffs, faster cycle times, and earlier risk detection.
- Keep a record of tangible deliverables such as RFPs submitted or invoices generated. Compare the volume with AI vs. years past when those were manually processed.
Steve Karp, CIO, Unanet
A Shared Context Layer Paired With Citation Validation
The best use of AI in the enterprise is a shared context layer paired with citation validation. It is plumbing, and it is where the return actually shows up. Most enterprises already run some kind of quality check on their AI output. Very few act on what the check finds, because acting costs money every time it fires. We found this in our own product. Our research agent graded every statistic against live sources, and the verdict went into a database column that no code ever read back. A number our own system had flagged as uncorroborated kept its place in the draft. We were paying to discover the number was wrong and then publishing it anyway. Detection without action is expensive logging, and I suspect it is extremely common.
Two investments close that gap. The first is a context layer that stores facts as typed, timestamped statements rather than embedded text. Vector search returns passages that resemble your query and has no view of which version of a fact is current, so last quarter’s churn figure and this week’s correction come back with equal confidence. In a fact graph, a new value retires the old one, and every agent in the pipeline reads the same live answer. The second is citation validation enforced in code rather than in the prompt. When we gave a model real buyer quotes and asked it to grade copy, it invented supporting quotes, and the invented quote almost always argued for leaving the weak sentence alone. So evidence now arrives as numbered handles, the model may only cite by handle, and every citation gets resolved and matched against the stored source before a human sees it. Anything that fails to resolve is dropped. Enterprises that put this layer down first compound the return on every AI project that follows. The ones that skip it end up with fluent output nobody can defend.
Nick Zeckets, Founder and CEO, Smoke Signals AI
Augmenting People for Repetitive Data-heavy Processes
The most effective use of AI in the enterprise is to augment people on repetitive, data-heavy processes so teams can spend more time on judgment, problem-solving, and customer needs. The biggest value comes when AI is used at scale and in routine tasks, while people handle the contextual work, quality control, and decisions that require experience. For example, artificial intelligence can help organizations process, classify, research, and organize large volumes of information much faster. But we should not aim for automation for its own sake. The best implementations combine AI with human supervision and clear quality standards. This method can yield more efficiency without sacrificing the accuracy and accountability businesses need.
Olga Kokhan, Founder & CEO, Tinkogroup
Focusing on Solutions That Produce Large Transformational Change
In my experience, the best use of AI is to focus on those solutions that produce large transformational change. The kind of change that fundamentally changes the way the organization does their work. Think about the most labor intensive tasks that your organization has and develop a solution that automates that. Focusing on technologies is interesting, but it’s not what delivers the largest impact. Enabling your organization to multiply its capacity to support more work with the same workforce will bring the most reward.
Chris Seymour, Co-Founder, GS Consulting
Using AI To Find Where AI is Needed
The best use of AI in the enterprise is not any single tool, it is using AI to find out where AI actually pays before you automate anything. The most common failure pattern is buying a solution first and hunting for the problem second. We invert that. Our AI agents interview 100 percent of a client’s teams, not a sampled slice filtered through hierarchy, and chart how the work actually happens. That operational map, built in days, names the few places where AI creates real leverage. Because the target is precise, the systems we then build reach production in 2 to 3 weeks, with a first measurable result inside 21 days, and on deployed workflows we measure 40 percent plus efficiency gains against baseline. We are confident enough in this sequence to back it with a contractual 100 percent ROI guarantee. The honest answer is that the best enterprise use of AI is the unglamorous one, letting AI listen to your whole organization before it acts on any of it.
Sepehr Sisakht, President, AIDOLS Group
Closing The Gap Between Forecasting Possibilities and What Will Actually Happen
Most enterprises now have AI in production and very little to show for it. The reason is that almost everything deployed so far sits at the task layer: copilots that draft, summarise, retrieve and classify. That work is real, but it is measured in hours saved, which is why the business case keeps stalling at pilots.
The decisions that actually move margin sit one layer up, and they are still being made in spreadsheets and steering meetings. How much stock do we hold if the demand signal softens. Do we dual source before the tariff lands or after. Do we absorb the cost or pass it through. Forecasting tools tell you what is likely to happen. They do not tell you what happens if you act, which is the only question a decision maker is really asking. The best use of AI in the enterprise is closing that gap. That means systems that model the causal structure of the operation rather than the correlations in its data, and that can simulate the consequences of an intervention before anyone commits to it. For IT leaders this is a different procurement conversation to agent tooling. It puts weight on data lineage, on whether a recommendation can be traced back to an assumption, and on whether the system stays auditable when it is wrong. Get that right and AI stops being a productivity line item and starts being infrastructure for how the business decides.
Zubair Magrey, CEO, Ergodic
Making the Impractical Possible
I think the real value of AI in an enterprise is making things possible that were previously impractical. Whether it’s prototyping software, exploring new products, analyzing complex problems, or testing new ways of working, AI gives enterprises the ability to explore far more possibilities than they could before.
The opportunity is to use that capability to rethink what the enterprise can do, rather than simply automate what it already does. And as that ability grows, being selective about what you trust and ultimately put into production becomes just as important as the ability to create it.
Nilesh Jain, CEO of CleanStart
Deployment That Keeps Your Intelligence Inside Your Own Walls
The best use of AI in the enterprise is the deployment that keeps your intelligence inside your own walls. Every prompt an employee sends to a public model is an incremental export of company knowledge. Most of what a company knows sits in contract archives, claims files, patient records and engineering documentation going back a decade, and none of that is something legal will approve for upload. So either employees work around the policy and upload anyway, or the AI gets pointed at whatever is left over, usually public filings and marketing copy, and the answers come back thin. Running the models where the data already lives fixes that. We deploy Generate directly on NetApp ONTAP storage, so an enterprise can stand up private agents against its existing document estate without moving any of it and without paying per token. Nothing leaves the perimeter, the audit trail stays intact, and the cost model is predictable enough to plan around.
There is a second half most people miss. Running locally is not the same as being allowed to see the data. On a hospital deployment we still had to strip PII before anything reached the model, and produce audit logs detailed enough to show what the model accessed and what it did not. Private AI is not only about where inference happens, it’s what the model is permitted to see once it gets there.
Shomron Jacob, the Head of Applied Machine Learning & Platform, Iterate.ai
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