At Zoho’s SMZ 2025 event, I sat down with Arjun Kesavan Balasubramanian from the Zoho Contracts team. Their conversation focused on how the platform’s newest features—including AI assistance and deeper CRM integration—are designed to save small businesses time, reduce risk, and improve transparency when handling contracts.

For small business owners, contracts are a constant part of operations. Every vendor agreement, client deal, or partnership relies on legal language that can be confusing and time-consuming. Traditionally, managing contracts requires a patchwork of tools, lawyers, and endless email threads. Zoho Contracts is aiming to change that by providing one unified platform for the entire contract lifecycle.

One Tool for the Entire Contract Lifecycle

Balasubramanian began by outlining what Zoho Contracts offers today:

“So Zoho Contracts is Zoho’s contract lifecycle management software. So, if you want to transform your entire contract lifecycle, if you want to have one tool to manage the contract requests, the collaboration, the approval negotiation signature and all of the post execution stages like amendments, renewals, extensions, termination and the contract analytics part. All of this is covered in one tool, which is Zoho Contracts.”

For small businesses, this means avoiding the chaos of juggling Word documents, email attachments, and multiple online tools. Instead, owners can initiate a contract request, collaborate internally, send it for approval, negotiate terms, and sign—all without leaving the platform. Post-execution tasks like renewals and amendments are tracked automatically.

AI Assistance for Contract Insights

One of the headline features from the 2025 update is the integration of large language models (LLMs) such as ChatGPT into the platform.

“The recent features that we launched with this update is basically our integration with LLMs like ChatGPT. So what that adds to the CLM system is it adds an additional layer of intelligence. So if you are working on a contract document, let’s say 50 pages long, it would be amazing to have an assistant that will tell you what are the key insights from this contract, right? Like it can summarize your contract. It can give you an update on what are the potential risks I have in this contract and it can extract what are the obligations you have in this particular contract so that AI assistant is a powerful feature.”

For small business owners who don’t have an in-house legal team, this is a big step forward. Instead of combing through dozens of pages, AI can highlight deadlines, risks, or unusual clauses in seconds.

Zoho also plans to introduce its own AI engine, Zia, to handle these tasks in a privacy-first way.

Where AI Fits—and Where Humans Still Matter

I asked the question many small business owners are thinking: Can AI really handle contract work without risk?

“That’s a good one. So if you think about it, you have an AI assistant that can summarize that can tell you what is the risk and what is not, but you still need a human judgment to validate whatever the AI is saying, because legal is this one sector where the nuances are a lot, right.”

Balasubramanian explained that while AI can suggest missing clauses, flag obligations, or summarize terms, humans remain essential for judgment. Industry regulations, company policies, and regional laws require context AI cannot fully capture.

In practice, AI reduces the time it takes for a business owner—or their lawyer—to make informed decisions. It speeds up drafting and reviewing but doesn’t eliminate the need for oversight.

Data Privacy and BYOK Integration

When it comes to AI, data privacy is always top of mind for small businesses. I pressed on this point, asking what data is shared with OpenAI when using the ChatGPT integration.

“Definitely. So this particular ChatGPT integration is covered as bring your own key integration. So if you already are using ChatGPT at your organization, you’re bringing that key and you’re enabling this integration with Zoho Contracts, right? So you’re already OK with ChatGPT processing that data to help you improve efficiency or make your contract management easier.”

This Bring Your Own Key (BYOK) approach means businesses maintain control. If they don’t want to use ChatGPT, they can opt out while still leveraging other features. Soon, Zoho’s own LLM will give businesses a private option that doesn’t use contract data for external training.

Balasubramanian emphasized:

“Exactly. Yeah. So what Zoho has achieved is trust, and that trust was built over 3 decades… when Zoho LLM is implemented into our system, the decision becomes very, very easy for our customers.”

CRM Integration for Sales and Legal Transparency

For many small businesses, contracts and customer relationships are tightly linked. Closing a deal often requires quick contract generation and approval. Zoho has deepened its integration with Zoho CRM to make this process seamless.

“So if you are a CRM user. And when you see that a deal is imminent, you can just create a record right from your deal, record in CRM and all of that information in CRM will be fetched. It will be automatically mapped into Zoho’s Contracts. A contract document will be generated and that document is also available as an attachment in Zoho CRM.”

This ensures sales teams don’t have to switch tools or chase down legal staff for paperwork. Contracts are automatically generated from CRM data, cutting down errors and improving speed.

Pre-Approved Templates and Clause Libraries

Another feature aimed at small businesses is the inclusion of templates and a clause library.

“We have 14 predefined templates and these templates are some of the commonly used contracts that any business would need, but actually we would still recommend the small businesses to actually do a check of whether these clauses are relevant for the industry that they are operating in.”

The benefit for small businesses is clear: rather than starting from scratch, they can use ready-made templates and adapt them for their industry. Balasubramanian noted that lawyers are still important but that Zoho Contracts can reduce legal dependencies by providing fallback clauses and pre-approved language.

I summarized it well:

“So more of a way to give a lawyer something that’s you know, 90% of the way there instead of starting from scratch.”

And Balasubramanian confirmed:

“Exactly. Exactly. It’s about reducing the dependency. It’s not about replacing lawyers.”

This approach cuts costs while ensuring negotiations move quickly. For example, if a customer requests a discount, fallback options are already documented. Sales staff don’t need to escalate every negotiation to managers or legal counsel.

Regional and Multi-Organization Support

Zoho Contracts has also expanded features to support businesses operating in multiple regions or managing different subsidiaries.

“We support multi organization. We have some regional settings covered now you can add files and URLs and attachments to your contract documents.”

For small businesses growing across borders—or simply managing multiple LLCs—this flexibility helps keep operations compliant without increasing complexity.

Practical Benefits for Small Businesses

Throughout the discussion, I pointed out how these updates can save business owners “a whole lot of time and effort.” That’s the central promise of Zoho Contracts: reduce headaches, speed up workflows, and improve legal preparedness without overwhelming owners or requiring full-time legal staff.

The combination of AI insights, CRM integration, and pre-approved templates provides a practical toolkit for:

• Freelancers negotiating service contracts

• Small retailers managing vendor agreements

• Agencies drafting client proposals

• Growing businesses dealing with renewals and amendments

By centralizing the contract process, Zoho Contracts helps ensure nothing slips through the cracks.

Trust as a Competitive Advantage

One theme that came up repeatedly was trust. Zoho has long emphasized privacy, and that principle is guiding its AI development. With many small businesses wary of where their data goes, Zoho’s commitment to in-house AI and transparent integrations is a differentiator.

Balasubramanian summed it up:

“Even though we have chat integration, people would still ask when is Zoho LLM coming? Because they would really want to switch. And yeah, that trust I think. Will definitely help the adoption of LLM as well.”

For small businesses, trust isn’t abstract—it directly impacts whether they feel safe using a platform for sensitive contracts.

Where to Learn More

As the interview wrapped up, Balasubramanian directed small businesses to explore the platform further:

“Yeah, people can learn more about Zoho Contracts at zoho.com/contracts.”

Final Takeaway for Small Business Owners

Zoho Contracts is positioning itself as more than just software—it’s a way for small businesses to work smarter with contracts. By combining AI, templates, CRM integration, and a privacy-focused approach, the platform provides a strong alternative to traditional, fragmented contract management.

Small business owners should see this as an opportunity to:

• Cut down on manual legal tasks

• Speed up deal closings

• Reduce dependency on external counsel for routine negotiations

• Improve compliance and risk awareness

• Build a scalable foundation for growth

In the end, Zoho Contracts doesn’t remove the need for lawyers, but it does ensure that small businesses come to the table prepared—with better insights, faster processes, and fewer headaches.

In 2025, while the world gushed about flashy robots, talking cars, and futuristic gadgets, a group of young people quietly built an AI business so unremarkable at first glance that almost nobody noticed it. It wasn’t glamorous, it didn’t have a viral video, and it didn’t try to predict the next big trend. But it was making millionaires out of regular people… people who had nothing but grit, curiosity, and patience.

This is the story of Maya and her “boring” AI model, and how it reshaped not only her life but the lives of thousands who followed her blueprint.

Maya was 27 in early 2024, living in a one-bedroom apartment above a grocery store in a crowded city. She had a degree in literature, not engineering, and worked as a night-shift clerk in a local print shop.

She had no investors, no startup culture contacts, and no idea how to code beyond simple website builders. But she had two assets few people had: relentless curiosity and the habit of looking for problems that nobody wanted to solve.

One evening, while scrolling through forums about small business challenges, she noticed dozens of owners complaining about repetitive, mundane tasks: sorting invoices, tagging emails, filing customer questions, and moving data from one platform to another. It wasn’t glamorous work. It wasn’t on the cover of magazines. But it was expensive and time-consuming.

And something clicked.

“What if AI could quietly do all these boring tasks?” she wondered.

While the tech world chased billion-dollar breakthroughs in self-driving cars and virtual universes, Maya started tinkering with the “unsexy” side of artificial intelligence. She didn’t try to build a sentient assistant. Instead, she built small, focused AI “micro-tools” that automated the ugly, tedious back-office work of everyday businesses.

Maya’s first tool wasn’t impressive. It was a simple AI model that read PDF invoices, extracted key data, and entered it into spreadsheets for small business owners. She found an open-source language model online, watched tutorials for weeks, and cobbled together an interface using no-code platforms. She spent nights testing it on free samples and begging café owners to try it.

Her first paying client was a family-run furniture shop. They hated doing paperwork at the end of every week. Maya’s tool reduced their 6-hour weekly process to 30 minutes. They paid her $50 a month. She danced around her apartment with joy.

But Maya didn’t stop. She built a second micro-tool to categorize customer emails into urgency levels. A third to predict low inventory items. Each small AI bot solved one tiny, boring problem. Together, they saved small businesses hundreds of hours.

By mid-2024, Maya had 47 clients paying $30-$200 a month. Not life-changing, but enough to quit her print shop job. She then created subscription tiers and white-labeled her micro-tools so other freelancers could sell them too. Her pitch wasn’t sexy; it was practical. “Save time. Save money. Grow quietly.”

This was the turning point.

A former schoolteacher named Daniel, 32, bought a license to resell Maya’s AI bots in his own city. He signed up 20 businesses in a month. He made more than he’d made teaching full-time. A retired accountant named Lucia, 58, did the same. She introduced the tools to her network of small retailers and built a six-figure income in a year.

The “boring” AI model had become a movement… not of tech moguls, but of ordinary people solving ordinary problems.

Maya’s philosophy was simple:

Don’t chase hype.

Solve persistent problems.

Keep costs low and margins healthy.

Let others partner and profit.

Instead of selling one giant software platform, she sold dozens of tiny, niche AI “workers” that anyone could subscribe to individually. This modular approach allowed even small-town businesses to adopt AI at their own pace.

By early 2025, hundreds of resellers around the world were using Maya’s framework to deliver micro-AI services. Some ran one-person operations; others built small agencies. They weren’t Silicon Valley founders… they were baristas, teachers, retirees, and college kids who saw a need and used Maya’s blueprint.

One such story was Sophie, a 21-year-old student who had grown up watching her parents run a bakery. Sophie bought Maya’s AI invoicing and scheduling tools, customized them with her own branding, and started selling them to bakeries and cafés in her region. Within six months, she’d replaced her part-time job income. Within a year, she was making $12,000 a month… enough to pay off her student loans before graduating.

Then there was Amir, 44, a former mechanic who lost his job during an economic downturn. He learned how to use Maya’s training materials, packaged AI bots for auto shops, and made more money in his first year of self-employment than he ever had before.

The model worked because it wasn’t glamorous. No flashy ads. No wild claims. Just steady value. Maya called it “AI plumbing”… building the pipes that let small businesses run smoother.

She focused on four principles:

1. Accessibility: Make it cheap and easy enough for non-tech people.

2. Education: Offer plain-language training and support.

3. Flexibility: Let resellers white-label and adjust pricing.

4. Community: Encourage sharing improvements and templates.

By mid-2025, Maya herself wasn’t just running a business. She was leading a decentralized movement of AI micro-entrepreneurs. Her own income grew into the millions, but she always reinvested in building better training and tools.

And yet Maya stayed humble. She still lived in a modest apartment, still answered customer support emails personally, and still said no to investors who wanted to “scale aggressively.” She believed the real revolution wasn’t another billion-dollar tech giant but thousands of small, empowered entrepreneurs earning honest incomes from useful AI tools.

Her success attracted skepticism. Some said it was too simple. Others thought the big companies would crush her. But Maya knew she was in a different lane. She wasn’t trying to win a popularity contest… she was trying to solve real problems.

And as the economy shifted in 2025, her approach turned out to be exactly what people needed: stability, low overhead, and the ability to start small.

By late 2025, analysts began to notice. Articles described the phenomenon of “boring AI” making quiet millionaires. But those inside the movement already knew: it wasn’t about hype. It was about mindset.

Maya often told her community:

“Find a problem. Build the simplest AI solution. Offer it to the people who need it most. Repeat. Don’t try to impress. Try to improve.”

It was a model anyone could adopt. A 19-year-old in Manila built AI tools for local fishermen to predict tide patterns. A 63-year-old in Nairobi used AI bots to help farmers monitor soil moisture. A single mom in Toronto built an AI appointment scheduler for local hair salons. The stories poured in, all rooted in the same principle: simple solutions, consistent effort, and community sharing.

Maya had proven something profound:

You don’t need to invent the next flying car to become successful. You can build “boring” tools that make life easier… and people will pay for that forever.

Moral of the Story

In a world obsessed with hype and spectacle, quiet consistency and practical problem-solving can outlast trends. The “boring” AI business model shows that success isn’t about dazzling innovation… it’s about meaningful impact. When you stop chasing fame and start solving real problems, you unlock a sustainable path to wealth, freedom, and purpose. Even the most ordinary ideas, applied persistently, can change lives… including your own.

Predictive AI offers tremendous potential – but it has a notoriously poor track record. Outside Big Tech and a handful of other leading companies, most initiatives fail to deploy, never realizing value. Why? Data professionals aren’t equipped to sell deployment to the business. The technical performance metrics they typically report on do not align with business goals – and mean nothing to decision makers.

For stakeholders and data scientists alike to plan, sell and greenlight predictive AI deployment, they must establish and maximize the value of each machine learning model in terms of business outcomes like profit, savings – or any KPI. Only by measuring value can the project actually pursue value. And only by getting business and data professionals onto the same value-oriented page can the initiative move forward and deploy.

Why Business Metrics Are So Rare for AI Projects

Given their importance, why are business metrics so rare? Research has shown that data scientists know better, but generally don’t abide: They rank business metrics as most important, but in practice focus more on technical metrics. Why do they usually skip past such a critical step – calculating the potential business value – much to the demise of their own projects?

That’s a damn good question.

The industry isn’t stuck in this rut for only psychological and cultural reasons – although those are contributing factors. After all, it’s gauche and so “on the nose” to talk money. Data professions feel compelled to stick with the traditional technical metrics that exercise and demonstrate their expertise. It’s not only that this makes them sound smarter – with jargon being a common way for any field to defend its own existence and salaries. There’s also a common but misguided belief that non-quants are incapable of truly understanding quantitative reports of predictive performance and would only be misled by reports meant to speak in their straightforward business language.

But if those were the only reasons, the “cultural inertia” would have succumbed years ago, given the enormous business win when ML models do successfully deploy.

The Credibility Challenge: Business Assumptions

Instead, the biggest reason is this: Any forecast of business value faces a credibility question because it must be based on certain assumptions. Estimating the value that a model would capture in deployment isn’t enough. The calculation has still got to prove its trustworthiness, because it depends on business factors that are subject to change or uncertainty, such as:

• The monetary loss for each false positive, such as when a model flags a legitimate transaction as fraudulent. With credit card transactions, for example, this can cost around $100.
• The monetary loss for each false negative, such as when a model fails to flag a fraudulent transaction. With credit card transactions, for example, this can cost the amount of the transaction.
• Factors that influence the above two costs. For example, with credit card fraud detection, the cost for each undetected fraudulent transaction might be lessened if the bank has fraud insurance or if the bank’s enforcement activities recoup some fraud losses downstream. In that case, the cost of each FN might be only 80% or 90% of the transaction size. That percentage has wiggle room when estimating a model’s deployed value.
• The decision boundary, that is, the percentage of cases to be targeted. For example, should the top 1.5% transactions that the model considers most likely to be fraudulent be blocked, or the top 2.5%? That percentage is the decision boundary (which in turn determines the decision threshold). Although this setting tends to receive little attention, it often makes a greater impact on project value than improvements to the model or data. Its setting is a business decision driven by business stakeholders, representing a fundamental that defines precisely how a model will be used in deployment. By turning this knob, the business can strike a balance in the tradeoff between a model’s primary bottom-line/monetary value and the number of false positives and false negatives, as well as other KPIs.

Establishing The Credibility of Forecasts Despite Uncertainty

The next step is to make an existential decision: Do you avoid forecasting the business value of ML value altogether? This would prevent the opening of a can of worms. Or do you recognize ML valuation as a challenge that must be addressed, given the dire need to calculate the potential upside of ML deployment in order to achieve it? If it isn’t already obvious, my vote is for the latter.

To address this credibility question and establish trust, the impact of uncertainty must be accounted for. Try out different values at the extreme ends of the uncertainty range. Interact in that way with the data and the reports. Find out how much the uncertainty matters and whether it must somehow be narrowed in order to establish a clear case for deployment. Only with insight and intuition into how much of a difference these factors make can your project establish a credible forecast of its potential business value – and thereby reliably achieve deployment.