The Next Generation of Business Software

A market guide from Simply Work with AI · October 2026

AI is bringing documents, data, analytics and applications together

The next generation of business software isn’t just about adding a chatbot to an existing application. It’s about changing how people find information, understand their business, collaborate, make decisions, and get work done.

A growing number of platforms are bringing together capabilities that used to live in separate products: documents, spreadsheets, databases, business intelligence, automation, organizational knowledge, and AI assistants or agents.

Some start with sophisticated data infrastructure. Others start with the documents and workflows people use every day. Still others aim to connect what a company knows and make that knowledge easier to find and use.

For small and medium-sized businesses, this presents an exciting opportunity—and a confusing marketplace. Which platforms solve fundamentally different problems? Which are approachable without a large IT department? And which are likely to deliver practical value rather than simply more technology to manage?

This guide looks at ten platforms through a business lens. It combines publicly described capabilities with our principal consultant’s observations from exploring this market. The observations are editorial judgments, not standardized hands-on tests or endorsements. The order moves from tools closer to everyday business work toward more specialized analytics and enterprise data infrastructure; it is not a ranking from best to worst.

Ten platforms at a glance

These brief introductions are followed by a more detailed assessment of each platform.

A comparison through the small-business lens

The important distinction isn’t simply how much AI a platform contains. It’s where the platform begins, what it asks a business to have in place, and how quickly it can help people accomplish something useful.

Platform	Primary strength	SMB relevance	Principal consideration
Superhuman Docs	Connected documents, tables, and workflows	High	Enjoyable to work with, fun, and unbeatable simplicity-to-power ratio.
Airtable	Structured data. This is the platform with “super spreadsheet” abilities & increasing AI integration.	High	Think spreadsheets and databases reimagined. Highly suitable for many SMB environments. Will deliver better structure and understanding of the data.
Notion	Flexible knowledge and project workspace	High	Flexibility can become complexity
Glean	Finding knowledge across applications	Selective	Enterprise fit, integrations, and economics
Akkio	Specialized media and marketing AI	High for its niche	Not a general-purpose business platform
ThoughtSpot	Conversational business intelligence	Moderate	Reliable answers depend on governed data
Omni	Consistent metrics and self-service analytics	Moderate	Needs a sound underlying data model
Definite	Connecting data to AI-driven analysis	Selective	Data integration and onboarding effort
Snowflake	Governed cloud data and AI foundation	Selective	Scale, cost, and technical demands
Databricks	Advanced data engineering and AI	Selective	Significant technical and implementation effort

SMB relevance is our editorial assessment of likely fit for a typical smaller business—not a product score, pricing comparison, or independent performance test. A specialized product may be excellent for the right company and irrelevant to another.

Three ways these platforms approach the future of work

1. Start with the work people actually do

Superhuman Docs and Notion begin close to everyday business life: documents, projects, structured information, and workflows. Glean approaches that same working day from another angle, helping people find information across tools they already use. These platforms are particularly interesting because their value can be understood without first discussing data warehouses or machine-learning infrastructure.

2. Start with data and trustworthy answers

ThoughtSpot, Omni, Definite, Snowflake, and Databricks place greater emphasis on the data foundation and the ability to analyze it. They differ enormously in scope, but they share an important truth: AI cannot reliably explain a business if the underlying information is fragmented, inconsistent, or poorly defined. Conversational interfaces may make analytics easier to use; they do not eliminate the need for data discipline.

3. Start with a particular business problem

Akkio illustrates a third approach: rather than trying to become the universal business platform, it concentrates on specialized workflows in media and marketing. For some businesses, that may be the most sensible path of all. A focused solution to an expensive, repetitive problem can outperform an ambitious all-in-one system that never quite gets adopted.

A closer look at the ten platforms

1. Superhuman Docs (formerly Coda) ✅

What it is: Superhuman Docs brings together rich documents, structured tables, formulas, connected views, automations, and AI-assisted work. Its roots are in Coda, whose distinctive proposition has long been that a document can behave more like a purpose-built application.

What caught our attention: The ability to turn a familiar-looking document into a working business system. Information doesn’t have to remain passive; it can be organized into processes, decisions, and repeatable actions.

Our experience: This has been our front-runner. Working in Coda changed how we think about the space between conventional documents and custom business software. There is genuine pleasure in making something useful and watching the pieces work together. Of the platforms in this guide, it is the clearest example of technology that invites experimentation rather than merely demanding administration.

Our reservations: Flexibility isn’t a substitute for good design. As documents become more capable, they also need clear ownership, sensible structures, and ongoing maintenance. A badly designed workspace can become another problem instead of a solution.

Our verdict: This is one of “Paul’s Picks.” Almost certainly suitable for a smaller business that wants to connect information and workflow without commissioning a conventional software project. Begin with one valuable process, build it carefully, and expand only when the first solution proves its worth.

2. Airtable ✅

What it is: Airtable began by reimagining the spreadsheet as something more powerful: a flexible, relational database that combines the familiarity of rows and columns with the ability to connect information, create different views, and build applications around business data.

Today, Airtable is moving well beyond its spreadsheet heritage. It brings together structured data, interfaces, automations, and AI capabilities that help people create and operate business applications. Its AI tools, including Omni, are making it increasingly possible to build and modify these systems through natural-language instructions rather than traditional programming.

The result is a platform that can serve as much more than a database. For the right business, it can become the foundation for managing customers, products, sales activities, projects, inventory, and other operational processes.

What caught our attention: The combination of structured information and practical application-building is compelling. Instead of simply recording information in a spreadsheet, you can connect related records, create views for different users, build interfaces around specific tasks, and automate parts of the workflow.

More recently, Airtable’s AI capabilities have begun to change the building experience itself. Rather than having to configure every element manually, you can work with an AI agent to help construct the system you have in mind.

We’ve been exploring this firsthand, building a Stores and Calls management system and working through the practical details of how the pieces fit together. The experience demonstrated how quickly a useful business application can take shape when structured data, linked records, and AI-assisted building work together.

What we like: This is one of “Paul’s Picks.” The power of the super spreadsheet. Airtable retains the flexibility and familiarity of a spreadsheet while adding the structure and relationships of a database. That combination is particularly useful for businesses whose information has outgrown ordinary spreadsheets.

The ability to build around the business. Different teams and users can work with the same underlying information through different views, interfaces, and workflows. This makes it possible to shape the software around the way a business actually operates.

AI as a building partner. Airtable’s AI capabilities offer a more accessible route into application development. You can describe what you want to accomplish and work with AI to turn the idea into something functional. The result still benefits from human judgment, testing, and refinement, but the barrier to getting started is falling.

Practical business applications. For small and medium-sized businesses, Airtable offers a way to build tailored systems without commissioning a conventional software-development project. That is a meaningful advantage when off-the-shelf applications don’t quite fit the way a company works.

What gives us pause: Airtable’s flexibility comes with responsibilities. A well-designed base can be an excellent business tool; a poorly designed one can become difficult to maintain. Linked records, field definitions, interfaces, permissions, and automations all need thoughtful organization as a system grows.

There are also practical limits to consider. Record allowances, AI credits, and other plan restrictions can affect how much a business can accomplish without upgrading. We encountered this ourselves when our Stores base exceeded the Free plan’s record limit, forcing us to reconsider which records we needed to keep.

And while AI makes building easier, it doesn’t eliminate the need to understand the underlying system. Businesses still need to check that the information is accurate, the workflows behave as intended, and users can operate the finished application confidently.

Our perspective: Airtable is particularly interesting because it occupies a space between the traditional spreadsheet and a custom business application. It gives smaller organizations a practical way to organize structured information and build tools around it, with AI increasingly helping to bridge the gap between an idea and a working system.

In a recent side-by-side exercise, we built a similar Stores and Calls system in Airtable and Superhuman Docs. Both proved capable, but we gave Superhuman Docs a slight edge in that particular comparison: 9/10 versus 8.5/10 for Airtable. Those are personal assessments of our building experience, not objective product rankings.

The distinction is revealing. Airtable’s strength is its structured-data heritage and the applications that can be built around it. Superhuman Docs approaches the problem from a more document-centered, flexible workspace. Both are moving toward a world in which businesspeople can create increasingly sophisticated systems with the assistance of AI.

For businesses that need connected records, operational tracking, and purpose-built applications, Airtable deserves serious consideration. It is no longer simply a clever alternative to a spreadsheet. It is becoming a platform for building the software a business needs to run its work.

Best suited to: Small and medium-sized businesses that need structured business information, connected records, custom applications, and increasingly AI-assisted workflows.

3. Notion

What it is. Notion is a broad workspace for documents, knowledge bases, projects, databases, and AI-assisted work. It can accommodate many different ways of organizing information and collaboration.

What caught our attention. Its maturity and sheer flexibility. Teams can bring many activities under one roof, and there is a substantial ecosystem of templates and practices around it.

Our experience. Our early exploration left us impressed—but also exhausted. The reaction was blunt: “I spent an hour in Notion and I got a headache.” That is not a technical criticism or a verdict on every user’s experience. It is a reminder that software must feel approachable to the people expected to use it.

Our reservations. With so many ways to structure a workspace, decisions about navigation, databases, permissions, and conventions can multiply quickly. Without a plan, the freedom to build anything can make it harder to find a clear starting point.

Our verdict. A serious contender for organizations that want a broad, flexible workspace and are prepared to govern it. Evaluate the experience of an ordinary employee—not just the enthusiasm of the person setting it up.

4. Glean

What it is. Glean is an AI work assistant and enterprise search platform intended to connect information across business applications and help employees retrieve and use it, subject to underlying access permissions.

What caught our attention. The idea that people should be able to ask a question and locate useful organizational knowledge without remembering which application or folder contains it. That addresses a problem almost every growing company recognizes.

Our experience. We were drawn to the intelligence of the concept but less convinced by the enterprise orientation of the offering. A smaller company may love the promise while finding that its needs are not quite the center of the product’s world.

Our reservations. Connector coverage, permissions, information quality, adoption, and cost all matter. An AI assistant can only find and explain what it is properly connected to and allowed to access.

Our verdict. An important model for the future of organizational knowledge. For a smaller firm, the first question is whether a dedicated cross-application intelligence layer is necessary—or whether simpler improvements to the existing workspace would solve the immediate problem.

5. Akkio

What it is. Akkio has developed a focused AI offering for media agencies and related marketing workflows, including work with campaign, audience, and performance data.

What caught our attention. Its specialization. While many vendors promise a platform for almost everything, Akkio illustrates how AI can be applied to a particular kind of recurring commercial work.

Our experience. We have not established a comparable hands-on verdict for Akkio, so our assessment here is of its market approach rather than its day-to-day usability.

Our reservations. Specialization cuts both ways. A focused platform can deliver excellent value within its niche while being of little use to a business outside it. Its current product focus should be checked against the exact workflow being considered.

Our verdict. Worth understanding as a reminder that a business doesn’t necessarily need an all-purpose AI platform. It may need one well-targeted tool that solves a costly problem exceptionally well.

6. ThoughtSpot

What it is. ThoughtSpot is a business intelligence platform built around making analytics more accessible through search, conversational interaction, and visual exploration of business data.

What caught our attention. The possibility of replacing some of the traditional back-and-forth of reporting with direct questions: Why did sales change? Which locations are performing differently? What deserves attention this week?

Our experience. Our interest is in the business proposition rather than a verified, extensive hands-on assessment. The test that matters is whether a manager can obtain an answer that is understandable, traceable, and correct.

Our reservations. Natural-language questions do not repair unreliable source data or ambiguous definitions. A polished answer can be dangerously persuasive if its underlying metrics are wrong.

Our verdict. A meaningful direction for business intelligence, especially where organizations already have dependable data. Evaluate it with real business questions and insist on explanations that can be checked.

7. Omni

What it is. Omni combines a governed analytics layer with dashboards, flexible analysis, and AI-assisted interaction, helping teams work from consistent definitions of business metrics.

What caught our attention. The emphasis on making numbers consistent. If sales, operations, and finance all define the same metric differently, easier dashboards and AI questions won’t solve the underlying disagreement.

Our experience. We see the logic of the approach, but we are not presenting a firsthand usability verdict. Its value is best judged against an organization’s actual reporting and data-modeling needs.

Our reservations. Governed analytics still needs someone to define and maintain the data model. For a company without that foundation, the setup effort may exceed the immediate benefit.

Our verdict. Particularly relevant to growing companies whose reporting has become fragmented or inconsistent. The opportunity is not simply to ask more questions, but to make sure everyone is asking questions of the same business reality.

8. Definite

What it is. Definite aims to connect business data sources and make analysis and AI-powered answers available through a more integrated environment.

What caught our attention. The appeal of bringing the journey from scattered data to useful insight into one place. For many businesses, stitching together separate tools is itself a major obstacle.

Our experience. Our exploration felt “too geeky and technical” for the kind of approachable environment we were seeking. An early onboarding experience that left us waiting for a response added to the frustration. That reflects our experience at the time, not a claim about every customer’s experience or the company’s current responsiveness.

Our reservations. Connecting data remains real work, even when a product promises to simplify it. Setup, support, data quality, and the path to a first useful result deserve as much scrutiny as the AI features.

Our verdict. An intriguing concept for businesses with genuine multi-source data problems. We would want to see a smoother, more accessible path from signup to business value before recommending it as a natural first stop for smaller firms.

9. Snowflake

What it is. Snowflake is a major cloud data platform with capabilities for storing, governing, sharing, analyzing, and increasingly applying AI to organizational data.

What caught our attention. The seriousness of its data foundation, governance, and enterprise capabilities. For companies whose operations depend on substantial, complex data estates, those qualities can be essential.

Our experience. We found ourselves thinking of Snowflake and Databricks as “lumbering battleships.” The comparison is affectionate in one sense—they are formidable—but it captures the mismatch between enterprise-scale machinery and the needs of many smaller organizations.

Our reservations. The platform’s full value depends on data architecture, governance, skills, and ongoing management. That is a very different proposition from giving a small team a better way to organize its everyday work.

Our verdict. A leading platform worth knowing, but not a default recommendation for a typical SMB. Start with the business problem and the existing data landscape; don’t start by assuming a major cloud data foundation is required.

10. Databricks

What it is. Databricks is a sophisticated platform spanning data engineering, analytics, machine learning, and AI development. Its strength lies in helping technically capable organizations work with data and AI at scale.

What caught our attention. The breadth and depth of what it makes possible. For a data-intensive enterprise, the ability to bring multiple technical disciplines together can be strategically significant.

Our experience. A “remarkable battleship” was the phrase that came to mind. We could admire the engineering without imagining it as the natural environment for a small business owner who wants to make tomorrow’s work easier.

Our reservations. Sophisticated infrastructure brings a corresponding need for technical expertise, implementation planning, and ongoing management. A product can be exceptional at what it does and still be the wrong scale of answer.

Our verdict. Important in the larger AI ecosystem, but for most smaller organizations, a platform to understand rather than the place to begin. Solve the immediate operational problem before taking on enterprise-grade architecture.

How should a smaller business choose?

The most useful starting point is not a product demo. It’s a clear description of where work is getting stuck.

• If information and processes are scattered across documents and spreadsheets: explore whether a connected workspace such as Superhuman Docs or Notion can bring order to the work.
• If employees cannot find what the organization already knows: investigate better knowledge organization and search, including the approach represented by Glean.
• If a particular industry workflow is repetitive or expensive: consider specialized solutions such as Akkio, where the fit is specific and measurable.
• If managers struggle to get trustworthy answers from business data: look at the analytics approaches represented by ThoughtSpot and Omni, while checking the quality of the data underneath.
• If the organization genuinely needs to integrate many data sources: evaluate the more substantial data-platform approaches represented by Definite, Snowflake, and Databricks—but only in proportion to the need.

Five questions worth asking before committing

1. What specific business problem are we solving? Name the wasted time, duplicated effort, missed opportunity, or unreliable decision.
2. Where does the information live today? Consider documents, spreadsheets, email, databases, and existing business applications.
3. Can we trust the answers? Check data quality, permissions, definitions, and the ability to trace an AI-generated conclusion back to its sources.
4. Will ordinary people actually use it? Test the experience with the people who do the work—not only the technically enthusiastic champion.
5. What will the complete solution cost to operate? Include implementation, training, integrations, maintenance, governance, and the time required to keep it useful.

The bigger picture: business first, technology second

What makes this generation of software so interesting is the possibility of closing a gap that has existed for decades: the gap between the way a business wants to work and the rigid structure of the software it has been given.

Documents are becoming more active. Databases are becoming more approachable. Analytics is becoming more conversational. Knowledge can be found across applications. AI is beginning to help connect these capabilities instead of merely sitting beside them.

But greater capability does not automatically mean greater usefulness. A complicated system that nobody wants to use is not progress. Neither is an elegant AI answer built on unreliable information. The winners for small and medium-sized businesses will be the solutions that fit the way people work, solve problems they actually have, and remain manageable as the business grows.

That’s why our strongest interest is not necessarily in the largest or most technically impressive platform. It’s in the tools that help people build better processes, connect their knowledge, and make sound decisions without turning every improvement into an IT project.

The goal isn’t to use more AI. It’s to build a business that works better.

Editorial note: This guide reflects the platform landscape and our assessments as of October 2026. Product capabilities, names, packaging, and pricing can change. Personal observations describe limited exploration rather than a standardized evaluation of all ten products. Before choosing a platform, verify its current features, integrations, security, support, and total cost directly with the vendor.