The industry’s product menu already stretches from political elections and World Cup matches to weather events. It now includes reality television, with Kalshi’s first markets tied to “Love Island USA” helping to more than double its weekly active female user base during part of June, illustrating how easily an exchange can turn an existing online fandom into a new trading constituency.

Prediction markets aren’t done there. Kalshi is reportedly in advanced discussions with regulators about expanding its perpetual futures business beyond cryptocurrencies into gold, other metals, foreign exchange and energy. Polymarket, meanwhile, has reportedly filed applications that would help it offer margin trading to customers in the United States.

Prediction markets, it would seem, are outgrowing the category that made them famous. They are evolving from event-based content into a new distribution layer for a potential next-generation of retail derivatives.

See also: Robinhood’s Memecoin Boom Shows Crypto’s Retail Market Is No Joke

Prediction Markets Are Becoming a Product Portfolio, Not a Betting Category

The event contract services business is evolving from predicting discrete events to trading continuous exposure to economically important assets. That transition is occurring just as the industry’s regulatory position is becoming more complicated.

A federal judge this week rejected Kalshi’s attempt to prevent New York from applying state gambling laws to its sports contracts. Last month, the Chicago Mercantile Exchange (CME) sued the Commodity Futures Trading Commission and its chairman, Michael Selig, challenging a decision to let Kalshi and crypto exchange Coinbase list perpetual futures.

The result is a market in which product demand may be the easy part. The harder question is whether prediction platforms can develop a compliance system broad enough to support everything from television finales to leveraged commodity trades.

The Love Island contracts, for example, expose the prediction market category’s fundamental surveillance problem. Television episodes are produced before they are broadcast, meaning cast members, production staff, editors and others can possess information unavailable to the public. Similar informational asymmetries arise around economic announcements, court decisions, corporate events and government actions. The more subjects a platform makes tradable, the more types of potential insiders it must identify.

Goldman Sachs prohibited employees from participating in financial and political event contracts that could create actual or perceived conflicts involving the bank, its clients or the financial industry, particularly when workers could possess confidential corporate or macroeconomic information.

The Senate unanimously adopted a rule in April prohibiting senators, staff and officers from participating in prediction markets. Arizona Gov. Katie Hobbs followed this month with an executive order prohibiting state executive branch employees from using nonpublic government information for prediction market profits.

Read also: Prediction Markets Turn Uncertainty Into a Business Model

A Short History of Prediction Market Products and U.S. Regulation

Despite all the action, prediction markets began as relatively constrained experiments in information aggregation. The CFTC said market operators have sought agency guidance since the early 1990s, and the first prediction market was designated as a federally regulated contract market in 2004. The central idea was that putting money behind a forecast could aggregate dispersed information more effectively than polls, surveys or expert opinion.

The model remained small partly because regulators treated event contracts as exceptional products. Contracts tied to economic indicators, elections or entertainment did not fit comfortably within either traditional futures regulation or state gambling frameworks.

Polymarket demonstrated the potential and limitations of operating outside that system. In 2022, the CFTC ordered the company to pay a $1.4 million penalty and wind down markets that violated federal derivatives laws. Polymarket later returned to the U.S. by acquiring federally licensed exchange and clearing infrastructure, creating a regulated domestic operation that is separate from its crypto-based international platform.

PYMNTS reported in September that when the CFTC issued a no-action letter regarding event contracts in response to a request from two businesses owned by Polymarket, it in essence gave Polymarket a regulatory green light to re-enter the U.S. market.

The industry’s short history, in other words, is not primarily a progression from one betting topic to another. It is a progression from restricted forecasting experiment to full-scale exchange infrastructure. That direction of travel appears to be continuing.

Organizations are shifting their focus from isolated use cases and standalone tools to connect AI directly to financial data across workflows.

Recent updates to Sage Intacct reflect this trend. The latest capabilities embed AI within everyday processes while also enabling finance teams to extend AI capabilities beyond Sage’s core system in a secure and governed way.

Introducing Finance AI

Sage Intacct has reached a major milestone with the release of Finance AI, now available to all customers at no cost through May 2027.

Finance AI represents the next phase of embedded AI, bringing together purpose-built Sage Copilot and AI agent capabilities for finance teams.

This enables Sage Intacct users to begin applying AI in their workflows immediately, without requiring budget approval or long-term commitment.

With Finance AI extended, teams can:

• Test AI-driven workflows in a real environment;
• Identify high-value use cases across finance; and
• Build internal adoption and confidence before investing further.

For many organizations, this will serve as the starting point for AI adoption, allowing them to test the technology and expand into more advanced use cases over time.

AI Inside and Around Sage Intacct

Sage continues to expand its AI capabilities through Sage Copilot and a growing set of AI agents designed to streamline operations and improve decision-making across finance.

Applications include:

• Automating invoice processing to reduce manual effort and errors;
• Enabling natural language queries to move from question to insight faster;
• Supporting the close process with task tracking and guidance; and
• Identifying unusual activity in real time.

With AI now available directly within financial workflows, teams no longer need to work outside the system to incorporate AI enhancements.

Sage is also expanding AI integration capabilities by giving organizations the ability to connect external AI tools to their financial data.

Extending AI with the Sage Intacct AI Gateway

The Sage Intacct AI Gateway is a key part of the platform’s evolution.

AI Gateway makes secure, AI-enabled access to the Sage Intacct REST API and MCP Server available to all customers and partners.

This opens the door for organizations to build AI solutions that align with their existing tools, processes, and architecture.

With the AI Gateway, finance teams can:

• Connect external AI tools directly to Sage Intacct data;
• Build custom AI-driven workflows and use cases;
• Maintain existing role-based access and permissions; and
• Use their preferred AI platforms without requiring a specific model or vendor.

In short, it enables organizations to customize their AI while connecting it securely to their financial system.

The Sage Intacct MCP Server: A Controlled Approach to AI Access

At the center of the AI Gateway is the Sage Intacct MCP Server, a secure orchestration layer that manages AI application interactions with Sage Intacct through a single governed endpoint.

Rather than allowing direct, uncontrolled access to financial data, the MCP Server centralizes how AI tools interact with the system.

Key characteristics include:

• Real-time, read-only access to core financial areas such as AP, AR, GL, cash management, purchasing, and order entry;
• Compatibility with MCP-enabled AI clients; and
• Governance and security aligned with existing user permissions.

The MCP Server does not write data and is not a standalone AI tool. As a read-only model, MCP Server helps ensure data integrity while enabling AI-driven insights and workflows.

Connecting Existing AI Tools with the MCP Connector

To further simplify adoption, Sage has introduced a newly released MCP connector that allows organizations to connect existing AI tools to Sage Intacct.

This removes the need for complex custom integrations and makes it easier to:

• Bring financial data into broader AI workflows;
• Extend existing AI investments; and
• Quickly test and scale new use cases.

Instead of starting from scratch, organizations can build on what they already have while maintaining the governance advantages within the MCP framework.

How These Pieces Work Together

Sage’s AI strategy is built around flexibility and choice. Recent updates enable:

• Sage Copilot and AI agents bring AI directly into the Sage Intacct experience;
• AI Gateway and MCP Server enable secure access for external AI tools; and
• MCP connector links those tools to real financial data.

This layered approach allows organizations to start with embedded capabilities and expand into customized AI solutions as their needs evolve.

Why This Matters

Finance teams are under increasing pressure to deliver faster insights, reduce manual work, and support strategic decision-making.

AI can help address these challenges, but only if it is connected to the data that matters most.

These Sage Intacct updates make it possible to:

• Access and use financial data within AI tools securely;
• Extend AI across systems instead of keeping it siloed; and
• Maintain control, governance, and auditability.

This is a shift from isolated automation to connected, data-driven workflows.

How CBIZ Can Help

As a leading Sage VAR partner, CBIZ works with organizations to evaluate where AI can drive the most impact. If you’re exploring how to connect AI to your financial data or want to better understand where to start your AI journey, our team can help you define the right approach and build a roadmap aligned to your goals.

Connect with a member of our team to explore how Finance AI, Sage Copilot, and the AI Gateway can support your organization.