Stop the Disconnect: How Joined-Up AI Helps Big Businesses Beat Silos and Get Better Results

It’s a common story in big companies: Marketing runs campaigns that the sales team doesn’t hear much about. Salespeople make promises that customer success teams find tough to keep. Information gets stuck in separate department “silos.” This creates blind spots, slows down company growth, and often leaves customers feeling frustrated. For large businesses juggling many product lines and worldwide operations, these disconnects aren’t just small irritations—they can mean losing millions in missed chances and wasted effort.

The Real Cost of Departments Working in Isolation

A 2023 McKinsey survey on how companies use AI showed something important: businesses where AI projects are stuck in silos see much lower returns than those that use AI in a connected way. In fact, companies that do really well are twice as likely to have their data systems joined up, connecting all departments, instead of keeping separate systems for each team.

McKinsey’s 2025 report on the state of AI in business also notes, “Organizations are applying the technology where it can generate the most value—for example, service operations for media and telecommunication companies, software engineering for technology companies, and knowledge management for professional-services organizations” (McKinsey, 2025). When these efforts are connected, that value usually gets much bigger.

Why Old Ways of Fixing This Often Don’t Work Well

Many big companies try to tackle these silo problems with older methods, but often hit snags:

• Huge CRM changes: These projects can cost a lot, become hard to manage, and need constant fixing and updates.
• Bringing data together by hand: This eats up a lot of time and it’s very easy to make mistakes.
• Lots of separate, small tools: This often just makes things more confusing and creates new headaches when you try to get them to share information. This is the opposite of a helpful integrated marketing AI platform.
• Building big custom systems from scratch: These can take 12 to 18 months, or even longer, to build, and you’re not always sure if you’ll get what you need at the end.

Good news for a more modern approach comes from a 2024 research paper on AI in Enterprise Resource Planning (ERP) systems: “businesses adopting AI-driven ERP solutions have experienced over a 30% increase in user satisfaction and a 25% boost in productivity due to enhanced personalization of interfaces” (ResearchGate, 2024). This highlights how AI can help when it’s built into how a company works.

A Smarter Way: The Joined-Up AI System

Forward-thinking large businesses are taking a different route. They are setting up connected AI systems that smoothly link how their marketing, sales, and customer success teams work. This helps achieve much better AI-driven sales and marketing alignment.

Here’s what that looks like in practice:

1. One Central Place for All Customer Information

Salesforce’s Customer 360 is a good example of this idea. It uses their Einstein AI to gather customer information from all the different ways a customer interacts with the company (website, sales calls, support, etc.). This helps create a very full and clear picture of each customer. As DigitalDefynd’s 2025 Salesforce AI case study points out, this allows for “predictive personalization.” The system can “forecast future customer actions, enabling businesses to tailor communications and offers instantly” (DigitalDefynd, 2024).

2. AI Workflows That Share Information Across Teams

Modern systems now allow information and smart AI suggestions to flow easily between departments, instead of getting stuck. ServiceNow shows this well with their “Follow the Workflow” idea. Tidemark Capital explained it like this: “ServiceNow executed the FTW strategy to build one of the largest and most important software franchises… in the past 20 years” by making computer programs that “mirror user’s workflows, extending throughout their flowchart of responsibilities” (Tidemark, 2024). This means the AI helps with tasks as they naturally move from one team to the next.

A real-life example from a ServiceNow developer blog shows how they helped a retail company get great results: “Faster Incident Resolution: Automation cut ticket resolution time by 25%, freeing up service desk staff to focus on high-priority tasks” (ServiceNow Community, 2024). This happened because AI helped different parts of their customer service process work together better.

3. Strong Security and Rules for Big Businesses

For large companies, keeping customer and business data safe and following all the rules is a must-do, not an option. Top AI platforms offer strong security setups. These let companies use AI tools with confidence across different countries and departments without worrying about breaking rules or creating new risks.

Salesforce, for example, built its Einstein AI platform with what they call a “Trust Layer.” This layer protects private customer information but still lets companies use different kinds of AI models. This way, companies can “keep your data locked down tight” while still using the power of various helpful AI language programs.

Getting It Done: A Step-by-Step Plan

The most successful big AI projects that connect departments usually happen in clear, planned stages. ServiceNow’s work often follows this kind of step-by-step plan, as shown in their case studies:

Phase 1: Building the Base (Roughly 90 Days)

• Set up a common way to organize all the data.
• Connect the main AI system with the company’s existing CRM or ERP tools.
• Start with some basic automated workflows for key teams.
• Create dashboards so leaders can see how things are going.

Solugenix, a company that helps set up ServiceNow, shared a success story with a logistics company. They “built a proof of concept tailored to the logistics provider’s key business goals, along with an implementation plan that was broken down into phases to accommodate their existing functions” (Solugenix, 2020). Starting with a focused test (a “proof of concept”) and a clear, staged plan is very important.

Phase 2: Adding More Capabilities (90-180 Days)

• Bring in more advanced data analysis and prediction tools.
• Give specific AI helper tools to different teams.
• Build custom AI workflows for any unique company processes.
• Start using the system in other countries or company locations.

Phase 3: Fine-Tuning and Growing (6-12 Months)

• Keep making the AI models better based on feedback and real results.
• Add advanced tools for checking what competitors are doing.
• Set up detailed reports for top bosses to help with big company decisions.
• Roll it out fully across the globe, making sure it works well in local languages and for local needs.

How to Measure Success When Teams Work Together

McKinsey’s research on companies that use data well points out that businesses need to stop looking only at how each separate department is doing. Instead, they should focus on results that show how the whole business is winning. As they said in their 2024 report, “To enable the scale required to operate data-driven businesses in 2030, data leaders will need an approach that accelerates how use cases provide impact while solving for scale through an architecture that can support the enterprise” (McKinsey, 2024). This means checking how the joined-up AI helps the entire company achieve its main goals.

Key Things to Get Right for a Smooth Setup

ServiceNow customers have found several things are very important for making these multi-team AI projects work well:

• Support from Top Leaders: Big changes like this need the C-suite (CEO, CFO, etc.) to be fully behind the project and actively helping it succeed.
• Helping People Adjust to New Ways of Working: In the Solugenix logistics project, they found that doing “knowledge-transfer sessions to train various teams on the new system” was key for “ensuring a smooth transition for the client’s employees and customers” (Solugenix, 2020).
• Good Technical Plan: A ServiceNow story with a global company showed how important it was to make sure the new AI fit with their existing tech. They noted they “reduced port density by 60%” by first improving their computer network.
• Start with High-Value Projects: Begin with AI projects that can show clear benefits quickly. This builds excitement and support for more.
• Keep Making it Better: Plan to keep tweaking and improving the AI system based on how it’s working in the real world.

Conclusion: The Big Advantage of Truly Joined-Up AI

McKinsey’s research confirms it: getting AI to work smoothly across different departments is becoming a major way for companies to get ahead of their competition. Top companies using AI are focusing more and more on “embedding gen AI solutions into business processes effectively (for example, changing frontline employees’ processes, creating user interfaces that incorporate AI features).”

Salesforce’s Customer 360 idea is a great example. It brings together data from all departments to create what they call “a single source of truth that drives action across agents, apps, automation, prompts, analytics, and more” (Salesforce, 2025).

For Chief Revenue Officers (CROs) and other leaders in large, complex businesses, the real question isn’t *if* you should join up your AI approach across departments, but how fast you can do it before your competitors get too far ahead.

Want to see how a joined-up, enterprise-level AI system could change how your company brings in revenue? Talk with our team for a private chat about your company’s setup and goals.