The Sheer Volume Of Software In Use In Corporates Is Mind Boggling
367+
On Average
Some Had 600+
Some Were Approved
Many Were Not
Average US Firm Uses 367 Software Applications.
(Forrester Survey 2022 – 1000 Companies and all staff)
The average large company in the USA uses an average of 367+ disconnected software applications.
Now THIS is a serious number – it came from a Forrester survey in 2022. Forrester spoke to over 1000 corporates in the USA and the UK and surveyed every team member. 367 was the average. Some used over 600!
That was in 2022. It is way worse now.
What Does 367 Software Apps All Siloed Look Like?
Your Team Is Drowning in Data
With an average of 367 different apps in use across the enterprise, most of them operate in silos—meaning they don’t share data or context with each other.
40% of staff say they have just too much data for any human to be able to absorb or work with. (Reuters)
Drowning in Data
0%
34% of staff say there is just not enough hours in the day to properly analyze all of the data that they need to do their job. (Forrester)
Drowning in Data
0%
Gartner has reported multiple times that most employees spend .. Wait for it .. 3 to 3 and a half hours every DAY that’s every day .. Working with or analyzing data. (Gartner)
Hours Every Day
0 Hrs
77% of C-Suite admit they use dashboards and analytics that they know are wrong to make decisions (Gartner)
Hours Every Day
0%
Unfortunately, Any AI Inside An Application (eg CRM or ERP) Will Not Help
Any AI that lives inside a single app (say CRM or ERP or Finance Software) can only see a small fragment of the picture—limiting its usefulness and leaving most risks, opportunities, and insights hidden.
In Fact They Come With Huge Risks.
1. Blind Spots in Critical Decisions
Siloed AI lacks the full context of the business. For example, a CRM-based AI might recommend targeting a client for upsell—completely unaware that the same client has an unresolved service issue in Zendesk, a late shipment flagged in the ERP, or unpaid invoices sitting in Finance. The result? A recommendation that looks smart but backfires.
2. False Confidence
Siloed AI often sounds confident—offering precise answers, charts, or alerts—but it’s operating on incomplete data. This can lead executives and teams to make poor decisions based on partial truths, not realizing what’s missing until it’s too late.
3. Hidden Risk Exposure
Because each system is unaware of the others, siloed AIs miss compounded risks—the kind that only emerge when signals across departments are correlated. A sudden spike in returns, a drop in sales velocity, and a supplier delay might seem unrelated when viewed separately—but together, they point to a failing product launch. Only cross-system intelligence can detect and act on that pattern.
4. Duplicated or Conflicting Actions
When AIs in separate tools act independently, they can work at cross purposes. One AI might suggest a discount campaign to drive sales, while another flags margin pressure and recommends pulling back promotions—confusing teams and undermining coordination.
5. Missed Opportunities
Insights often live in the intersections—between Marketing and Sales, between Operations and Finance. Siloed AIs don’t operate in those intersections, so they miss the “unknown unknowns” that drive innovation, customer delight, or competitive advantage.
The Bottom Line:
Siloed AIs are not just underpowered—they’re dangerous when trusted to guide strategic decisions. Without a unified view across the enterprise, they create illusions of insight while leaving teams exposed to risk and blind to opportunity.
Sentia’s DIO was built to operate from a MetaLayer—watching across all systems, correlating all signals, and acting with full context. It’s the difference between tunnel vision and true executive intelligence.
will save 30% in costs, amplify returns 5 to 10 fold, and unlock new levels of productivity across your enterprise.