Generative AI, chatbots, RPA, and AI-powered analytics—
According to HubSpot, “new tools and integrations are launching almost daily,” and McKinsey reports that hundreds of AI applications have emerged within just a few months.
Many organizations feel pressured to adopt AI solutions quickly to stay competitive.
But in reality, the challenges are often far more complicated.
The SaaS management platform Zylo warns:
“About 35% of enterprise AI subscriptions are not actually used, resulting in millions in wasted spending every year due to inactive subscriptions and overlapping contracts.”
So the critical questions are:
Which AI tools does our business actually need?
Which ones will truly drive productivity while controlling costs and security risks?
Below are not just popular names but tools proven to deliver measurable productivity gains in real business environments.
These tools can certainly boost productivity—but they also introduce real-world challenges.
GitHub Copilot helps developers write code up to 55% faster on average.
But according to The Verge,
“Not every developer experiences the same gains—some spend more time reviewing AI-generated code.”
A Stack Overflow survey found:
“Senior developers captured most of the productivity benefits, while junior developers saw much lower ROI.”
In many large teams, this led to efficiency improvements mostly concentrated among a small group of experienced engineers.
Images created with Midjourney and Stable Diffusion have already triggered copyright and trademark lawsuits.
Reuters reports:
“Ownership of AI-generated images remains unclear, and using them commercially can pose serious legal risks.”
Canva’s user terms state:
“Users are solely responsible for AI-generated content and must verify compliance with copyright and brand guidelines.”
One global marketing team was ordered by their headquarters to take down AI-generated social media posts for violating brand guidelines.
Samsung engineers uploaded internal code to ChatGPT, prompting an immediate ban due to security concerns.
Bloomberg reported:
“JP Morgan, Amazon, and Verizon also restricted internal use of ChatGPT over fears that sensitive data could be included in AI training.”
While ChatGPT API helped teams draft proposals faster, many companies found:
“Legal and security teams strongly warned that customer data might be stored on external servers.”
These examples point to three consistent worries most companies share:
“AI tools often feel like personal assistants.
Even with company subscriptions, not everyone uses them the same way, making ROI hard to prove.”
“When we upload data to AI services, we can’t always control where it’s stored or how it’s used.
That’s a real legal and compliance risk.”
“With each department managing subscriptions separately, we have no clear picture of total usage or spend.”
These concerns often lead to three specific risks:
✅ Hidden Costs & Redundant Subscriptions – Auto-renewed pilots and unused licenses
✅ Security & Compliance Risks – Exposure of sensitive or personal data
✅ Management Blind Spots – Inability to track usage or control spend centrally
Adopting AI isn’t about moving fast for the sake of it—it’s about having a clear strategy for purpose, security, costs, and governance.
🎯 1. Purpose-Driven Adoption
📊 2. Usage-Based Cost Management
🛡️ 3. Security & Compliance Readiness
🧩 4. Centralized Management
Sellease is an integrated platform that helps you manage SaaS, AI subscriptions, and IT assets in one place.
It enables your teams to track AI usage more easily and manage spending based on real data.
✅ Captures usage details for both installed and browser-based AI tools
✅ Provides reports on average usage by department and category
In the AI era, success isn’t about adopting every new tool—it’s about transparent management and gradual optimization.
Sellease can be a practical partner to help you stay in control.
