40% of ‘AI Startups’ Don’t Use Real AI— Indium Build AI that Actually Delivers
AI isn’t the problem. Credibility is—and it’s quietly becoming the biggest risk for enterprises claiming to be ‘AI-first’.
AI is no longer a futuristic concept—it’s the invisible layer shaping everyday experiences. From hyper-personalized recommendations on Netflix to frictionless online shopping, customers are constantly interacting with AI-powered systems. As expectations for speed, intelligence, and seamless experiences rise, enterprises are under intense pressure to keep up.
But in the rush to appear “AI-first,” many organizations are falling into the trap of AI-washing—relabelling traditional, rule-based systems as intelligent or autonomous to improve their ROI. In reality, a large portion of these solutions still depend on predefined workflows and human intervention, lacking true learning or decision-making capabilities.
According to a BBC report, 40% of new tech firms described themselves as AI start-ups wherein they were using no AI at all.
Industry reports suggest that a significant percentage of enterprise AI initiatives fail to move beyond pilot stages or deliver meaningful ROI. One of the key reasons? Overstated capabilities and underwhelming execution of transforming operations.
Customers become sceptical, stakeholders question investments, and genuinely advanced AI initiatives struggle to stand out in a crowded, noisy market. More importantly, it diverts attention and resources away from building AI systems that unlocks true transformation, improves decision-making, and translates into tangible business impact.
At Indium, we see this gap play out across enterprises every day—ambition outpacing actual AI capability.
If enterprises want to unlock real value from AI, the shift needs to move from “looking AI-ready” to “being AI-capable.” Anything less risks slowing down not just individual progress—but the evolution of AI-driven transformation as a whole.
Why AI-Washing is a Critical Bottleneck to Enterprise Growth
AI-washing isn’t just a marketing misstep—it’s a systemic risk. When enterprises disguise conventional systems as AI-powered, the consequences ripple across customers, employees, and business performance.
1. Illusion of Transformation, No Real Outcomes
Enterprises overpromise AI capabilities but underdeliver in execution. The result? Wasted investments, stalled initiatives, and little to no measurable business impact, eroding both ROI and customer experience.
2. Rising Employee Burnout
When “AI-powered” systems still rely heavily on manual intervention, the burden quietly shifts to employees. Teams are forced to compensate for gaps in automation, leading to increased workload, frustration, and ultimately higher attrition.
3. Erosion of Trust Across the Ecosystem
AI-washing doesn’t just hurt individual organizations—it damages the entire market. As more enterprises fail to justify their AI claims, customers grow sceptical, stakeholders hesitate, and even genuinely advanced solutions struggle to gain trust.
AI-washing doesn’t just slow progress—it actively blocks it.
Why Are Service Providers Falling into the AI-Washing Trap?
Despite the risks—legal, reputational, and operational—AI-washing continues to rise. It’s not accidental; it’s driven by a mix of market pressure, hype cycles, and competitive urgency.
Here’s what’s really fuelling it:
1. AI as a Buzzword Economy
In an overcrowded market flooded with AI platforms and solutions, some service providers capitalize on this by positioning legacy or rule-based systems as AI-powered, without embedding real intelligence that truly performs.
2. Investor Pressure & Valuation Games
Capital is flowing toward companies that appear AI-ready, creating a dangerous incentive—inflate AI maturity to attract funding, leading to overstating capabilities and make solutions appear more future-forward than they actually are.
3. Shortage of AI-Expertise
While AI adoption is accelerating, true AI expertise remains scarce. As a result, organizations often take shortcuts, relying on superficial implementations or overstating capabilities instead of investing in deep, sustainable AI proficiency.
4. Market Expansion & Fear of Missing Out
AI is no longer industry-specific—it’s everywhere. From banking and healthcare to retail and gaming, AI use cases are expanding rapidly.
This cross-industry relevance triggers a strong FOMO effect, pushing enterprises to jump on the AI bandwagon—even when their capabilities are not mature enough.
5. Hyper-Competitive Positioning
The AI services market is fiercely competitive, by overstating capabilities, organizations aim to expand their pipeline, win large deals, and signal technological leadership—even if the underlying systems don’t support those claims.
Ultimately, service providers are failing to understand that customers don’t need more “AI-powered” claims—it needs AI that actually works. AI that actually transforms.
Detecting AI-Washing: Key Red Flags to Watch For
With a flood of “AI-powered” solutions in the market, identifying genuine is becoming increasingly difficult—and critical. Not every product labelled AI is truly intelligent.
Here’s how to cut through the noise and identify AI-washing:
1. Vague Claims, No Proof
Authentic AI solutions are backed by clear data, real evidence, measurable outcomes, and defined use cases not confusion or ambiguity.
2. No Clear Model or Architecture
Any enterprise deploying AI—whether proprietary or open-source—should be able to explain what models are being used and how they function, a lack of transparency is always a red flag.
3. Heavy Reliance on Manual Intervention
True AI systems are designed to learn, adapt, and automate decision-making overtime. If it relies on rules—not learning—it’s not AI.
4. Marketing-Led, Not Technology-Led
Organizations with genuine AI capabilities lead with technology depth, use cases, and outcomes—not just polished messaging.
Wondering if your AI is real or just rebranded automation?
Let’s assess it.
Indium’s AI: Beyond Simple Automation, Towards True Intelligence
At Indium, we take a clear and uncompromising stand against AI-washing and don’t repackage legacy systems with AI labels or chase short-term hype for quick wins.
Every claim we make is backed by real-world use cases, measurable outcomes, and tangible business impact.
Our AI solutions are powered by agentic architectures, advanced models, and modern frameworks designed to solve complex enterprise problems with speed, precision, and accountability—without compromising sensitive data.
We ensure our AI systems are not static but continuously evolving. Through techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), our models are aligned with enterprise goals and refined for real-world performance. With built-in feedback loops, these systems learn, adapt, and improve over time—because true AI doesn’t just operate, it evolves.
At Indium, data privacy and security are foundational. With robust guardrails—including audits, red teaming, and prompt injection safeguards—we ensure every deployment is secure, compliant, and enterprise-ready. Because businesses only enjoy AI when it delivers value within the framework of governance and control.
From Hype to Real Intelligence: Introducing- The Lifter
Built by Indium’s AI engineers, The Lifter is an Agentic AI platform designed to deliver what most “AI solutions” only promise—a complete understanding of your systems before actual transformation begins.
It doesn’t just automate tasks. It understands how your business actually works—across applications, data, and workflows.
Most transformations fail not because of lack of intent—but because of lack of understanding. That’s exactly where The Lifter changes the game:
- Decoding Legacy Complexity
The Lifter reverse-engineers legacy code to build system intelligence—because 58–70% of developer time is spent understanding code, not writing it.
- Preserving Data Meaning During Migration
Data loss isn’t just about missing records—it’s about losing context. With 83% of data migrations failing or exceeding budgets due to lost meaning, The Lifter ensures context-aware data transformation, not just movement.
- Eliminating False Confidence in Testing
Traditional testing often gives a false sense of security—72% of automated tests produce false positives. The Lifter goes deeper, validating systems beyond “green signals” to ensure they are truly production-ready.
In the end, the real winners in the AI game won’t be the ones who adopt the fastest but the ones who build it right and ethical.
See how The Lifter decodes your systems before transformation begins.
Book a 30-min assessment.
