The three biggest misconceptions about AI in business heading into 2026

Experts building custom AI solutions for critical industries explain why conventional wisdom misses the mark

VILNIUS, LITHUANIA, February 3, 2026 /EINPresswire.com/ -- Businesses that are waiting for “smarter AI” to solve their problems are running out of time. According to experts building enterprise AI systems, the real bottleneck is not better models, and firms that don't recognize this will find themselves left behind by 2027. AI and custom software engineering powerhouses working with Europe’s leading banks, energy firms, and manufacturers, identifies three critical blind spots in mainstream AI beliefs.

Blind spot #1: Delaying AI adoption in expectation of better models

"For several years, companies have been waiting for the next model release to finally make things work," says Pranas Sileika, CEO of Softeta. "New GPTs didn't solve it. Neither did Claude models, impressive as they are for software engineering. Neither will whatever ships next quarter. The bottleneck was never the model."

The core issue: AI systems lack visibility into how decisions get made inside organizations. An AI handling an invoice exception needs to know what triggered it, who submitted it, and what happened the last time this vendor had an issue.

"Your CRM stores that a deal closed," says Justas Selenkovas, Softeta's Chief Business Development Officer. "What it doesn't store is that you were the second choice. Or that the winner already had one key feature you're only shipping in six months. Without that history, the system is just guessing. And an AI that guesses is worse than no AI at all, because at least a human would have figured it out."

As a result, the companies making progress are capturing decision logic. Documented decisions become data for ingestion and inference, and captured exceptions become precedents the system learns from.

Sileika's test: "If a new hire asks why you approved that exception last year, and the answer is 'ask John, he might know more about this' – you're stuck."

Blind spot Nr.2: Treating chatbots and dashboards as the primary AI entry point

According to experts, the copy-paste workflow that characterizes most ChatGPT use – copying context, pasting into chat, waiting for output, pasting back – limits what AI can learn about organizational patterns.

"The next wave of AI looks nothing like a chatbot," says Martynas Dadurka, Softeta's Chief Innovation Officer. "The winning AI experiences will be seamless invisible integrations into systems people already use."

Today, many enterprise AI implementations end up as chatbots coupled with dashboards that show metrics pointing to issues teams already know. What organizations need instead is problem-solving rather than problem-surfacing.

“When a deal approval sits too long, an effective AI solution notices it, escalates automatically, and includes enough context for an informed decision without requiring someone to ask around and dig through multiple systems,” says Selenkovas.

Blind spot Nr.3: Full automation isn’t as close as many think

Code generation and data analysis represent effective AI applications because outputs are verifiable. Code compiles or it doesn't, numbers reconcile or they don't. But most business work is different: pricing calls or customer negotiations depend on context that is not documented.

"Full AI autonomy is the future vision, but human oversight is the current reality," Dadurka says. "Today, you can't reliably prove AI’s output is correct, so you can't safely automate it. The companies that understand this design for 80-90% AI handling. Humans make the final 20-10% decisions, providing valuable data to AI for more automated operations over time.”

Under this model, AI handles research, cross-referencing, routing, and follow-up. The human role shifts from processing work to making judgment calls, fewer in number but higher in stakes. Each captured decision improves the system, gradually expanding what AI can handle without human participation.

What separates winners from the rest

Technology executives who recognize these blind spots are taking concrete steps: modernizing legacy systems and establishing integration points across data pipelines. Those who continue waiting for better models or chasing full automation risk falling further behind.

"Better models won't help if your company's decision making process is trapped in people's heads," Sileika says. "Write those decisions down, expose them to AI, and the automation you’ve been waiting for starts working."

Softeta is a European artificial intelligence and custom software engineering company with three R&D hubs in Poland and Lithuania. The company builds software solutions for critical operations in finance, energy, manufacturing,

Zivile Barkauskaite
UAB Softeta
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