What It Really Takes to Get Value from AI: Highlights from Innovecs’ Live Session

On December 10, Innovecs hosted a live webinar, “AI Adoption Without Regret: How to Achieve Real Business Value and ROI,” bringing together delivery, engineering, and QA leaders to talk through what actually works in AI adoption — and what doesn’t. 

The session was moderated by Rocky Osborn, Chief Business Officer at Innovecs, and featured insights from:  

• Karyna Prykhodko, Delivery Manager;  

• Anton Shashuk, Senior Engineering Manager;   

• Iaroslav Gerasimov, Senior QA Automation Engineer.  

Together, they shared real project experiences, practical lessons, and concrete metrics that help teams move from AI experimentation to measurable business impact. 

The discussion combined real-world use cases, honest lessons from failed and recovered projects, and a clear focus on how to measure AI performance, risk, and ROI in production. 

Karyna Prykhodko noted that AI is still often treated as something teams can simply “turn on” and expect immediate results from. In reality, successful AI initiatives follow a full lifecycle: a clearly defined business problem, data readiness, controlled pilots, validation, and only then scaling. 

She emphasized that vague goals like “we need AI” usually lead to misaligned solutions and wasted effort. Without clear business objectives and success criteria, teams risk building technology that doesn’t solve a real problem. 

Anton Shashuk added that early alignment across business, data, and engineering teams is critical. When that alignment happens too late, AI becomes a technical experiment instead of a business tool.
The shared conclusion was simple: clarity at the start prevents costly corrections later.  

Anton Shashuk focused on why AI projects often exceed budgets even when the initial scope looks reasonable. The biggest cost drivers he called out were: 

• Data prep: Preparing usable datasets frequently takes far more time and effort than teams expect, sometimes accounting for a large share of the project timeline. 

• Demo vs. production gap: Tools that perform well in controlled scenarios can behave very differently once exposed to real data, real users, and real edge cases. Planning budgets around best-case performance, rather than realistic conditions, often leads to overruns. 

• Scope creep: In AI projects, even small changes can require retraining models or reworking data pipelines, making them more expensive than similar changes in traditional software. 

The advice was pragmatic: invest more time in discovery, test solutions in real conditions early, and plan budgets with buffers instead of optimism. 

Anton Shashuk shared a practical example where AI delivered clear value. A warehouse management system demonstrator that would normally take months to build was delivered in weeks by combining an agentic LLM with strong domain expertise. The gains came from speed, lower costs, and faster time-to-market, not from “AI magic,” but from clear goals, limited scope, and human oversight at every step. 

Yaroslav Gerasimov then balanced that with a cautionary case from QA automation. An ambitious attempt to build an end-to-end AI testing framework failed to meet expectations because the scope was too broad and the context too varied across projects. AI struggled when requirements were unclear, domains differed, and tasks weren’t sufficiently broken down. 

The takeaway was consistent: AI performs best when problems are well-defined, bounded, and decomposed. When clarity is missing, it tends to amplify confusion rather than resolve it. 

Karyna Prykhodko emphasized that many AI initiatives derail not because of technology, but because success is never clearly defined. Teams often focus on what AI can do instead of agreeing on what it should do for the business. Without shared success criteria, projects drift and surprises surface late. 

She stressed the importance of defining measurable business outcomes early, not just technical metrics like model accuracy. Cost savings, time reduction, or revenue impact need to be clear from the start. Continuous involvement from business stakeholders throughout development, not only at launch, was highlighted as a critical factor in staying aligned. 

Yaroslav Gerasimov added that neglecting QA early is another common risk. In AI-driven systems, late discovery of issues is especially costly, as retraining and adjustments take time and resources. Monitoring predictability, performance, and timing against planned milestones helps surface problems early, when they are still manageable. 

To sum up: clarity, early feedback, and continuous validation reduce both financial risk and reputational damage. 

When the conversation turned to metrics, Anton Shashuk stressed that ROI can’t be measured without a baseline. Without understanding the current state, teams have no reliable way to judge whether AI is improving anything or simply adding complexity. 

He outlined three layers of metrics: 

• System metrics show whether the AI is functioning as intended, including error rates, response times, and stability. 

• Operational metrics connect AI performance to process improvements, such as time saved or cost reductions. 

• Business metrics translate those improvements into outcomes leaders care about, like customer satisfaction, conversion rates, or time to value. 

Yaroslav Gerasimov highlighted early warning signs: 

• Missed milestones 

• Declining predictability 

• Performance that drifts over time 

When those signals appear, teams should pause, reassess assumptions, and involve both technical and business stakeholders to decide whether to fix, pivot, or stop. 

Karyna Prykhodko closed this part by emphasizing continuous visibility: 

• Shared dashboards 

• Regular discussions 

Together, these help teams interpret data, align expectations, and address problems before they turn into costly failures. 

After the main discussion, attendees were invited to ask questions directly to the panel. The Q&A reinforced many of the session’s core themes and brought them into very practical, real-world scenarios. 

Question 1: How do you convince executive leadership to invest in AI when there is skepticism around ROI and high failure rates?
Karyna Prykhodko explained that skepticism is healthy and should be addressed directly. Rather than asking for large investments upfront, she advised starting with small, focused pilots tied to specific business problems. These pilots should demonstrate measurable results within a short timeframe. She also emphasized speaking the language of business outcomes, not technology, and openly acknowledging AI failure statistics while clearly explaining how risks are mitigated through discovery, metrics, and phased delivery. 

Question 2: What tools can be used for data labeling?
Anton Shashuk noted that Innovecs is currently evaluating several tools as part of its AI framework. For enterprise use cases, SuperAnnotate was mentioned for its feature set and flexible pricing. For teams looking for open-source options, Label Studio was highlighted as a viable alternative based on positive industry feedback. 

Question 3: How do you check AI-generated code for originality and licensing risks?
Anton clarified that the focus is not on detecting whether code was generated by AI, but on identifying potential intellectual property risks. The approach involves scanning generated code against public and open-source repositories to flag potential licensing conflicts. Tools under evaluation include ScanOSS as well as GitHub Advanced Security for code scanning and matching. 

Question 4: How can teams reduce hallucinations in large language model outputs?
Yaroslav Gerasimov explained that hallucinations often occur when models are asked to handle overly broad or highly creative tasks. His recommendation was to narrow scope, decompose tasks, and clearly define responsibilities for each AI component. Simpler prompts and well-defined contexts reduce error rates significantly. Karyna added that usable data matters more than perfect data, and that iterative improvement based on real usage is key to stabilizing outputs over time.

We’d like to thank our speakers and everyone who joined the session live for the thoughtful questions and open discussion. The conversation highlighted how important it is to approach AI implementation with clarity, shared understanding, and realistic expectations. 

As promised, all registered attendees received the AI Metrics Handbook, a practical resource designed to help teams evaluate AI performance, track ROI, and make informed decisions at every stage of implementation. 

Our teams will keep sharing hands-on AI experience and creating space for open discussion. We’d be glad to have you join us again.