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Explore the key AI consulting and adoption trends defining 2026, from enterprise-scale deployment and governance to human-centered, value-driven AI systems.

In early December 2025, Yahoo News rolled out an AI-powered audio news format called Your Daily Digest - an on-demand, personalized audio summary of the day’s top news stories that users can listen to on their phones from midday through the afternoon. This launch builds on a morning edition introduced earlier this year and reflects a broader media trend where AI is reshaping how audiences consume news by making it more accessible, timely, and tailored to individual interests. Yahoo’s product combines editorial curation with machine-driven recommendations, producing short, six-to-eight-minute summaries that reflect each listener’s preferred topics, from current events to entertainment and lifestyle. According to Yahoo’s VP of Product, this move is driven by the fact that two-thirds of Americans now listen to on-demand audio content, and publishers must evolve to meet audiences wherever they are - whether that’s driving, working out, or transitioning between tasks. This isn’t an isolated experiment. Like other useful AI solutions that bring businesses to more effective functioning, audio news formats powered by generative AI are gaining traction across the industry. Major publishers like TIME and Business Insider also launched AI-assisted audio briefings this year, underscoring a shift toward multimodal news experiences where reading, watching, and listening coexist. One industry commentator summed it up well: “Audio is a core piece of our evolution… whether you’re watching, listening or reading.”

In early December, Anthropic and Accenture announced a multi-year partnership designed to embed Claude - one of the world’s leading AI models into enterprise workflows at scale. While the headline alone might resemble many other “AI partnership” announcements, this one marks a turning point. Not because of the model. Not because of the consulting brand.But because of what it signals about how enterprises are finally choosing to adopt AI: seriously, systematically, and at scale. According to Reuters and the Wall Street Journal, the partnership includes the creation of a dedicated business group and the training of 30,000 Accenture professionals specifically on Anthropic’s models. Anthropic’s leadership called it “a step toward safe, enterprise-ready AI,” while Accenture emphasized that clients no longer want pilots — they want integrated, production-grade capabilities they can deploy across business lines.

Design today sits at the crossroads of technology, psychology, and rapid innovation, and few roles capture that complexity as clearly as UX/UI design. At YTC, where AI is woven into nearly every stage of product development, the designer’s ability to combine human insight with intelligent tools becomes essential. We spoke with Yelyzaveta Moiseieva, UX/UI Designer at YTC, about her vision of user experience, how AI has transformed the design process, and why true expertise still begins with understanding people. - What does UX design mean to you? Why did you decide to make it your profession?

Where to search for the professionals and how to manage the team: interview with YTC COO Oleksandr Maryniuk

Artificial intelligence has become a boardroom fixture: investments are soaring, pilots are proliferating, and the promise of transformation is everywhere. Yet as the BCG research shows, the vast majority of organisations are not realising value. Only about 26 % of companies have developed the capabilities needed to move beyond proof-of-concept and extract measurable value, and just 4 % are at the frontier of AI-driven transformation. What this tells us is that the technology itself is not the barrier. The obstacle lies in people, processes, integration, governance, and culture. Why people and processes matter more than algorithms Among the most striking findings of the report is the so-called “10-20-70” distribution of focus for successful AI transformations: approximately 10 % of effort is directed at algorithms, 20 % at technology and data, and a full 70 % on people and processes. This may seem counterintuitive in an era of hype around models like LLMs and generative AI — yet the empirical evidence speaks clearly. Companies that invest heavily in people, workflows, change management, process redesign, and the human embedding of AI outperform those that focus primarily on hard tech. Even building a team using AI is more efficient now.

In the fast-evolving world of artificial intelligence, knowing what to build first often matters more than knowing how to build it. Many companies rush into AI adoption with excitement — exploring use cases, generating ideas, and visualizing automation opportunities — only to face a hard question soon after: which project deserves priority? Should they start with a chatbot, an analytics dashboard, or a predictive model? Each idea promises value, but not all of them are equally feasible, actionable, or impactful. At YTC, we believe that successful AI adoption doesn’t begin with coding — it begins with clarity. That’s why one of the most effective tools we use in our consulting practice is the AI Bubble Chart: a structured, visual method for prioritizing AI initiatives based on business value, feasibility, and actionability. It’s more than a chart — it’s a framework for making data-driven strategic choices that balance ambition with practicality. Let’s explore how AI Bubble Charts work, why they make sense for business leaders, and how they turn abstract AI ideas into a clear roadmap for growth.

The excitement around artificial intelligence has shown no signs of slowing. Every week brings new headlines of breakthroughs, new models, and new tools. Yet despite this hype, value remains elusive. According to BCG’s extensive research, while upwards of 98 % of companies are experimenting with AI, only around 26 % have developed the capabilities to move beyond proof of concept and generate measurable value. Even more striking: just 4 % of companies have achieved the kind of systematic AI-driven transformation that the consulting firm classifies as leadership. These numbers aren’t just statistics — they’re a warning. Investing in AI without the right foundation is like building on sand: visible, exciting, but unstable. Yet the story does not end in caution. The same report outlines what the top companies are doing differently — and offers a blueprint for how your business in finance, travel, media, coaching, or news/disinformation can break through the barrier from “pilot” to “productised value.” At YTC, we’ve absorbed those lessons into our deep and wise AI consulting through T-Shaped research approach and solution-development methodology. Let’s walk through the critical differentiators and how you can apply them. 1. Focus on core processes and value generation, not just experiments

Artificial intelligence is dramatically reshaping how companies operate — yet most organizations still struggle with the same challenge: Where do we start? Choosing a random use case and hoping it works is no longer an option. Real value from AI comes only when a business understands the full landscape of opportunities — and then focuses its efforts on the one that can drive the biggest impact. At YTC, we call this approach T-Shaped research. It allows us to look broadly across a company's processes and industry to identify every meaningful AI opportunity — and then dive deeply into developing and scaling the one with the highest strategic potential.

Artificial intelligence has made enormous progress in reasoning, problem-solving, and automation — but even the most advanced AI systems still rely on how we talk to them. A new study, “LLMs Learn Better When They Think First” (AlphaXiv, 2024), challenges the standard way we structure prompts and proposes a deceptively simple idea: instead of telling AI to “answer first, explain later,” we should encourage it to reason before responding. At first glance, that might sound like semantics. But the paper’s findings reveal a consistent and measurable performance boost when large language models (LLMs) are prompted to explicitly reason through a problem before generating the final answer. This structured approach mirrors how humans think — laying out assumptions, exploring possibilities, and validating reasoning before committing to a conclusion. The traditional workflow in AI interaction often looks like this:

Artificial intelligence is transforming the way businesses approach consulting — and at YTC, we’re taking that transformation one step further. Our new project, Hekira, aims to automate and enhance the discovery phase of AI consulting, helping companies uncover the most impactful ways to integrate AI into their workflows faster than ever before. We spoke with Danylo Vorvul, Head of AI at YTC, about the mission behind Hekira. This technology powers it, and it’s redefining what effective consulting looks like in the age of intelligent automation. – How would you describe the purpose and main mission of the Hekira project?

Artificial intelligence is no longer a futuristic advantage reserved for tech giants — it’s a working instrument that reshapes how businesses plan, execute, and measure marketing strategies. From finance and travel to media, coaching, and even the fight against disinformation, AI-driven marketing has become a key to understanding audiences, optimizing budgets, and achieving growth faster and smarter. What once required weeks of brainstorming, research, and testing can now be done in hours — and with better precision. But the power of AI lies not in replacing human creativity, but in enhancing it. When used strategically, AI helps teams make data-backed decisions, find hidden opportunities, and create campaigns that resonate deeply with real people. So, how exactly does AI transform the process of marketing strategy creation? Let’s walk through the main stages — and explore how businesses can use it to build more effective and adaptive strategies.