Only one in five AI projects succeed in creating real digital value.
The core issue isn’t the technology itself but how it is implemented. Many fail because a company’s culture and technological infrastructure isn’t ready to support the transformation.
When inflated expectations, weak infrastructure, and a culture resistant to change meet AI, a project is unlikely to thrive. Say a business invested $10 million in a vaguely planned, overhyped AI initiative and expected it to deliver 50% growth in its top line. Such expectations are doomed from the start.
Chasing hype isn’t the goal, but practical accuracy is. A good project begins with defining users’ needs and expectations and identifying cases where AI adds value.
To make your project a success, you need a solid AI implementation framework and strategy that realistically defines AI’s impact on existing processes.
Mature AI requires fewer experiments and more structure
Multimodal engines today fluently handle text, images, and audio. A model’s logical flow through interactions with users (called contextual coherence) is no longer a weak spot but a competitive battleground.
Back in 2023, AI chatbots supported call center representatives by synthesizing and summarizing large volumes of data, including voice messages, text, and technical specifications. They suggested responses to customer queries but remained reactive.
In 2025, AI agents converse with customers and plan follow-up actions like processing a payment, checking for fraud, or completing a shipping request. Gartner predicts that by 2028, 33% of enterprise software applications will incorporate agents to handle daily tasks.
As technology advances and businesses seek real digital value, the key is moving away from experiments toward building a solid AI architecture, thoughtfully weaving AI into product logic, workflows, and customer journeys.
4 pillars of AI implementation for digital value
AI should earn its place by strengthening business processes. Without clarity on a few key points, you risk amplifying the mess.
Get these must-haves right before adoption, and you’re halfway to success.
Know your goals
The best AI applications solve specific business problems. To move beyond lab experiments toward revenue growth and better experiences, scrutinize your products and processes. Where are the bottlenecks? Where are teams overwhelmed? Where can automation cut costs?
For example, a cloud-based contact center platform MaxContact, saw opportunities in call recordings, WhatsApp messages, and webchat data. AI helped them analyze conversations, identify key topics, and gauge sentiment, leading to improved customer experience through better operations.
Keep your data ready
Every successful AI project rests on refined data. Strong data enables accurate models, which drive better decisions. Yet data readiness is often the weakest link. Half of the respondents in Harvard Business Review’s research on data readiness for AI admitted their organizations struggle to unify data across silos.
In finance, fragmented, inconsistent data across risk systems slows progress. By first building a platform to monitor data quality, financial firms gain compliance and credibility while preparing for AI integration.
AI needs a human touch
AI doesn’t run itself. Behind every implementation is a team of engineers and architects who know how to build systems that create value.
In MaxContact’s case, third-party data scientists led research and solution development. Looking for a trusted partner, the company prioritized proven AI products, genuine communication, and invested in talent as the cost of entry.
A culture of change is essential
While employees are often technically well-versed in AI, their resistance to shifting processes or miscommunication often blocks performance.
Businesses should foster a culture that embraces change and treats experimentation as progress. Leaders should communicate the impact of AI, define roles, and train employees on its use and benefits. Ultimately, employees should feel valued and supported, not left behind.
Choosing the right AI technology
From startups to enterprises, choosing the right AI boils down to creating digital value. Rational business decisions start with defining problems and understanding the business context, not chasing hyped or larger models.
Several clients Coherent Solutions worked in finance, renewable energy, AI-enabled retail, and construction took this approach. They aimed at balancing business goals, existing infrastructure, and AI scalability potential and, as a result, got solutions that delivered real impact.
Data availability and quality
Technology that depends on large datasets works only if those datasets exist. In a finance project automating PDF-to-database migration, OCR was combined with LLMs to preserve original layouts of tables, images, and metadata. That was possible due to the sufficient, well-labeled historical PDF reports the client retained.
Feasible integration
When legacy systems are in place, new technology should strengthen them. For a renewable energy provider already running on AWS, we used AWS-native tools like SageMaker. They needed electricity usage predictions, so XGBoost model on AWS SageMaker ensured smooth integration with their forecasting systems.
Latency and performance
Retail demands speed. An eyewear company wanted a virtual try-on feature with real-time feedback. We deployed a custom GenAI approach based on DeepLearning networks (U-Net, GAN) fine-tuned on their data, cutting new design iterations from a month to under a week.
Investment and scalability
Some projects justify proprietary models, others thrive with open-source. A roofing client needed a self-hosted chatbot that avoided cloud APIs for cost and privacy reasons. Using open-source LLMs with LangChain and ChromaDB/Qdrant, we built a secure, cost-efficient chatbot fully under their control.
That’s what choosing the right AI technology looks like: Every decision is meaningful and tied to digital value creation.
The framework for value-driven AI adoption in action
Creating digital value requires applying AI to real business bottlenecks. Coherent Solutions’ no-hype framework is simple and practical: start small, validate, iterate, scale, and govern.
Here’s how it worked for one construction operator.
The path to digital value began with a familiar challenge: slow, error-prone blueprint analysis. Blueprints are the backbone of every construction project, yet architects and contractors spend hours manually counting doors, windows, and fixtures. Our team quickly managed to identify opportunities for AI.
With the pain point detected, the team started building a proof of concept using a YOLOX detection model to identify over 18 architectural symbols. Early tests reached nearly 92% accuracy, proving the idea was sound.
During the scale stage, we deployed the model via API into the core platform, cutting manual work and streamlining workflows for 500,000+ users nationwide.
Governance and risk mitigation hold every AI effort together, so a monitoring pipeline was built to track drift and performance, ensuring the system stayed reliable over time.
By moving thoughtfully through each stage, faster project estimates, higher accuracy, and a smoother customer experience were the outcomes that produced real digital value.
No-hype as the new AI standard
Modern business leaders are rethinking their assumptions. Whether it comes to a marketing assistant drafting an email sequence or an AI agent processing technical documents, model smartness comes second to output quality that meets business requirements.
Some AI teams turn straight to OpenAI’s APIs, others fine-tune open models like Meta’s Llama 3, and those with large budgets may build proprietary models from scratch. But the choice should never be anchored in hype. It should be navigated by the digital value you aim to create.
