Holistic AI & Data Strategy: Why 95% of AI Initiatives Fail

We're in the middle of an AI gold rush. Companies are pouring millions into AI tools, hiring data scientists, and chasing the latest models. Yet, this July 2025 MIT research report showed that 95% of AI initiatives fail to deliver financial returns. Why? They're building on quicksand. As our Head of AI Kalina Cherneva wrote in our Dreamix blog post on successful AI strategy, companies need a proper data strategy and a set of tangible metrics in order to succeed in AI business incorporation. 

We live in times when LLMs and AI tools have become so powerful in prototyping and MVP product development saving tremendous amounts of time, effort and costs. But even though many tasks that once took years now can be drafted in a day, this doesn't guarantee their long term success or good ROI. That's why we recommend starting with an AI Proof of Concept to validate your data readiness before scaling AI initiatives.

But even though many tasks that once took years now can be drafted in a day, this doesn't guarantee their long term success or good ROI. McKinsey reports that by mid-2025, 88% of organisations now use AI in at least one business function, up from just 56% in 2021 McKinsey & Company. However, the gap between AI adoption and AI value creation remains substantial. That's why one of the long term strategies which enables AI is data strategy.

The missing foundation: Data strategy

A comprehensive data strategy is a strategic framework that aligns an organisation’s data assets with its overall business goals, enabling informed decision-making and competitive advantage. It encompasses key components such as data modeling, business modeling, and business process modeling to ensure data remains consistent, accurate, and actionable across the enterprise. 

A robust data strategy emphasises holistic data management - integrating data from diverse sources through effective data onboarding and AI onboarding processes - to foster seamless data flow and utilisation. For B2B decision makers partnering with top software development companies, a well-crafted data strategy is essential for leveraging advanced analytics, enabling digital transformation, and driving innovative solutions that propel business growth.

In majority of cases for established companies, before you can extract value from AI in your company, you need to answer two fundamental questions:

1. What data goes through the company?

This would vary a lot from company to company. Financial and accounting data,  client interactions (emails, cookies, etc.), historical product prices - company and competitors, client feedback associated with different versions of product, etc.

For product companies, one of the most important pieces of data is product metadata - current and historical. Imagine an established retail shop which has been operating for years and now wants to innovate by adding a client support chatbot to their website, which would answer questions about company products. The quality of the chat bot would be proportional to the quality of data - how well and uniform products are described. 

Try to pay attention, set standards and ask people to be diligent - at least for new products. In a fast paced business environment the reality is, of course, messy. Business models, products change sometimes on a daily basis. We can help here to prep and transform your data, to find out the gaps and propose uniform data models which would work best with future AI solutions. 

Most companies do not realise that they generate data even within the company, outside the client/product context. AI and data-driven modelling of employee churn, offer acceptance rate, success rate of sales processes are some common to most business within-company processes which can be thought of as data. Of course, we do not want to collect data for its own sake. However, we ask questions about process improvement:

• How effective is my company at retaining top talent? How to answer: collect historical people data, model the probability of an employee leaving in the next quarter, year.
• How to optimise interview processes? How to answer: given a person's CV, seniority, position, requested salary, etc., predict probability of offer (of value X$) acceptance. Dreamix has been optimising interview processes and playing with those analytics.

So yes, data is the new virtual "gold" and it is abundant. It is a new way to think about business - the business process becomes a flow of data between different business units. In very complicated cases, Agentic AI workflows can help make sense of the complex data network. 

Sounds cool, right? Yes, it is! But it can be complicated and noisy. However, one needs to be smart about it, especially in bigger companies, since data ... costs money, which leads us to the second question.

2. How is data being processed?

When data moves within your company, it evolves, gets augmented, possibly enriched, and sometimes...it gets lost. And “lost” has many shades - from never recorded/stored and thus auto-deleted, to stored but without essential meta data which would allow for this data to be linked to meaningful business KPI later. 

Let’s break this down and identify the key best practices when collecting and processing data:

Proper data storage

Structured repositories, vector data bases, not scattered files. However, storing data is only half about how to store it. It needs to have all necessary meta and identifier columns needed to use if effectively later. Imagine storing individual client historical interaction data without keeping a client_id, which can be associated with the client information. The value of this data dramatically decreases. 

At Dreamix we recently had a client whose project data was scattered across different departments with varying presence of identifiers. And sometimes it was super hard or even impossible to match to the real project identifiers. We managed to “save” the majority of essential data, and here new LLM technologies sometimes can help a lot - for e.g. processing long unstructured documents and extracting key missing data pieces, the manual work for which would not be justified. 

Correct data versioning

Track changes, enable rollbacks. Data evolves with your business, in the general case you would start collecting more and more data, say, for the same client. Or you would incrementally introduce new tables which can be associated with your data. This is why it is important to know when and according to what methodology and assumptions (code version) the particular data piece has been recorded. 

The very interpretation of it depends on these assumptions. In the retail shop chatbot case from the above section, your clients could ask questions about products you no longer offer, but being able to answer about them would make a positive impact on your client impression. This requires you to have kept the decommissioned products associated with data. 

Appropriate data documentation

Schemas, lineage, business context. Try to make sense of every table, and data column. Define what it means, what to expect as type and values. 

Building with a scale in mind 

When choosing the platform and data architecture, ask yourself: what if this data becomes 10x, 100x, 1000x, 10000x in one year?

• How would the price change?

• How would the speed of queries change?

• What other tables would appear?

This mental arithmetic at best happens at every key business data flow changes or when a new paradigm is introduced: for example a new product or company strategy. 

Common challenges when developing a data strategy

B2B decision makers often encounter several key challenges when developing a data strategy, including:

• Data Silos and Fragmentation: Integrating data spread across multiple departments or systems can be difficult, hindering a unified view of information necessary for holistic decision-making.
• Data Quality and Consistency: Ensuring that data is accurate, complete, and standardised is a persistent challenge for most companies. Especially when they deal with diverse data sources.
• Technology Integration and Scalability: Aligning existing infrastructure with new data management tools and ensuring scalability to handle increasing data volumes can be complex and costly.
• Data Governance and Compliance: Navigating evolving regulations (like GDPR or CCPA) and establishing strong data governance policies to protect sensitive information is crucial yet challenging.

• Aligning Data Initiatives with Business Goals: Ensuring that data strategy efforts directly support and enhance overarching business objectives can be difficult, especially in rapidly changing markets.
• Change Management and Cultural Barriers: Fostering a data-driven mindset across teams and overcoming resistance to adopting new processes or technologies requires effective change management.
• Limited Resources and Expertise: Developing and implementing an effective data strategy often requires specialised skills in niche areas like data modeling, AI onboarding, and data governance, which may be scarce in-house.

Exactly the lack of enough technical expertise in-house is a primary driver for companies to partner with external vendors. Especially when it comes to AI project investments, outsourcing partnerships have been a trend for a couple of years. 

The chart below highlights a very interesting trend that’s bound to continue in 2026: a substantial 72% of outsourcing scope areas are focused on data and analytics. It underscores how critical data-driven initiatives have become for organisations aiming to stay competitive - especially in today’s rapidly evolving digital landscape. And, given the complexities and skills gaps many companies face, partnering with specialised AI development companies like Dreamix for data and analytics projects is not just strategic but often essential.

The bottom line

AI is cool, but when particular to your business solutions are built, their quality beyond the prototype, speed of implementation and easiness of support later on would be proportional to how well you have stored your data and built resilient pipelines and documentation.

We at Dreamix know it is a lot to process and think about. Our two key pieces of advice are:

1. Establish data as a strategic asset

Successful organisations treat data with the same rigor they apply to product development—as a valuable asset requiring investment, governance, and continuous optimisation.

2. Deploy specialised expertise to extract business value

Effective data monetisation requires domain experts who can architect, implement, and optimise AI applications aligned with your business objectives. At Dreamix, we deliver holistic data and AI solutions that transform organisations into data-driven enterprises. Rather than isolated tactical implementations, we build comprehensive frameworks that differentiate successful AI initiatives from failed experiments.

How Dreamix enables your data and AI success

Our comprehensive data strategy services include:

Data Assessment & Roadmap Development: We start by conducting a thorough audit of your current data landscape. Our teams evaluate your data sources, storage systems, quality issues, and gaps in documentation or governance. Based on this assessment, we develop a phased roadmap that prioritiсes quick wins while building toward long-term data infrastructure goals. 

Data Architecture Design & Implementation: Our experienced architects design scalable data infrastructures tailored to your business needs. Whether you need cloud migration, data lake implementation, or hybrid solutions across AWS, Azure, or Google Cloud, we build systems capable of handling exponential growth. 

Data Modeling & Business Process Alignment: We map how data flows through your organisation and align it with your business KPIs. Our teams create data models that reflect your actual business processes, not generic templates. This includes defining schemas, establishing data lineage, and connecting data assets to measurable business outcomes. For product companies, we pay special attention to product metadata that powers customer-facing AI applications.

Data Quality & Governance Frameworks: We establish the policies, standards, and technical controls that ensure your data remains accurate, consistent, and compliant. This includes implementing version control systems, documentation standards, data validation rules, and governance structures that satisfy GDPR, HIPAA, and other industry-specific regulations. 

AI Readiness & Implementation: Once your data foundations are solid, we help you capitalise on them. Our AI development teams build custom machine learning models, implement LLM-based solutions, and create agentic workflows that turn your data into competitive advantage. We've successfully delivered AI solutions across aviation, healthcare, transportation, and manufacturing sectors - always starting with proper data strategy.

Ongoing Optimisation & Support: Data strategy isn't a one-time project. As your business grows and technology changes, we provide continuous optimisation. Our teams monitor data quality metrics, identify new opportunities for data-driven insights, and scale your infrastructure ahead of actual demand. 

What makes Dreamix different:

• 20 years of experience building complex enterprise software solutions, including data analytics and AI solutions at scale
• 95% employee retention rate ensuring project continuity and deep knowledge of your systems
• End-to-end software development capabilities from data strategy through AI implementation
• T-shaped expertise combining deep technical skills with business domain knowledge across your industry
• Proven track record with industry leaders like Coca-Cola HBC, CERN, and VistaJet
• Agile POD teams focused on business outcomes, not just technical deliverables
• Transparent pricing models including fixed-price, time-and-materials, IT staff augmentation and dedicated team options