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Why Most AI Projects Fail And How Offshore Teams Build the Foundation for Success
Most AI projects fail because businesses lack the data, skills and operating model needed to support them. As RAND Corporation noted in its 2024 study of why AI projects fail, by some estimates more than 80% of AI projects fail, twice the failure rate of IT projects that do not involve AI. For many organisations, building offshore AI and data teams provides access to the specialist talent needed to implement AI successfully while controlling costs.
Why Do AI Projects Fail
Everyone is talking about what AI can do for your business. Far fewer are talking about what happens when you get it wrong. The pressure to adopt artificial intelligence is everywhere. Boards are demanding it. Competitors are announcing it. Consultants are selling it. And the excitement is not baseless: the businesses that implement AI well are reporting real productivity gains.
But here is the part that doesn't make it onto the conference slides. If the estimates cited by RAND are even close to right, much of the money enterprises are now pouring into AI is not delivering the value it was meant to.
The reason is rarely the technology itself. Most AI projects fail because organisations lack the data, specialist talent and operating model needed to implement and scale AI successfully.
While AI tools are becoming increasingly accessible, building the capability required to make them work remains a significant challenge. The estimates cited by RAND suggest that a large share of AI initiatives fall short of their intended business outcomes. The reasons vary, but four challenges appear repeatedly.
1. Poor data foundations: AI is only as effective as the data it is trained on. Many organisations attempt to implement AI using fragmented systems, inconsistent data sources and poorly governed information. The result is unreliable outputs, inaccurate recommendations and low trust in the technology. Before AI can create value, businesses need clean, structured and accessible data.
2. No clear strategy before deployment: AI should solve a clearly defined business problem. Too often, organisations invest in AI because competitors are doing so or because leadership feels pressure to innovate. Without clearly defined objectives, projects struggle to demonstrate measurable value and quickly lose momentum.
3. Skills and talent gaps: AI implementation requires far more than access to software tools. Organisations need specialists who can prepare data, design scalable systems, manage governance requirements and align technology with business objectives. Many businesses underestimate the importance of roles such as data engineers, data architects and AI strategists, and discover too late that talent shortages have become a major barrier to success.
4. Treating AI as a one-off project: Businesses that view AI as a one-time deployment often struggle to maintain value over time. Models require ongoing monitoring, optimisation and governance. As business requirements evolve and technology advances, AI systems must evolve with them.
A Common Pattern
Consider a business that deploys an AI assistant to handle its customer service chats at volume. If it puts cost first and cuts the people around the assistant, quality tends to fall on the conversations that need judgement, and customers still need to know they can reach a person. The assistant can handle the volume, but the business still needs people for the conversations where quality matters.
The Hidden Challenge: Accessing AI Talent
AI implementation is as much a talent challenge as a technology one. Demand for data engineers, data architects, machine learning engineers, governance specialists and AI strategists is high: PwC counted around 180,000 UK job postings requiring specialist AI skills in 2025, up from 112,000 in 2024. In our experience, UK organisations compete for a relatively small pool of experienced professionals, which pushes salaries up and makes hiring slow and retention hard.
The UK AI Talent Problem
The UK is, by many measures, a serious AI nation. The Government's AI Sector Study 2024 counts more than 5,800 AI companies, with London, the South East and the East of England home to around three quarters of them. AI-related employment in those companies grew 72% between 2022 and 2024. Workers with AI skills command a premium too: PwC's UK AI Jobs Barometer puts the average UK wage premium for AI skills at 34% in 2025, up from 11% the year before.
But the total number of people employed in AI-related roles across those companies sits at approximately 86,000. This is a thin layer from which to build the data engineering, strategy, architecture and ML talent that serious AI implementation demands.
In our experience, the result is a constrained, expensive, highly competitive talent market for the professionals your AI implementation depends on. We see businesses compete aggressively for the same small pool of people, bid up salaries and still find themselves with talent gaps that delay or derail projects.
When you rely exclusively on the UK market to build your AI team, you are fishing in a very small pond, and so is everyone else.
What Happens When You Offshore AI Capability?
Offshoring gives businesses access to a much larger talent market. We build teams in five hubs in four countries: Cape Town and Johannesburg in South Africa, Iași in Romania, Bengaluru in India and São Paulo in Brazil, at a substantially lower cost than equivalent UK hiring. The typical savings in each hub are in the table below.
India's AI talent pool is large and growing quickly. NASSCOM (August 2024) puts it at 600,000 to 650,000 professionals and expects it to exceed 1.25 million by 2027, while the Indian AI market grows at 25 to 35% a year.
Romania combines a deep engineering pool, around 200,000 ICT specialists by the European Commission's count, with labour costs well below Western European rates. Brazil leads Latin America in generative AI adoption: LinkedIn data analysed by INSEAD (September 2025) shows more than 110,000 Brazilian professionals listing GenAI skills, over three times as many as in Mexico. South Africa offers English-speaking capacity with strong data and analytics capability. It is one hour ahead of the UK in British Summer Time and two hours ahead in winter, so a 9 to 6 day there runs from 8am to 5pm UK time in summer and 7am to 4pm in winter, overlapping most of the UK working day.
These figures measure different things, so treat them as indicators of each market's depth rather than a like-for-like comparison with the UK's roughly 86,000 people in AI-related roles.
| Market | AI and tech talent indicator (measure varies) | Typical saving in our hub* |
|---|---|---|
| United Kingdom | ~86,000 people in AI-related roles (DSIT, 2024) | Baseline |
| Brazil (São Paulo) | Over 540,000 AI and digital professionals in Greater São Paulo (LinkedIn data via INSEAD, 2025) |
Operating costs typically 30 to 50% below the UK |
| India (Bengaluru) | 600,000 to 650,000 AI professionals; projected to exceed 1.25 million by 2027 (NASSCOM, 2024) | Salaries often 40 to 70% below the UK, especially for mid-to-senior roles |
| Romania (Iași) | Around 200,000 ICT specialists (European Commission DESI, 2023 to 2025) | Labour costs typically 40 to 50% below Western European rates |
| South Africa (Cape Town, Johannesburg) | Growing AI and data-analytics capability; English-speaking; working day close to UK hours | Labour costs typically 30 to 50% below the UK |
*Saving column: what we typically see in each of our hubs. The ranges are measured on different bases, so they do not rank the hubs against each other. Talent figures: DSIT, INSEAD Knowledge, NASSCOM, European Commission DESI. For how the hubs compare for technology and data roles, see the best offshore location for a technology team.
Why Offshoring Supports Successful AI Implementation
Offshoring has evolved far beyond cost reduction. Today, businesses use offshore teams to access specialist skills, accelerate growth and build scalable capabilities.
Larger talent pools: Countries such as Romania, South Africa, Brazil and India have developed strong technology ecosystems, producing highly skilled professionals across data engineering, software development, analytics and AI disciplines. Access to a broader talent pool helps you find the right expertise for your specific requirements faster and at a lower cost.
Better hiring decisions: When you can recruit across several deep talent markets rather than one, you go through a richer recruitment funnel. You are more likely to find the specific combination of skills, experience and cultural fit that your team needs. You are less likely to compromise on a second-choice hire because the market is thin.
Higher retention: The risk of building AI capability in a hyper-competitive domestic market is attrition. In our experience, hiring from a larger, less saturated pool improves retention. Professionals who secured a high-quality role with a UK-based or UK-facing business in a market with strong competition for those positions have a compelling reason to stay.
Improved continuity and knowledge retention: AI systems become more valuable as organisational knowledge accumulates. In highly competitive talent markets, frequent turnover can put intellectual property at risk. Maintaining continuity within specialist AI functions helps preserve critical expertise, improve execution and reduce the disruption and risk associated with staff attrition.
Cost efficiency and quality: Under the Embedded Offshore Team Model, offshore teams can often be established at a significantly lower cost than equivalent domestic hires, allowing organisations to build broader capabilities without compromising quality. Businesses can create more balanced teams that include the full range of skills required for successful implementation.
Building Lasting AI Capability
The organisations seeing the greatest returns from AI share a common characteristic: they focus on building capability rather than delivering isolated projects. They invest in:
- Strong data foundations
- Scalable infrastructure
- Specialised talent
- Governance frameworks
- Long-term AI strategies
Most AI projects fail because businesses focus on the technology before they build the foundation required to support it. It is the data engineers who ensure clean, well-structured data flows into your models. It is the strategists who ensure AI projects are solving real business problems. It is the architects who build systems that scale. It is the governance professionals who ensure you are managing risk and compliance as AI becomes more deeply embedded in your operations.
Successful AI implementation doesn't end with choosing the right tools or defining the right strategy. It also depends on having the engineering capacity to integrate AI into existing systems, build supporting applications and continuously refine solutions as business needs evolve. Learn how businesses are accelerating innovation by delivering their software development roadmap before the money runs out.
Key Takeaway for COOs
Businesses that build AI capability into their operating model with the people, processes and infrastructure to sustain and develop it, will compound their advantage over time. That compounding is what separates the minority who succeed from the majority who do not. Building that capability starts with a strong data foundation. For how to structure a data team that supports AI initiatives, read our 2026 guide to high-performing hybrid data analytics teams.
For many organisations, offshoring provides access to the specialist talent needed to build that foundation faster, more cost-effectively and at greater scale than domestic hiring alone can provide. If you are investing in AI, the question that matters is whether you are building the foundation that will make that investment pay.
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Explore Our SolutionsFrequently Asked Questions
Why do most AI projects fail?
RAND's interviews with 65 experienced data scientists and engineers found five root causes. The most common is a business problem that has been misunderstood or miscommunicated; the next is a lack of the data needed to train an effective model. A shortage of specialists such as data engineers makes both harder to fix.
What skills are needed for successful AI implementation?
Most organisations require a combination of data engineers, data architects, machine learning engineers, AI strategists and governance specialists to implement and scale AI effectively.
Why is the UK AI talent market a challenge for businesses?
Competition for AI specialists is intense, salaries are high and experienced people are in demand across many employers, which makes them hard to hire and hard to keep. The result is a constrained, expensive and volatile hiring market.
Which countries are popular for offshore AI talent?
India, Brazil, Romania and South Africa all have strong technology talent pools. We build data and software engineering teams in all five of our hubs, Cape Town, Johannesburg, Iași, Bengaluru and São Paulo, and Bengaluru has the deepest pool for AI and machine learning, data engineering and system architecture.
How does offshoring support AI implementation?
Offshoring provides access to larger talent pools, faster hiring, broader specialist expertise and lower operating costs, helping businesses build the teams needed to support AI initiatives.
How long does it take to build an offshore AI team?
In our experience, hiring typically takes four to six weeks in Cape Town, Johannesburg, Iași and Bengaluru, and four to eight weeks in São Paulo, from starting the search to the candidate accepting the offer. We see the UK equivalent take six to twelve weeks or longer. Notice periods and onboarding follow the offer.
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Sources: RAND Corporation | DSIT AI Sector Study 2024 | PwC UK AI Jobs Barometer 2026 | NASSCOM, Advancing India's AI Skills (2024) | INSEAD Knowledge (2025) | European Commission DESI, ICT specialists
Potentiam
Strategic Offshoring Consultancy, Potentiam
We are a London-based strategic offshoring consultancy that helps mid-sized companies scale by building high-performing, embedded offshore teams in five hubs across South Africa, Romania, India and Brazil. Our founders scaled EnergyQuote JHA to more than 300 people, about 60% of them offshore, before its acquisition by Accenture in 2015.
