--- title: "BCG Says Only 10 to 15% of Services Firms' AI Talent Does Applied AI. What Should Buyers Ask?" slug: "bcg-applied-ai-talent-technology-services-buyers" author: "ibl.ai Engineering" date: "2026-11-01 13:00:00" category: "Premium" topics: "applied AI talent, BCG, technology services, forward-deployed engineers, AI services buyers, AI workforce, AI implementation" summary: "BCG found applied AI is 10 to 15% of the AI-tagged workforce at technology services firms against 90 to 95% at AI-native firms. Its conclusion is a talent decision, not a shortage, which gives buyers specific questions to ask." banner: "" thumbnail: "" linkedin: | It is tempting to shorten BCG's new talent research to "only 10% of engineers do applied AI." The reported finding is narrower and more useful. Applied AI is 10 to 15% of the AI-tagged workforce at technology services firms, against 90 to 95% at AI-native firms. Forward-deployed roles are around 45% of applied AI talent at AI-native companies and under 5% at services firms. The money needs the same care. The AI services opportunity by 2030 is reported as $100 to 200 billion net. About $200 billion is what AI-led work is expected to displace from existing contracts. BCG's conclusion is the part I would underline. Its Sreyssha George: "Technology services firms do not have an AI talent shortage; they have a talent decision to make." If you buy services, that decision lands on your project. Ask who is actually on it. Ask how many of them will sit with your systems. Ask what you keep when they leave. #iblai #AppliedAI #AIStrategy #EnterpriseAI #ForwardDeployed --- ## The Short Answer **BCG reports that applied AI is 10 to 15% of the AI-tagged workforce at technology services firms, against 90 to 95% at AI-native firms, and calls this a talent decision rather than a shortage. Buyers should ask who is staffed on their project and what stays behind. With ibl.ai you own all the code and the data when the engagement ends.** Shortening this to "only 10% of 50,000 engineers do applied AI" gets the finding wrong. The real one tells a buyer more, because it separates AI skill on a CV from AI work that reaches production. ## What did BCG actually find about applied AI talent at services firms? BCG found that most AI-tagged people at traditional services firms work on infused AI, meaning AI added to existing work, rather than on applied AI. [IANS reporting](https://www.thehansindia.com/business/ai-led-services-likely-to-create-100-200-billion-opportunity-by-2030-1137074) on the October 29, 2026 report puts infused AI at 85 to 90% of the AI-tagged workforce there. The analysis [drew on more than 2,500 AI-tagged job postings and over 50,000 professional profiles across more than 15 firms](https://capitalinformation.net/ai-to-create-100-200-billion-net-opportunity-in-tech-services-by-2030-says-bcg/). The report is BCG's [The Talent Is Already on Your Payroll. The Clock Isn't.](https://www.bcg.com/publications/2026/india-talent-is-already-on-your-payroll) We read its public summary; the detailed figures here come from press coverage. Note the denominator. The 10 to 15% is a share of the AI-tagged workforce at these firms, not of all engineers and not of the 50,000 profiles. Note the scope too: BCG files it as a local article under an India URL, so check the sample before applying it everywhere.
MeasureTechnology services firmsAI-native firms
Applied AI share of AI-tagged workforce10 to 15%90 to 95%
Forward-deployed share of applied AI talentUnder 5%Around 45%
## Is the applied AI bottleneck a talent shortage? BCG says the bottleneck is not a shortage. Sreyssha George, a BCG managing director and partner, is [quoted saying](https://capitalinformation.net/ai-to-create-100-200-billion-net-opportunity-in-tech-services-by-2030-says-bcg/) that "technology services firms do not have an AI talent shortage; they have a talent decision to make," and that "the people who can deliver applied AI are largely already on the payroll." The report identifies 40 distinct applied AI roles, each linked to an existing technology role. Forward-deployed engineers, semantic engineers and AI operations engineers are among them, as reported. The moves are not equally easy. Going from data scientist to forward-deployed engineer is reported as roughly three times the effort of going from data engineer to AI data engineer. Some of that reskilling is quick, and the part closest to customers is not. ## How big is the AI services opportunity BCG describes? The opportunity is reported as $100 to 200 billion in net new work by 2030, not a flat $200 billion. AI-led services are expected to add $300 to 400 billion of growth, offset by around $200 billion of existing contract revenue. So $200 billion is closer to the amount AI-led work takes away than to the prize. The reported services market grows from $1.6 to 1.7 trillion in 2025 to $2.2 to 2.4 trillion by 2030. That shift explains the incentive problem for buyers. George says clients are moving from paying for hours to paying for outcomes, which changes how work is staffed and rewarded. A firm still paid for hours has a reason to move slowly. ## Why should a buyer care about forward-deployed share? A buyer should care because forward-deployed share is a rough proxy for how much of a firm's AI talent sits next to real systems. Around 45% against under 5% is a large gap, and [the forward-deployed model](/blog/karp-ai-sales-last-mile-forward-deployed-engineers) exists because the hard work is local. Connecting an agent to your identity system, your records and your permissions cannot be done from a slide. It takes someone reading your schema and sitting with your team. BCG's data does not prove that services firms do this badly, only that few of their applied AI people carry that role. ## What should a buyer ask an AI services firm before signing? A buyer should ask five questions that turn a capability deck into something checkable. 1. Name the people on our project. How many hold applied AI roles rather than infused AI roles? 2. How many will work inside our systems, and for how long? 3. Are we paying for hours or for a defined outcome with acceptance criteria? 4. Which deliverables do we keep: source code, infrastructure definitions, evaluation harnesses? 5. Who at our organization can run this without you, and when? The last two matter most, because hiring a services firm moves the talent gap rather than closing it unless skills and code stay behind. ## How does ibl.ai work with a buyer's own engineers? ibl.ai engineers build alongside your team, and the code stays with you. With ibl.ai you own all the code and the data, on your infrastructure, model-agnostic across any LLM, with no per-seat pricing. [Forward-deployed engineering](/service/ai-transformation) at ibl.ai delivers connector source code, infrastructure manifests, policy configuration and evaluation harnesses, so your team can run and extend the system after handover. That matters for public buyers too, where [owning code is only useful if someone can operate it](/blog/sovereign-ai-code-ownership-vs-government-equity). ibl.ai is family-owned and operated from New York, NY. *Source: figures and quotes are from press coverage of [BCG's report](https://www.bcg.com/publications/2026/india-talent-is-already-on-your-payroll), including [IANS via The Hans India](https://www.thehansindia.com/business/ai-led-services-likely-to-create-100-200-billion-opportunity-by-2030-1137074). We read BCG's public summary, not the full report, so check it for exact definitions before citing these numbers.* ## Why does owning the AI stack matter? **ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.** - **You own all the code and the data.** Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours. - **Model-agnostic.** Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform. - **No per-seat pricing.** Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ. - **Deploy anywhere.** Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.