The Most Advanced AI Companies in the World Just Hired Humans
Technical Consultants in the Era of AI
Last updated: September 16th, 2026
5min read
By Carlos A. Delcid
Why this matters if you are a US-based Seed to Series B SaaS CEO, COO, or CTO with AI in your product
Most AI adoption failures are implementation failures, not technology failures.
Customers who never fully deploy your AI cannot see its return, and customers who cannot see the return cancel.
The most valuable AI companies in the world have added a human deployment layer. The pattern is not optional anymore.
The question is no longer "how good is our model?" It is "who makes sure the customer gets value from it?"
ABOUT
67%
Success rate with a specialist implementation partner, vs one third alone. MIT, 2025.
95%
Of enterprise AI pilots delivered no measurable return. MIT, 2025.
87%
Of customers say human access is essential when AI handles service. Gartner, 2026.
AI didn't remove humans from the equation. It raised the bar for what they need to know. The companies winning with AI are the ones who built the human layer to make it work.
The problem is not the model. It is the last mile.
Every AI company has the same demo now. The model summarizes, drafts, predicts, automates. It looks like magic in the sales call.
Then the customer tries to use it inside their real business, with real data, real workflows, and real people. And something breaks. Not the model. The adoption.
The numbers are hard to ignore. MIT's State of AI in Business 2025 report found that 95 percent of enterprise generative AI pilots delivered no measurable financial return. And S&P Global found that 42 percent of companies abandoned most of their AI initiatives in 2025, with unclear business value among the leading causes.
Companies are not abandoning AI because it does not work. They are abandoning it because nobody helped them make it work. MIT's researchers called it a learning gap. We call it the last mile. And the last mile is where retention lives.
When customers never reach adoption, they never experience the value they were promised. And when they do not experience the value, renewal becomes a pricing conversation instead of a results conversation.
What the winners figured out
The same MIT report found that companies who bought AI solutions from specialized vendors and built real partnerships succeeded about 67 percent of the time. Companies that tried to build and adopt on their own succeeded roughly one third as often. The difference was not the technology. It was having someone on the other side of the table who knew how to implement it.
The most valuable AI companies have already acted on this. Palantir built its business on forward deployed engineers: technical people who sit inside the customer's operation and make the software work for that specific business. OpenAI and Anthropic borrowed the playbook. Job postings for forward deployed engineers grew more than 800 percent between January and September 2025. In May 2026, OpenAI formed a dedicated deployment company to do this at scale.
The companies with the most advanced AI on the planet decided the model alone was not enough. They needed a human layer to translate what the model can do into what a customer actually needs.
If that is true for them, it is true for every SaaS company shipping AI.
The chatbot ceiling
There is a temptation to solve the adoption problem with more AI. A chatbot for onboarding. A knowledge base for implementation. A video series for training.
Klarna tried that. In 2024 the company replaced a large part of its customer service team with an AI assistant. By mid-2025 the CEO was publicly reversing course and hiring humans again. His words: "we went too far." Customer satisfaction had dropped, and the company learned that customer relationships are not just information processing. They are trust management.
Customers say the same thing directly. A 2026 Gartner survey found 87 percent of customers consider access to a human essential when a company uses generative AI for service. And 85 percent of service leaders report they are expanding human agent responsibilities, not shrinking them.
Simple questions can go to a bot. Complex implementations cannot. Nobody wants to configure a mission-critical AI workflow by watching a video and hoping.
Human-led, selling to humans
We are still a human-led business, selling to humans, building products that humans will use. AI has not changed that. It has changed what the humans need to be good at.
The person who owns your customer relationship in 2026 cannot just be a friendly account manager who checks in quarterly. And they cannot just be an engineer who ships tickets. They need to be both, and something more.
Technical depth. Enough to understand the customer's stack and get their hands into the implementation.
AI fluency. Not as a buzzword but as a daily tool, so they can show the customer what good looks like.
Human skills. Patience, teaching, and the ability to explain in plain language what the return on investment actually is and where it is showing up in the customer's business.
That is the technical consultant in the era of AI. Not an account manager with more product knowledge. Not an engineer who occasionally joins customer calls. A hybrid role whose job is turning product capability into customer adoption. The person who makes sure the customer sees the value, because a customer who sees the value does not churn.
Three questions before you scale your AI product
1. Who owns the last mile?
Not the sale, not the support ticket. The implementation. If the answer is "the customer figures it out," that is your churn risk.
2. Can your customer-facing team explain your ROI in plain language?
If your AI saves a customer eleven hours a week, someone on your side should be able to show them that number and where it came from. If nobody can, the customer will assume the answer is zero.
3. Are you hiring for one skill when the role needs three?
Technical depth, AI fluency, and human skills. The engineer without people skills and the account manager without technical depth both fail at this job. Hire for all three, or design the team so they cover each other.
Where Puzzle fits
At Puzzle, we build Advanced Support teams for SaaS companies: high-skill implementation specialists, technical consultants, and customer success professionals from Latin America who are embedded in your team and own the customer relationship from kickoff to adoption.
Over the last year, the request from our partners has changed. Nobody is asking for people to answer tickets. Every partner is asking for the same profile: technically strong, AI-forward, and able to run an implementation while keeping the relationship warm. That is the profile we recruit, vet, and develop.
What that looks like in practice:
Defining the technical consultant role around your product, your customers' stack, and your onboarding milestones
Vetting for all three skills: technical depth, daily AI fluency, and the communication ability to explain ROI to a non-technical buyer
Embedding Puzzlers into your tools, rituals, and customer conversations so they operate as your team, not a vendor
Time zone overlap with your US customers, so implementation questions get answered the same day
Managing sourcing, payroll, benefits, equipment, and secure infrastructure
Every engagement starts with a 90-day pilot. Every team member you approve and start working with is billable from day one. During the pilot, you can stop working with someone at any point if they are not meeting expectations, with no long-term lock-in. If that happens, Puzzle covers the recruiting work required to identify a replacement. Once you approve the replacement and they begin working, they become billable at the standard rate.
The AI era did not remove humans from the equation. It raised the bar for what the humans in the loop need to know. The companies that understand this are building the most durable customer relationships in software.
Sources
S&P Global (2025), 42% of companies abandoned most AI initiatives. https://agenticwork.io/blog/ai-project-abandonment-sp-global
The New Stack, Why OpenAI and Anthropic are hiring forward deployed engineer teams. https://thenewstack.io/forward-deployed-engineers-ai/
Perspective AI, 2026 FDE Hiring Trends: What 1,000 Job Posts Reveal. https://getperspective.ai/blog/2026-fde-hiring-trends-what-1000-job-posts-reveal
Forbes, May 2025, Klarna Reverses on AI, Says Customers Like Talking to People. https://www.forbes.com/sites/quickerbettertech/2025/05/18/business-tech-news-klarna-reverses-on-ai-says-customers-like-talking-to-people/
Gartner, August 2026, 87% of Customers Say Access to a Human Agent Is Essential. https://www.gartner.com/en/newsroom/press-releases/2026-08-04-gartner-survey-finds-87-percent-of-customers-say-companies-using-genai-for-customer-service-must-provide-access-to-a-human-agent0
Gartner, April 2026, 85% of Service Leaders Expanding Human Agent Responsibilities. https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-survey-finds-eighty-five-percent-of-service-and-support-leaders-are-expanding-human-agent-responsibilities-despite-expectations-of-mass-ai-layoffs
Userlens, Impact of Onboarding on SaaS Retention. https://userlens.io/blog/impact-of-onboarding-on-saas-retention
MIT NANDA, The GenAI Divide: State of AI in Business 2025. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
