I wrote earlier that “AI-first” is the minimum requirement and that organizational strength determines who wins. Just under a year later, I can be much more specific about this. R&D teams are building faster than ever, sometimes up to four times as efficiently as they were a year ago. That’s a win. However, a customer only notices the difference if you ’re also building the right things faster.
The bottleneck has moved
For years, development was the limiting factor. There were more ideas than there were engineers to build them. Roadmaps were just waiting lists. AI has shortened that waiting list. Writing, testing, and documenting code now takes a fraction of the time. If you look only at developer productivity, you’ll see progress. But if you look at what actually reaches the customer, you’ll see that this gain doesn’t yet translate directly into results. Building faster only pays off if you also know faster what you need to build.
Invest in product strength first
The traditional ratio of one product manager to every six to ten engineers doesn’t work for teams working with AI. In the market, that ratio is shrinking rapidly. Some teams are already assigning one product manager to just a few engineers. At Blinqx, we’ve expanded the CPO organization over the past year. And we’ve shifted all our roadmaps to an AI-first approach. Every roadmap now begins with the question of which of the customer’s tasks an agent can prepare or take over.
Product managers also receive help from AI in this process. Gathering signals, summarizing input, and monitoring the big picture are increasingly being done automatically. This frees up a product manager’s time for customer meetings, validation, and decision-making. That’s exactly the work that humans need to continue doing.
Small squads with their own space
New agentic solutions require teams that operate independently of day-to-day operations—working at their own pace, with their own goals, and with the freedom to experiment. At Blinqx, we call these “incubators”—small squads with a startup mentality, focused on agentic solutions that make our clients’ work an order of magnitude better. Think of reducing fragmented workflows in accounting or monitoring deadlines and compliance in legal. The squads are given the freedom to fail and take initiative. We also encourage this through the way we reward success.
Those squads need a solid foundation. That’s why we’re building a single, centralized agentic platform —WorQX— that all Blinqx companies run on. It features its own memory, workflows that adapt to the task at hand, and its own model layer. The division of labor is clear. The central AI team provides the platform layers. The teams in our segments provide the domain expertise.
Every discipline evolves along with it
An AI-first organization doesn’t stop at Product and R&D. Every discipline plays a role, from sales to legal to customer success.
- Sales focuses on adoption. A signature doesn’t mean revenue. Sales really begins when the customer starts using the product, and that includes usage-based pricing.
- Support uses AI for first-line support and has specialists on the team for questions where experience matters.
- Customer success becomes a growth engine. It’s easier than ever to quickly build a competitive product. Satisfied customers are your best defense.
- R&D attracts AI-native developers and implements AI tools as standard practice.
Change takes time
The biggest lesson from the past year: change takes time—both internally and among users. Teams have to learn new habits. Product managers have to make decisions at a pace they’re not used to. And customers have to build trust in agents who prepare work for them. That last part is taking the longest—and for good reason. Our clients operate in regulated industries where they remain responsible for the outcome.
What this requires of your organization
To ensure your AI transformation succeeds, you’ll need to restructure your organization as follows:
- Make your product organization more influential than your engineering teams
- Organize innovation into small squads that operate independently of day-to-day operations, with room to fail.
- Build shared platform layers centrally for all your customer groups and keep domain expertise with the teams for each customer group.
- Involve sales, support, and customer success in the change from day one.
- Be prepared for a long-term process—including for your customers.
Everyone has access to the same models and the same tools. There are plenty of smart people, too. What sets organizations apart is the courage to make choices and the creativity to organize work differently. Anyone who wants to succeed in 2027 will organize their entire organization around the product.
AI accelerates development, while customer research, validation, and decision-making remain largely human tasks. Engineers are building faster than the product organization can determine what needs to be built. As a result, the limiting factor is shifting from R&D to product.
That ratio is shrinking. Whereas one product manager used to provide work for six to ten engineers, a team building with AI requires more product capacity per engineer. Organizations are therefore investing in their product teams and are also supporting product managers with AI.
All disciplines. Sales focuses on adoption, support combines AI with specialists, customer success becomes a growth driver, and M&A automates due diligence. R&D attracts AI-native developers and uses AI tools as standard.
Expect it to take at least a year before the change becomes truly visible. New habits, new roles, and building trust among users take time. Organizations that persevere will then see the impact in terms of efficiency, adoption, and AI revenue.
