AI Research & Industry Insights

What the latest data says about AI implementation—and how we help you win.

Latest Industry Pulse — February 2026

What 1,100+ developers, CTOs, and founders are saying about AI implementation

DigitalOcean's Currents report (February 2026) surveyed over 1,100 developers, CTOs, and technology founders on the direct challenges they face with AI implementation and whether businesses are seeing real ROI from AI. Here's what they found.

60%

See applications and agents as most long-term value

49%

Identify inference cost as top scaling blocker

53%

Report productivity/time savings from AI agents

44%

Report new business capabilities from AI agents

44%

Spend majority of AI budget on inference (not training)

64%

Integrate third-party AI APIs into applications

61%

Use hybrid or niche tools (not single stack)

15%

Train AI models from scratch

The industry is betting on agents and integration. We build both—and we're built to control inference costs so you see ROI, not runaway spend.

See how we build agents that deliver ROI

Read the full DigitalOcean Currents report

Foundational Research: The 2025 AI Landscape

A comprehensive MIT study that highlighted what separates AI winners from the rest

Companies that got it right

$30-40 billion

Investment (2024-2025)

300+

Public AI Deployments Analyzed

150+

Business Leaders Interviewed

350+

Employees Surveyed

MIT's landmark 2025 study examined 300+ AI deployments, interviewed 150+ business leaders, and surveyed 350+ employees to understand exactly what separates AI winners from the rest. Here's what they found—and what it means for you.

What Holds Many Back

Understanding what holds many back is the first step—here's what the top 5% do instead.

Skipping the Business-Alignment Step

Organizations tried to bolt generative AI into existing processes without deeply embedding AI-savvy personnel within the business to understand operations, pain points, and objectives.

Treating Pilots as "Set and Forget"

Most pilots never advanced beyond experimental stages—meaning they did not get adapted or scaled to drive measurable business results.

Underestimating the People Side

Lack of necessary skills in staff, cultural resistance, and not having cross-functional teams embedded within the business further compounded the disconnect.

Not Structuring Workflows Around AI

Organizations did not understand how to use AI tools effectively or structure workflows to extract benefits, leading to poor adoption and ROI.

Every pattern MIT identified as a roadblock? We've designed our process specifically to sidestep it. We're here to help you get there.

We're built to sidestep these roadblocks—let's talk

How We Help

Our approach addresses what both studies identified—and delivers the outcomes the industry is betting on.

Deep Business Process Mapping

We don't guess at your workflow — we embed in it. Our engineers sit with your team, map every friction point, and build AI around your actual operations.

Embedded Technical Teams

Your AI project gets a dedicated cross-functional team, not a vendor who disappears after kickoff. We stay in the room through launch and beyond.

Workflow Adaptation Expertise

AI works best when the workflow is shaped around it — not the other way around. That's what we do: redesign the process so the AI fits naturally.

Scalable Implementation

A pilot is only worth it if it becomes a product. We're obsessed with making that transition smooth — from proof-of-concept to full-scale, with measurable ROI at every step.