Explore, Scale, Efficiency: How We Actually Rolled Out AI Internally

The market research industry isn’t adopting AI in a single, unified wave. It is divided into two groups going at two distinct speeds.

The 2026 GRIT Insights Practice Report highlighted a widening divide across the market: traditional research functions are facing budget constraints and narrowing methodology suites, while tech-enabled organizations are pulling ahead. The same report revealed that agentic AI has concentrated heavily in three primary operational tasks: data analysis, report updating, and data preparation. That tells us AI adoption is very real, but it is taking hold in targeted operational pockets rather than spreading evenly across the enterprise.

We don’t have a tidy explanation for why the split is happening the way it is. What we can offer is how we approached the decision ourselves at PureSpectrum, and the three-phase pattern we’ve watched play out since.

We Skipped the Pilot

Eighteen months ago, everyone at PureSpectrum had an AI license on day one. Not a pilot group. Not an approved-use-case shortlist. As a Google Workspace organization, Gemini was our immediate starting point, and we later added Claude as specialized use cases evolved. We didn’t choose the small-pilot approach; we released it across the organization and paid attention to where people found utility.

Certainty was never really on the table. The space moves fast enough that if we had waited to get the tooling decision exactly right, we would probably still be in a room debating which model to standardize on. So we optimized for speed to insight over speed to certainty, letting the teams closest to the daily work show us where the value lived.

Phase 1: Explore

True discovery comes from the people closest to the daily work, not from a top-down strategy deck. AI tools make it easy for employees to prototype solutions directly within their own workflows, meaning the people doing the work can test their own thinking and walk away with functional tools.

We’ve watched that approach produce more than 50 use cases across the business:

• Sales Reporting Assistant: Gives each rep a live view of their week against quota targets.
• Email Triage System: Helps a busy services team field and manage a high daily inbox volume.
• Independent Team Utilities: Tools picked up organically by support, engineering, and QA to solve functional pain points.

None of this was assigned from the top down. Every single use case surfaced from people solving their own operational friction.

Phase 2: Scale

Unchecked exploration eventually hits diminishing returns. That is the signal to transition from open discovery to refinement and selection. Not every internal prototype earns dedicated engineering support.

We evaluate candidate applications strictly on benefit versus cost:

• Organizational Reach: How broad the impact is across the company and how many people it touches.
• Time Savings: How much manual time it saves relative to the cost to build, run, and maintain.

That filter keeps scaling decisions grounded in measurable outcomes rather than in whoever made the loudest case for their project.

It is worth naming the part that is easy to underestimate here: adoption is probably harder than the build itself. Once something works as a prototype, turning it into a finished application that people rely on daily still takes real investment in change management, training, and enablement.

Phase 3: Efficiency

The final phase isn’t about using AI to make an outdated workflow run slightly faster. It requires fundamentally redesigning the process around what is now possible. This is where a tool stops being a shortcut and starts being embedded in a genuinely different way of working.

We are not fully there yet as an organization, but two examples demonstrate what this looks like in practice:

• ISO Compliance Auditing: An internal audit agent scans communications across Slack, email, and Google Drive to fill out our ISO compliance checklist, work that previously consumed real human hours every cycle.
• Operational Health Scanning: A project scanning tool allows managers to evaluate thousands of active research studies simultaneously, automatically surfacing only those falling behind schedule.

Three Decisions, Not One Leap of Faith

Every phase carries its own specific failure point. Skip Explore by locking down access too early, and you never find out where the real value is. Skip Scale by failing to establish graduation criteria, and you end up investing in whatever got the most attention. Skip Efficiency by leaving the old process untouched, and AI just makes an outdated way of working marginally faster instead of building something better.

If the industry really is splitting into two groups the way GRIT describes, we suspect the difference comes down to which organizations treated AI adoption as a catalyst to help change the business, and those that are waiting on certainty and product.

This is the first post in an ongoing series on how PureSpectrum has approached AI adoption, both internally and with clients.