At Cascade Insights, we’ve all the time sought instruments that assist us ship stronger insights extra effectively. For greater than a yr, we’ve been rigorously testing and integrating AI throughout our workflow, from feasibility checks and survey writing to visible storytelling and closing report creation. Over that point, we’ve seen sure duties change into simpler, a couple of get trickier, and fully new prospects emerge due to quickly evolving capabilities.

This has been an ongoing exploration, not a one-off experiment. Alongside the way in which, we’ve collected a variety of wins, from shaving hours off prep time to unlocking new methods of synthesizing advanced findings, whereas additionally studying the place AI nonetheless falls quick.

Critically, we solely discover AI platforms that meet our strict knowledge safety requirements. Shopper knowledge isn’t fed into instruments with out correct management, transparency, and confidentiality, permitting us to innovate confidently with out risking delicate info.

Our inner #aitools Slack channel has change into the take a look at kitchen for exploring what AI can (and might’t) do. It’s the place our workforce shares experiments, breakthroughs, and challenges as they incorporate new fashions and toolkits into their work.

Right here’s a behind-the-scenes take a look at how we’re weaving AI into our workflows. Whereas we’ve explored many various functions, these are the use circumstances we’ve discovered to supply essentially the most worth and make the largest distinction.

1. Interview Summarization & Thematic Evaluation

Summarizing giant volumes of qualitative knowledge is the place AI can supply huge raise.

  • Speedy Summarization: ChatGPT condensed 21 IDIs into slide-ready content material in hours.
  • Theme Extraction: CoLoop and NotebookLM tagged verbatims, extracted themes, and surfaced early suggestions.
  • Comparative Evaluation: AI recognized gaps and shifts throughout a number of interview rounds.

Takeaway: NotebookLM is a standout for synthesis, whereas CoLoop accelerates theme-tagging. Handbook assessment is crucial to make sure quotes are correct and extractions preserve their integrity.

2. Deck Creation & Visible Ideation

We examined AI for a variety of visible wants, from preliminary slide templates to full picture era and branded deck builds.

  • Slide & Deck Creation: Gamma and ChatGPT transformed outlines into branded decks, with Gamma excelling in structure and coloration customization. AI helped construction content material, counsel layouts, and even generate draft speaker notes.
  • Picture & Infographic Era: Firefly, Ideogram, and Canva Magic Studio produced idea photographs, icons, and easy infographics. Easy prompts usually yielded clear, usable outcomes; advanced scenes or multi-step knowledge visuals generally suffered from odd artifacts, inconsistent styling, or gibberish labels. Infographics typically labored finest when generated in components (i.e., icons + textual content individually) after which composed in instruments like Illustrator or Figma.
  • Refinement: Closing sprucing in Photoshop, Illustrator, or Canva was wanted for model consistency and readability.
  • Future Video Potential: We’ve experimented with quite a lot of new AI video instruments (i.e., Runway, Veo3, and rising generative video options in Adobe) and see robust potential to increase related workflows to explainer movies, animated infographics, and short-form social content material.

Takeaway: AI is superb for breaking inventive blocks, rushing iteration, and producing robust first drafts. Human refinement stays important for model alignment, readability, and polish.

3. Writing Help & Report Drafting

AI is now normal in drafting proposals, experiences, {and professional} communications.

  • Drafting & Structuring: ChatGPT co-authored white paper intros, summarized transcripts, and refined report sections.
  • Tone Sprucing: Claude and Gmail’s AI instruments supplied fast tone changes, every with distinct kinds.

Takeaway: ChatGPT helps construction and refine content material. Claude provides depth. Customized GPTs present potential for standardizing narrative parts. Human oversight stays important for making certain circulation, tone consistency, and closing polish.

4. Structuring Open-Ends & Quant Knowledge

AI can construction each open-ended and quantitative knowledge (although limitations stay).

  • Open-Ends: Claude categorized 157 responses with multi-tag logic and counts. ChatGPT created code frames and thematic tallies however generally missed delicate overlaps. CoLoop’s quant instruments allowed just one tag per response; gaps typically wanted handbook QA or instruments like CodeIt.
  • Quant Knowledge: Claude and ChatGPT dealt with primary charts and single-select summaries effectively. Multi-select and matrix questions have been extra error-prone, requiring fact-checking to stop hallucinated labels or incorrect stats.

Takeaway: AI could be very useful for exploratory or draft-level quant evaluation; closing numbers required validation and area experience.

5. Survey Writing, KIQs & Query Design

AI is very efficient at drafting and refining analysis questions.

  • Survey & Information Creation: Claude persistently produced considerate, nuanced questions for surveys and moderator guides. 
  • Brainstorming KIQs: ChatGPT, Claude, and Gemini helped generate key intelligence questions from name transcripts.
  • Query Reframing for Totally different Codecs: One venture concerned adapting preliminary analysis findings into purchaser persona questions with ChatGPT. One other noticed an in-depth interview information remodeled into a spotlight group script with actions and follow-ups.

Takeaway: Claude is especially efficient at phrasing subtle and nuanced questions. ChatGPT and Gemini are robust for brainstorming, reformatting, and adapting inquiries to totally different analysis modes. Skilled assessment stays important to make sure questions are correct, unbiased, and aligned with the analysis aims.

6. Automation & Workflow Hacks

Some workforce members took AI a step additional, utilizing it to automate workflows:

  • Customized Workflows: A Zapier + Lindy setup pushed Fathom transcripts to Google Sheets, summarized calls, and prepped persona-specific follow-ups.
  • CRM Evaluation: Claude prioritized outreach based mostly on engagement and spend historical past.
  • Sudden Perks: ChatGPT merged PDFs, saving handbook effort.

Takeaway: AI-driven automation delivers severe time financial savings however requires the suitable use case and technical setup.

7. Deep Analysis & Venture Kickoffs

AI deep analysis options are giving groups a sooner begin on market evaluation, aggressive monitoring, and venture preparation.

  • OSINT Analysis: Workforce members used Deep Analysis to analyze competitor positioning, pricing, and product updates. ChatGPT adhered intently to prompts, supplied structured management, and favored concise, U.S.-only sources. Gemini generated wide-lens narratives and summaries however typically drew from mixed-quality sources. Perplexity Professional accelerated discovery with quick supply tracing and citations, although it generally ignored geographic limits, paraphrased quotes as direct speech, or linked to firm blogs offered as impartial opinions.
  • Venture Kickoff Preparation: By shortly surfacing rivals’ positioning, up to date pricing, and up to date bulletins, these instruments lowered ramp-up time and allowed analysts to start out discovery calls and secondary analysis with sharper context.

Takeaway: AI deep analysis options are serving to to speed up secondary analysis and put together for venture kickoffs. ChatGPT delivers robust, structured insights, whereas Gemini and Perplexity can add coloration. Skilled context is crucial to confirm accuracy, filter out low-quality sources, and guarantee findings are dependable.

8. Moderated Interviews 

The workforce examined AI interviewer platforms like Strella, Versive, and Hear Labs:

  • For easy, structured B2C subjects, these platforms carried out moderately effectively. They might ask predefined questions, keep on script, and preserve a impartial tone all through. In eventualities the place the purpose was simple knowledge assortment, comparable to product preferences or usability suggestions, their potential to remain constant and environment friendly was a transparent power.
  • Nevertheless, for advanced B2B interviews, efficiency fell quick. AI moderators struggled to:
    • Comply with up meaningfully based mostly on nuanced or jargon-heavy solutions.
    • Alter pacing in real-time, generally dashing responses or lingering awkwardly.
    • Acknowledge delicate cues {that a} human interviewer would use to pivot or dig deeper, particularly round delicate or high-stakes subjects.

Regardless of these limitations, AI moderators confirmed promise as coaching aids, serving to new workforce members rehearse query units, take a look at totally different phrasing, or simulate edge-case eventualities. In addition they supplied worth for inner dry runs earlier than reside interviews with C-suite members or technical stakeholders.

Takeaway: AI moderators aren’t prepared to exchange human researchers in B2B settings. The nuance, improvisation, and context-awareness required for high-quality qualitative interviews nonetheless demand a human contact. That stated, there’s actual potential for AI to help behind-the-scenes duties like recruitment and prep, liberating up researchers to give attention to the conversations that matter.

AI Takeaways: What We’ve Discovered

After a few yr of teamwide experimentation, a couple of constant themes have emerged:

AI Speeds Up the First Draft

Whether or not it’s summarizing 20 interviews or constructing the primary draft of a deck, AI dramatically reduces time spent on rote duties, liberating our consultants to give attention to interpretation and storytelling.

Software Selection Issues
  • ChatGPT: Greatest for summarizing, writing, and structured outputs.
  • Claude: Glorious for deep reasoning and quantifying open ends.
  • NotebookLM / CoLoop: Nice for transcript dealing with and synthesis.
  • Gamma / Canva / Firefly: Helpful for design inspiration and structure scaffolding.
Human Oversight Is Non-Negotiable

AI is a co-pilot, not an autopilot. It wants vital considering, subject-matter information, and moral oversight to ship reliable insights. Throughout the board, human oversight stays vital for refining tone, eliminating bias, and aligning query design with analysis targets.

Steady Experimentation is Key

AI capabilities change nearly each day. Our ongoing exploration over the previous yr has uncovered new AI capabilities that simplify advanced duties and result in extra environment friendly workflows. We’ll proceed to adapt and evolve our strategies because the know-how advances.

AI’s Function in Analysis: We’re Simply Getting Began

From refining customized GPTs to experimenting with ChatGPT-4.5’s picture capabilities, we’ve solely scratched the floor of what’s doable, and we’re transferring quick to remain forward.

Our workforce continues to stretch the boundaries of AI’s position in analysis. We’re now exploring:

  • Agentic workflows: Working full analysis duties autonomously, like parsing and summarizing hundreds of feedback to extract sentiment, themes, and contradictions throughout a dataset too giant for handbook evaluation.
  • AI-powered tone and sentiment detection: Utilizing AI to determine tone, perspective, and implied emotion throughout transcripts and open-ends, whereas preserving nuance.
  • Mid-Coding Passes (MCPs): Testing AI-assisted workflows for early reads on patterns inside qualitative knowledge, rushing time to perception with out dropping the depth that human analysts carry.
  • AI as a analysis co-pilot: Not simply writing or formatting, however figuring out gaps in a dialogue information, flagging conflicting knowledge, or proposing new instructions mid-project.

We’re not stopping at enhancement, we’re actively redefining how work will get accomplished. AI isn’t simply saving time; it’s unlocking fully new workflows. Because the instruments evolve, so will our strategy, all the time guided by strategic considering, methodological rigor, and knowledge safety.

So we’ll maintain testing. Hold iterating. And maintain sharing what really works. And because the instruments proceed to evolve, so will we.


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