Analysts built their careers on knowing what others didn’t. Now, AI knows everything –maybe? Faster, cheaper, able to churn through mountains of data, LLM’s (Large Language Models) and GenAI (Generative Artificial Intelligence) can whip up a market share report or vendor evaluation in seconds. AI promises insights on demand, no humans required. Start-ups are asking why they should bother with building relationships with analysts when a bot can summarise reports and spit out trends in seconds. It sounds tempting. Too tempting.
But while AI can process information, it can’t build trust, influence, credibility or context. It can’t persuade a CFO, spot a hidden market shift or read the subtext in a vendor strategy presentation (i.e. what are they NOT saying). Analysts aren’t just data crunchers; they’re the masters of the attention economy; the people whose judgement actually moves markets. Treating them like replaceable algorithms is a dangerous mistake.
Analysts view AI as a force multiplier.
We asked some industry analysts what they think of AI and it seems that they aren’t hiding from it, they’re weaponising it. IT Harvest, a cybersecurity specialist firm, was one of the first to leverage AI, fully integrating LLMs into its research processes only seven weeks after ChatGPT was announced, while HfS Research claims to be the first “Generative Analyst Firm” in July 2023 with its GenAI search capability. Then, Forrester launched Izola in October 2023 and now we’re seeing AskGartner hit the market. New AI-powered methodologies like the Gartner emerging Market Quadrant, the Futurum Group Signal and IDC ProductScape are promising to revolutionise vendor RFI experience. Carsten Bange / BARC explained that “AI markes our internal processes more efficient” and “enables us to create new products.”
Analysts see AI as an opportunity to do even more. With it, coverage can scale even further, as Richard Stiennon / IT Harvest pointed out, no firm could cover the long tail of 4,000+ cybersecurity vendors without strategic use of AI. This kind of automation frees analysts to focus on what Jon Collins / GigaOm said actually matters: delivering higher-value research, deeper insights and better communication. This is because, as Jon outlines, “AI will accelerate market dynamics, requiring leaps of insight based on strategic sentiment that isn’t immediately reflected in data or content (the input to LLMs) – as long as there are people involved in complex decision making, they will require other people to make sense of it all.”
Raw processing power only takes you so far. Frameworks built on decades of experience and pattern recognition, as Jonathan Care / KuppingerCole emphasised, carry weight AI cannot replicate; “the analyst-validated stamp matters more than ever in an AI-saturated market”. Likewise, Ben Wood / CCS Insight said “we feel that the years of experience and deep industry knowledge held by our analysts still significantly eclipses what an AI model can provide”. While Rodolphe d’Arjuzon / Verdantix outlines how advisory can become the premium offering, providing context, nuance and actionable guidance that machines simply can’t deliver.
But of course, it’s still a disruptor.
Forrester notes that AI shares similarities with other tech industry disruptors. Forrester describes how even though the cloud disrupted traditional storage, it has changed how organizations build, deploy, and manage software across diverse environments. Social networking disrupted human relationships — but it is now a mainstay for consumer interactions and a key influencer of purchasing decisions. The internet and search engines disrupted libraries — today’s libraries are busier than ever, as they’ve become idea spaces where communities and people gather together. The question is – what will that look like?
In a recent webinar, Daniel Newman / Futurum Group and Phil Fersht / HfS Research highlighted how AI is rewriting the rules for analyst research. Traditional deliverables like Gartner Magic Quadrants and long reports are being devalued because LLMs can synthesise insights faster and often better. So, content will only have value when it comes from a “superstar” analyst with authenticity and influence. In their view, smaller, agile firms can pivot faster than the giants to break through in this reality.
And yet, just like the internet disrupted the music industry, GenAI will disrupt content monetisation. The analysts we’ve interviewed for this blog post are confident that artificial intelligence relies on original content and proprietary data –whose value isn’t going away. Just like it’s been the case with internet and music, AI is accelerating the move towards experience as I predicted in this post from 2011: Analyst firms: rock star bands or record label dinosaurs? In the case of analysts it means original insights, sharper advisory delivered by experienced analysts, more in-person events and interactions like HFS or Constellation run all the time.
3 scenarios for the impact of AI on industry analysts.
KIMRA already reported that 100% of the research pros surveyed in their study are using GenAI tools “representing a complete shift from experimental to operational status”. At Starsight, we have been watching the industry closely, and we see three plausible directions it could take. In each case, the analysts who navigate these changes effectively will quietly emerge as the real linchpins of the industry. We’re bold and brave, stuck our finger in the air and added some probabilities –leave your own thoughts in the comment!
- Brave new world: (Gen)AI becomes the interface for research. Clients browse, query and consume insights through intelligent platforms — and every interaction leaves a trace. Each query costs tokens and those tokens somehow convert into payments to analyst sources. The more useful a report or inquiry, the higher it ranks and the more it earns. It’s AdTech (think a SSP) meets AlphaSense: a self-sustaining marketplace where the machine measures value through engagement, and analysts are rewarded for accuracy, clarity and impact. Faster access, fairer pricing, and a vibrant ecosystem where humans and algorithms co-create insight. 0.3 probability.
- Gartner disrupts itself, moving faster than anyone else. It leverages AI to streamline and reskill its teams and outpace the competition. Research cycles shrink, market influence concentrates, and the winner-takes-all scenario isn’t theoretical — it’s inevitable. Technology investment becomes the ultimate determinant, leaving cash-strapped mid-size firms scrambling to keep up. 0.5 probability.
- AI makes raw research a commodity, and only analysts with sharp advisory skills or a strong personal brand thrive. The industry fragments, with more voices and more niches, but monetisation becomes tricky: will the market consolidate around a few trusted sources, or will it support a proliferation of smaller, interesting players? Either way, insight alone isn’t enough. 0.7 probability.
The bottom line: trusted analysts trump algorithms.
No company is making million-dollar buying decisions based on an LLM’s output. AI can summarise, crunch numbers and spit out trends, but it can’t replace judgement, credibility or the subtle influence analysts bring to the table. Peer reviews, analyst opinions and original research are more important than ever. In short, buyers will trust analysts over algorithms.
Analysts exist because buyers don’t just need data, they need perspective. AI has no experience, no accountability, no track record. Analysts do. They make sense of complexity and give decisions weight based on their human experience. That’s why they’ll remain the trusted advisors in a market flooded with automated insight.
Personal relationships with the people who build, shape and interpret the algorithms have never mattered more. Mentions in analyst research reports, quality briefings and strategic advisory sessions with those strong analyst brands are the currency that still moves deals. And to get there, you need an analyst relations strategy that’s built on forging those relationships with the influencers who matter most.
Thank you to the the following analysts and firms who took time to answer our questions for this blog post.
- Ben Wood / Chief Analyst & CMO, CCS Insight.
- The Forrester team.
- Daniel Newman / CEO, Futurum Group.
- Jon Collins / VP of Engagement, GigaOm.
- Phil Fersht / CEO, HfS Research.
- Richard Stiennon / IT Harvest.
- Simon Burton / Director, KIMRA.
- Jonathan Care / Lead Analyst, KuppingerCole.
- Rodolphe d’Arjuzon / Co-founder and CPO, Verdantix.
Read the latest Starsight Transmissions.
- What is The Role of industry Analysts in an AI-Driven Market?
- What does it take to get into the Gartner Magic Quadrant?
- The top industry analyst events vendors should invest in.
You inspired me to write this post today Ludovic: https://stiennon.substack.com/p/of-long-tails-and-stalking-horses If it were not for Wall Street’s fickleness Gartner would have plenty of time to react and adopt AI. As it is, they have to do something fast.
Thanks for the nod Richard, it means a lot coming from you.
Your point about exhaustivity is really interesting –and supported by data.
IIAR> members asked this question precisely when Forrester removed the lower band in its Wave evaluative methodology: this also removes their relevance in tracking emerging players.
See > https://analystrelations.org/2024/07/02/iiar-analyst-firm-webinar-forrester-wave-update-16th-july/
Good analysts maintain manual spreadsheets and notes about vendors in the categories they track –but this is time-consuming and out-of-process.
To give Gartner some credit, they came up with the Emerging Magic Quadrant which tracks more vendors and with a faster refresh cycle -but it’s very very far from being perfect. They have the resources to correct it (and hopefully change the confusing product name) –will they?