Since the early 1990s, Gartner’s Magic Quadrant has been the gold standard for B2B buyers looking to simplify the complex vendor selection process.
Magic Quadrant reports are refreshed annually by a team of expert industry analysts who follow Gartner’s well established methodology to evaluate vendor strengths and weaknesses, then plot each provider on a neat grid divided into four categories: Leaders, Challengers, Visionaries and Niche Players.
Magic Quadrants are Gartner’s most-read reports, and their popularity with both B2B buyers and tech providers has propelled the research and advisory firm’s annual global contract value to nearly $5 billion.
Other research firms have tried to replicate Gartner’s success with their own versions of vendor ranking reports. Examples include Forrester Waves, IDC MarketScapes and Everest Group PEAK Matrix reports.
Love it or hate it, Gartner has managed to retain its position as a leader for three decades, thanks in large part to its renowned Magic Quadrants. But could the advancement of Generative AI threaten to knock the firm out of the upper right quadrant — or banish it from the grid altogether? Could it be possible for B2B buyers to bypass gated reports and human analysts and simply ask ChatGPT to recommend a Digital Experience Platform or a Supply Chain Planning Solution?
Our take: AI won’t replace analysts, but it will transform their roles and the way they conduct and disseminate research. As a result, Magic Quadrants and other vendor assessments will become more agile, dynamic and interactive.
In this new era of AI-augmented research, human skills such as creativity, empathy, critical thinking, and relationship-building will become meaningful differentiators for both analysts and Analyst Relations (AR) professionals.
As enterprises race to leverage AI for competitive advantage, they are turning to industry analysts for reliable, up-to-date information to help inform decisions related to AI strategy and investments.
The demand for trusted information on AI became insatiable in 2023, the year “ChatGPT” surpassed “Magic Quadrant” as the most searched-for term on Gartner’s website.
Industry analysts are seen as trusted advisors in the fast-moving and often confusing AI era. As deepfakes and misinformation proliferate, the trust that analysts and analyst firms have banked becomes invaluable and essential to maintain.
In the coming years, analysts won’t just offer advice about AI — they will leverage AI to become smarter, faster and better at their own jobs.
AI-augmented analysts will use AI to assist with real-time data collection, retrieval and analysis, making them better equipped to keep a steady pulse on the categories and markets they cover.
AI will help analysts write more content faster and contextualize research for specific clients. It will also help streamline communication and collaboration between analysts and the tech and service providers that they evaluate — which will make the research process faster and easier for all involved.
As AI adoption increases, the human analysis and understanding that analysts bring will remain indispensable. AI can process vast amounts of data and identify patterns, but it cannot fully grasp the nuanced intricacies of each client’s unique business context. Gartner analysts’ deep industry knowledge and their ability to interpret complex business environments will continue to be crucial.
Analyst insights, derived from years of experience and client interactions, will allow them to provide tailored recommendations that AI alone cannot offer. This blend of human expertise and advanced AI will ensure that analysts and their assessments, including Magic Quadrants, remain essential tools that inform key investments and business decisions.
With AI enhancements such as continuous data collection and integration, vendor assessments such as the Magic Quadrant will become less static and more dynamic. Forget the annual refresh — in the future, AI could enable instant updates and real-time insights.
Both Gartner and Forrester have debuted more interactive vendor evaluations in recent years, which have made the evaluations more customized to clients. These reports will become even more interactive and useful to B2B buyers in the coming years thanks to AI’s ability to personalize content at scale.
These dynamic digital experiences could help readers better understand complex topics and trends, and supply them with timely, actionable insights to better inform urgent and important decisions.
AI could also improve the time- and resource-intensive research experience for vendors by replacing traditional annual requests for information (RFIs) with continuous data collection through real-time feeds shared by vendors with analyst firms. This shift, powered by APIs and AI, will make data sharing more efficient for vendors and data collection and analysis easier for analysts.
Continuous data collection and analysis could also enable the integration of inputs from additional sources, such as user reviews. As a result, user review platforms — from Gartner Peer Insights to G2 — will play a more significant role in vendor assessments, because user feedback provides a window into the real-world experience of technology users.
This multi-source, continuous data stream will be analyzed by AI, machine learning algorithms and advanced sentiment analysis tools, which could help analysts identify patterns and trends as they emerge and provide tech buyers and providers more valuable and actionable insights.
These advancements will result in Magic Quadrants and other vendor assessments that are more magical than ever before because they’re more current, accurate and dynamic than ever before.
As we stand on the brink of an AI-driven revolution in research, the future of AR is brimming with exciting possibilities.
Over the next five years, AR professionals will play an essential role connecting both data streams and people. To do this, they’ll pioneer new ways of working with analysts by developing dedicated vendor collaboration portals where providers can share updates and innovations. These portals will foster transparency, continuous communication, and a more collaborative AR ecosystem.
AR pros and other leaders who oversee AR as part of broader mandates will also need to develop and maintain automated pipelines that collect real-time data from various stakeholders within their organization and deliver it directly to analysts. Establishing agreements with analyst firms for data governance will ensure that analysts have access to accurate and timely information while vendors retain control over how their data is shared — and perhaps more importantly, how it’s not shared.
The most successful AR pros will embrace AI innovation and continuous learning. They’ll automate mundane tasks such as data entry and interaction scheduling and lean into strategic work such as insight activation and relationship-building.
Because human relationships can’t be replicated with AI, people who work in AR should double down on cultivating mutually beneficial relationships with analysts and internal stakeholders. These relationships are essential to uncovering and activating insights, and they’re strengthened with every meaningful interaction.
As Magic Quadrants and other vendor assessments evolve with AI, AR pros can learn more from them, particularly when it comes to monitoring user experience and sentiment. Those who expand their remit to manage user reviews and set up systems for sharing valuable analyst and user insights internally will help their organizations gain a competitive advantage.
For AR pros and other tech leaders, the real magic will happen when they leverage AI and their human skillset to identify insights that help their organizations make better decisions — and ultimately win in both the vendor assessment and the market.
