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For professional services firms like management consultants, market researchers, and IT services, performing quality research is essential but time-consuming. Fortunately, new generative AI tools can automate many research tasks to increase efficiency. In this post, we’ll explore five ways generative AI can streamline research for professional services teams.
When beginning work with any new client, you need to develop an intimate understanding of the client’s industry, including trends, disruptions, regulations, supply chain issues, and competitor landscape. This typically requires reading through many lengthy industry reports to identify key details, which can take more time than you have.
With generative AI, you can use a prompt such as “Please analyze auto industry reports over the past two years and summarize current trends, market size, key players, risks and opportunities in a 250-word report” and within seconds receive a synthesized brief with exactly what you need to know.
By using prompts for generative AI to scan industry sources, you can save hours of manual reading and analysis. For example, a firm working with a telecom client could prompt the AI to highlight the most important industry KPIs, M&A activity, political factors, and global trends. This arms your team with a comprehensive overview at the outset of a project, saving you time and resources throughout the project.
MORE: How to conduct deeper market and competitive intelligence research
Developing detailed profiles on clients, competitors, partners, and other companies is essential for consultants before key meetings and deliverables. Standard profiles covering history, leadership, financials, products/services, and operations can take hours to create manually. With generative AI, consultants can provide company names and prompt the generation of 250-word summaries to instantly get profiles.
For example, a prompt like “Write a 200-word company profile overview for a specific pharmaceutical company including history, founders, total employees, leadership team, top products, revenue, and recent acquisitions” would allow consultants to gather background quickly.
Researchers could also substitute competitors or partners to efficiently build profiles needed for strategy projects. This accelerates the ability to develop company briefs, especially for firms or markets where information is scarce—which could put you ahead of your competition.
MORE: Top 5 ways professional services teams are using generative AI
In any client engagement, consultants need to synthesize market research reports covering trends, forecasts, benchmarking data, and best practices related to the client’s industry and business needs.
However, these reports often run over 500 pages making thorough analysis time prohibitive. Generative AI can read and digest these long reports to produce short abstracts highlighting the most important insights, charts, takeaways, and recommendations.
For example, researchers can prompt the AI to “Please read the 2022 Forrester Research Report on Digital Transformation Trends and summarize the key findings, data points, and recommendations in a 300-word abstract.”
Rather than needing to read the full report, consultants get the essence to incorporate into deliverables. This enables one report abstract after another to be produced to build a knowledge base, which can then be easily sent to other key stakeholders.
MORE: Follow trends in financial services for better business intelligence
Monitoring news and current events most relevant to a client is hugely valuable but requires constant research. Generative AI can rapidly find the latest news, scandals, executive changes, product releases, lawsuits, and more based on company or industry prompts. This keeps consultants continuously updated on happenings that may impact client work with minimal effort.
For instance, prompts like “Summarize key news and events over the past 3 months for Coca-Cola” or “Find the most important mergers and acquisitions in the software industry in Q3 2022” can yield summaries of the events most relevant to ongoing projects. This application saves analysts time while ensuring real-time awareness, allowing you to make decisions with relevant information.
MORE: How to capitalize on generative AI to enhance decision-making
While generative AI has immense potential to enhance research efficiency for professional services firms, there are challenges to consider.
First and foremost, ensuring information accuracy is integral to any consultant research. Generative AI may inadvertently include incorrect or outdated data in its synthesized research. You should always verify your sources and ensure that you are using accurate information to power your reports.
Additionally, generative AI does not always cite sources, so you’ll need to do extra work to ensure you’re crediting the right people.
AI-generated research does cut down on time, but it’s necessary to make sure it’s tailored to each client. Even if you have similar clients across one industry, you may need to significantly adapt a summary created by generative AI.
While these present challenges with generative AI, the efficiency gains often outweigh the limitations. Overall, generative AI is becoming a powerful asset for professional services firms. But it requires thoughtful adoption, security, governance, and human oversight to minimize risk. If leveraged properly, AI-assisted research can drive productivity and knowledge gains.
Generative AI has immense potential to automate tedious research tasks for professional services firms. While human oversight is still needed, AI can analyze endless information faster than any team.
With the right prompts, Generative AI can produce summaries, profiles, and current awareness to jumpstart research. This allows firms to deliver higher-quality work in less time, increasing productivity and value for clients, making this new technology a trend to watch.
Discover how your company can harness the potential of AI for maximum research efficiency while mitigating risk with our comprehensive toolkit. Download it now to learn the importance of high-quality data and best practices for powering your AI projects.