From Time-Saving to Decision-Making: What’s Generative AI’s Next Leap for 2025?
By: Sarah Hoffman, Director of Research, AI at AlphaSense
Generative AI (genAI) success stories often focus on time savings and efficiency. Organizations have seen 9% time savings when using genAI for coding. GenAI is saving marketers 3 hours on a single piece of content. AlphaSense’s Generative Search tool is saving users between 20-50 hours per month on business research.
GenAI for time savings was the first step for many businesses to realize the value of the technology. As we enter another year of genAI-focused innovation, we are still just scratching the surface of what’s possible. While harder to quantify, the strategic benefits of genAI — from revolutionizing market analysis to reimagining future scenarios and driving product innovation — are enormous and are already changing decision-making.
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Market and Competitive Analysis
Generative AI combines automated data collection with real-time analysis, saving the time spent on manual data collection, while also enhancing decision-making. GenAI can also create a SWOT analysis based on large datasets, analyzing and assessing competitors by processing financial reports, product launches, marketing campaigns, and customer sentiment data. Businesses can use this to quickly spot areas where they are outperforming or lagging behind competitors. GenAI can also evaluate potential synergies — identifying optimal acquisition targets and predicting post-merger performance. This data-driven approach not only accelerates the due diligence process but also ensures that M&A decisions align with long-term strategic objectives.
However, one challenge persists: how can businesses ensure the accuracy and reliability of these insights? Retrieval-augmented generation (RAG) addresses this by leveraging trusted, citable sources for data, mitigating the risk of misinformation. Companies also face the critical decision of whether to build custom AI models or buy existing solutions. Building offers greater customization and alignment with specific needs, while buying pre-built models saves time and resources. Interestingly, when building, companies are finding that deploying multiple models to their AI stack — often three or more — actually works best.
Spotting Opportunities Early
This technology can also help predict market shifts. By processing vast amounts of historical data and real-time information, businesses can quickly create accurate forecasts, allowing them to adapt and capitalize on emerging opportunities ahead of their competitors. Specific use cases range from tracking changes in consumer behavior to the rise of new technologies or regulatory shifts.
These insights can lead to the development of new products or services that fill a gap. For example, Bentley Systems, a construction software company, uses genAI to create schematics that help architects design buildings in less time and can simulate how changes would affect their real-world performance. Another example is Nestlé, which uses a proprietary genAI tool to validate new product ideas and has seen the production ideation process go from six months to six weeks.
A Boston Consulting Group experiment found that 90% of participants improved their performance when using genAI for creative ideation; however, when applied to business problem-solving there was a 23% decline in performance. This was primarily because participants trusted misleading outputs, highlighting the necessity of proper training and understanding of genAI limitations to harness its full potential, especially in more strategic areas.
Strategic Risk Management
Generative AI is becoming a critical tool for identifying, analyzing, and mitigating strategic risks. By processing historical data, market trends, and real-time information, AI can highlight potential vulnerabilities and help organizations prepare for a wide range of scenarios.
For example, AI models can simulate the impact of economic downturns, supply chain disruptions, or competitive threats, enabling businesses to develop robust contingency plans. In addition, AI’s ability to predict emerging risks, such as regulatory changes or cybersecurity threats, allows corporate strategists to stay ahead of challenges and safeguard organizational resilience.
A genAI platform can also help risk managers maintain compliance with internal or external requirements by verifying each step or requirement of a regulation or framework. In fact, 89% of professionals in risk, fraud, and compliance recognize the advantages that AI brings to their sector.
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Takeaways for Business Leaders
Generative AI is not just a tool for enhancing efficiency — it’s a strategic enabler that can redefine how businesses operate, compete, and thrive. The potential for reshaping market analysis, spotting emerging opportunities, and navigating strategic risks demonstrates its transformative impact across industries.
However, realizing these benefits requires careful planning and execution. Companies must prioritize integrating AI tools into their existing processes, such as CRM platforms and financial analytics tools, while ensuring reliability and accuracy, and implementing auditing processes to ensure the technology is being used appropriately.
Teams must be equipped with the knowledge to use generative AI effectively, understanding both its capabilities and limitations. Comprehensive training programs can mitigate risks, such as overreliance on misleading outputs, while fostering confidence in AI-driven insights. AI adoption should be treated as an evolving process. Leaders must regularly evaluate its impact, adjust strategies as needed, and keep up with advancements in the technology to maximize value.
Generative AI is more than a tool for saving time; it’s a transformative force reshaping how organizations make decisions and lead in an increasingly dynamic and competitive world.
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