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In consulting and corporate strategy, generative AI (genAI) is becoming indispensable, equipping teams with tools to analyze market trends, optimize strategic planning, and provide more data-driven guidance to clients.
In June 2023, Accenture announced a $3 billion investment in data and AI over the next three years, while, in April 2024, Deloitte announced a $2 billion investment to help businesses with their technology transformation. According to McKinsey, genAI high-performing companies, i.e. companies that report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of genAI, are more likely to implement genAI in more business functions — not just in supply chain and inventory management — but also in risk, legal, compliance, strategy, and corporate finance.
Below, we take a closer look at the ways consultants and strategy leaders can use generative AI tools to process vast amounts of data from internal and external sources.
Reimagining the Future: Scenario Planning and Forecasting
Generative AI can simulate different market scenarios, enabling strategy teams and consultants to test “what if” conditions, forecast potential outcomes, and help clients make proactive decisions. While today most use cases for generative AI are still efficiency related, companies are beginning to recognize the potential of applying this technology in more strategic ways. According to a McKinsey survey, 46% of CFOs plan to pursue using generative AI for cashflow optimization, and 30% plan to pursue it for revenue forecasting.
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.
Innovating from Within: Operational and Product Innovation
By analyzing consumer trends, operational data, and emerging technologies, AI helps consultants and strategy teams provide guidance on product innovation, process improvements, and business growth opportunities, tailored to specific industries. 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.
Navigating Uncertainty: 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.
Staying Ahead: Market and Competitive Analysis
Generative AI can analyze news, financial reports, and industry data to provide real-time insights on market trends and competitor activities, allowing consultants and corporate strategists to identify opportunities and threats quickly. In October, EY partnered with Microsoft and launched EY Competitive Edge, a cloud-based genAI platform that provides real-time, tailored insights into markets, companies, and sectors.
As shown in the AlphaSense Generative Search query below, genAI prompts like “Create a SWOT analysis for iRobot’s Roomba” can help consultants and corporate strategists quickly generate comprehensive competitive analyses. By processing vast amounts of market data, generative AI tools can not only help with competitive analysis, but 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.
Stay Ahead of Generative AI Developments
Generative AI is not just a tool for enhancing operational efficiency — it’s becoming a transformative force, redefining how consultants and corporate strategists drive innovation and make strategic decisions. By leveraging genAI’s powerful capabilities, organizations can unlock deeper insights and respond to challenges proactively.
As this technology continues to accelerate, its role in corporate strategy and consulting will only grow, enabling businesses to stay ahead in an increasingly complex and dynamic world. We can also expect genAI to become more proactive and autonomous. Emerging genAI agents, capable of executing tasks independently, could transform decision-making by not only providing insights but also recommending or taking action in real time. As genAI transitions from a supporting tool to an active participant in strategy, the collaboration between human creativity and machine intelligence will enable organizations to innovate faster and adapt more effectively.
ABOUT THE AUTHOR
Sarah Hoffman
Director of Research, AI
Sarah Hoffman is Director of Research, AI at AlphaSense, where she explores artificial intelligence trends that will matter most to AlphaSense’s customers. Previously, Sarah was Vice President of AI and ML Research for Fidelity Investments, led FactSet’s ML and Language Technology team and worked as an Information Technology Analyst at Lehman Brothers. With a career spanning two decades in AI, ML, natural language processing, and other technologies, Sarah’s expertise has been featured in The Wall Street Journal, CNBC, VentureBeat and on Bloomberg TV, and she was named a Rising Star for WatersTechnology’s Women in Technology and Data Awards in 2018. Sarah holds a master’s degree from Columbia University in computer science with a focus on natural language processing, and a B.B.A. from Baruch College in computer information systems. Sarah is based in New York.
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