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Consulting firms are experiencing a significant shift in their operations as they increasingly adopt generative AI to automate knowledge work. This adoption of AI technology is causing disruptions in the industry, leading to workforce shakeups and layoffs.
This trend was evident recently when PwC announced a reduction of around 2% of its U.S. staff, resulting in approximately 1,500 job cuts in audit and tax departments. Similarly, EY eliminated 150 roles while investing $1.4 billion in building an enterprise AI platform. Accenture also downsized by cutting 19,000 positions, reflecting the impact of AI-driven changes on the workforce.
McKinsey & Company reportedly offered senior staff up to nine months’ salary to voluntarily leave the firm, a move attributed to the downturn in consulting spending accelerated by AI-driven transformations. KPMG is also realigning its workforce as AI platforms replace certain audit processes and routine tasks, resulting in the elimination of 333 jobs, representing 4% of its U.S. audit employees.
The wave of AI-driven layoffs is further exemplified by IBM, where several hundred routine human resources roles were replaced by AI agents. This shift highlights the diminishing relevance of roles centered around repetitive tasks, prompting employees to feel threatened by AI agents and leading some to create shadow AI apps defensively to maintain their relevance.
It is evident that generative AI is reshaping knowledge work at a rapid pace, catching even industry leaders off guard.
AI layoffs are sparking a survival mindset
As fears of layoffs driven by AI and automation loom large, elite consultants and high performers in the industry are reinventing themselves to adapt before their roles become obsolete. Many teams have developed shadow AI apps to enhance efficiency and productivity in various areas such as proposal and pitch automation, financial modeling, and client relationship management.
Python is becoming the language of reinvention
Top-tier strategists, marketers, and practice leaders are increasingly proficient in creating Python-based apps to augment the existing genAI tools provided by IT. These apps leverage APIs from platforms like Open AI, Google programmable search engines, and Perplexity, allowing teams to fine-tune their shadow AI tools for customized automation and insights.
Building Shadow AI apps with enterprise-grade reach
By combining APIs and search engine IDs from various AI platforms, associates are able to develop shadow AI apps that deliver insights beyond traditional copilots and chatbots. These apps enhance the speed and accuracy of data analysis, providing a competitive edge to consulting firms.
Shadow AI is quickly emerging as the new consulting stack
An analysis of AI usage across millions of employees revealed that a significant portion of workplace AI tools are personal rather than corporate. This indicates that consultants are independently turning to these tools to enhance their productivity. Shadow AI has become a preferred tool for consultants, enabling them to produce high-quality work efficiently.
Estimating the true scale of shadow AI in consulting
Field interviews and data from various sources suggest that shadow AI is no longer a fringe phenomenon but a parallel tech stack developed by consultants themselves. The number of shadow AI apps in consulting is expected to continue growing, enhancing the delivery of high-value outputs to clients.
Shadow AI growth trajectory: What comes next
Shadow AI is scaling rapidly, outpacing sanctioned internal platforms in many firms. With a projected growth rate, the number of actively used shadow apps is expected to double by mid-2026. This evolution highlights the transformation of shadow AI from a productivity hack to a parallel delivery stack.
Projected shadow AI app growth in consulting
Projections indicate a significant increase in the number of shadow AI apps in consulting over the coming quarters, driven by the integration of these tools into client delivery workflows and the emergence of self-maintained apps.
How to strategically manage shadow AI risks
Traditional IT and cybersecurity frameworks are ill-equipped to track the use of shadow AI, leading to potential risks within enterprises. Establishing a strategic governance framework can help organizations harness the potential of AI securely and transform risks into strategic advantages.
A blueprint for governance
A detailed governance framework that includes shadow AI audits, an Office of Responsible AI, AI-aware security controls, and continuous training can help enterprises manage the risks associated with shadow AI effectively. Proactive governance can empower organizations to leverage AI securely and drive innovation.
Overall, the rise of shadow AI in consulting underscores the need for strategic adaptation to AI-driven changes in the industry. By embracing these innovations and implementing robust governance measures, consulting firms can maintain their competitive edge in a rapidly evolving landscape.
