AI Chip Sales Skyrocket, Consultants Find Their Niche Amidst the Revolution
The artificial intelligence landscape is experiencing explosive growth, with projections indicating a staggering $1 trillion in AI chip sales by the end of 2027. This rapid expansion, however, is not without its complexities. While the allure of advanced AI models and agents promises to transform industries, a crucial piece of the puzzle remains the human element. Far from becoming obsolete, the consulting sector is finding itself more indispensable than ever in navigating the intricate integration of AI into business operations.
The initial assumption following the advent of powerful AI models like ChatGPT was that professions centred on advice and strategy, such as consulting, would be rendered redundant. After all, AI can now offer strategic insights, propose organisational restructures, and even identify third-party software solutions. Furthermore, AI agents are proving adept at stitching systems together and handling tasks like coding and customer support. This led many to believe that the era of human consultants was drawing to a close.
However, reality has painted a different picture. The very companies developing these sophisticated AI agents have discovered a significant need for external expertise. As highlighted in recent industry analyses, selling and effectively implementing AI agents often necessitates substantial organisational transformation. This includes the often-arduous tasks of data cleansing, redesigning existing workflows, and thoughtfully redeploying human workforces. Crucially, it also demands strategic foresight to identify how AI can genuinely deliver a competitive advantage.
The AI model vendors themselves lack the extensive resources required to provide this level of in-depth support at scale. For instance, OpenAI reportedly has only around 70 “forward deployed engineers” dedicated to helping clients implement their AI models. Anthropic is believed to have a similar number. While it’s conceivable that AI could eventually fulfil some of these advisory roles, a significant trust deficit persists. Many corporate boards still place greater confidence in the recommendations of established consulting firms like McKinsey or Boston Consulting Group than in the output of a chatbot. A more pragmatic view suggests that CEOs often leverage consultants to validate their decisions to the board and to provide a scapegoat should initiatives falter.
To bridge this gap, major AI players are forging strategic alliances. OpenAI has established its “Frontier Alliance” with consulting giants such as McKinsey, Boston Consulting Group, Capgemini, and Accenture, aiming to assist clients in leveraging OpenAI’s platform for building and managing AI agents. Similarly, Anthropic has formed partnerships with Deloitte, Accenture, and Cognizant, and is reportedly exploring collaborations with private equity firms like Blackstone to implement Claude-based solutions within their portfolio companies.
The Enduring Value of Domain Expertise and Human Insight
Fernando Alvarez, Chief Strategy Officer at Capgemini, recently shared his firm’s perspective on the future of consulting in the age of AI. He emphasised that while clients are eager to adopt AI agents, they are simultaneously concerned with governance, cybersecurity, and ensuring seamless integration with legacy systems and disparate data sources. These are precisely the areas where consulting firms like Capgemini have historically excelled, offering services that clients are not yet ready to entrust solely to AI.
Alvarez pointed to deep industry and domain expertise as another significant advantage for consulting firms. He noted that AI labs, while technologically advanced, often lack the specialised knowledge required to optimise complex operations like pharmaceutical manufacturing plants or streamline logistics for fast-fashion retailers. Consulting firms, conversely, possess this critical understanding. This expertise is paramount when aiming to deploy AI agents effectively. The conversations clients are seeking are not about the sheer volume of AI agents that can be deployed, but rather about whether the consulting partner truly understands their unique business problems.
Shifting from Recipes to Results: The Outcome-Based Model
Capgemini, like many of its competitors, is actively adapting its service delivery model. The focus is shifting from simply selling technology and advice to delivering tangible outcomes. In this new paradigm, consulting firms assume a greater degree of risk, taking responsibility for achieving specific results, such as improved customer support. This can be accomplished through various means, including business process outsourcing to lower-cost regions like the Philippines or India, or by leveraging AI agents.
Alvarez uses the analogy of wanting “the cake, not the recipe.” Clients are less interested in the intricate details of how a solution is built and more focused on the end result. The core proposition for consulting firms now is: “Here is the problem. Here is the risk I’m willing to take, and this is the outcome I guarantee.” Clients then pay for these guaranteed outcomes, which are measured by key performance indicators like successful customer issue resolutions and enhanced net promoter scores. A significant departure from traditional billing models, this outcome-based approach means consultants are compensated for the results achieved, not merely for the number of personnel deployed on a project.
Expanding Reach and the Human Challenge of AI Integration
The advent of AI is also enabling consulting firms to penetrate market segments previously beyond their reach, particularly midmarket companies. Historically, the economic viability of serving these smaller businesses was challenging due to the higher staffing and cost requirements associated with traditional consulting engagements. AI has now significantly lowered these barriers, allowing firms like Capgemini to offer solutions at attractive price points for midmarket clients while maintaining healthy profit margins.
However, perhaps the most significant challenge facing consulting firms is the imperative to retrain their own workforce to collaborate effectively with AI agents. Alvarez acknowledges that this transition will not be seamless for everyone, stating, “Some people will make it, some people will not.”
Despite these challenges, Alvarez expresses immense enthusiasm for the current technological era, describing it as “probably the best opportunity I’ve seen in the history of technology.” The critical question remains whether firms like Capgemini can adapt and reconfigure themselves as rapidly as the technology demands – a process that mirrors the very advice they are providing to their clients.
