Lauri Sulonen, who oversees financial strategy at the Finnish mobile gaming firm Supercell, once harbored doubts about the degree to which artificial intelligence could revolutionize his professional role. “I believed a profound understanding of the background and extensive interaction with individuals were essential to accomplish tasks . . . a challenge for AI.”
Nevertheless, when he directed an AI-driven “analyst assistant” to generate the monthly performance report, a task that typically consumed three hours for his team, it was completed in merely five minutes. Sulonen asserts that the AI committed no errors, the output quality was high, and it furnished references to verify the figures. “I was quite pessimistic prior to commencing this . . . I’ve revised my presuppositions.”
His AI collaborator for this assignment was Pigment, a specialized business planning platform based in France, which managed the “fundamental repetitive operations that are least engaging for us, yet most susceptible to human fallibility.”
While certain enterprises are still in the experimental phase, an increasing number are discovering that routine analytical endeavors—ranging from projections to financial modeling—as well as investigative work and content drafting, can now be executed almost instantaneously by software agents. Many have already implemented specific utilities that have transformed the work of professionals within their respective fields—for instance, Harvey in legal services, Writer for corporate communications, Synthesia for instructional material, and Intercom’s Fin for client assistance. Some corporations are developing their own tailored instruments internally.
However, an announcement issued last week by the AI company Anthropic signaled a cautionary note regarding the potential future landscape for AI in the workplace. The firm unveiled a suite of new functionalities capable of being customized for particular sectors such as law, finance, sales, marketing, and customer support, and are equipped to perform white-collar responsibilities with minimal human intervention.
The debut ignited concerns among investors who had been banking on more industry-specific AI advancements, which they suddenly perceived as more exposed. A comparable pattern was apparent in the wealth management domain, where the market values of several companies declined last week amidst apprehensions about potential disruption stemming from a novel AI-led investment tool.
The Anthropic disclosure reverberated throughout corporate environments, prompting employees already utilizing customized AI tools for more mundane duties to explore alternative options, and to evaluate whether these augment the risk of their positions being superseded.
Up until now, corporations such as Anthropic and OpenAI have constructed expansive AI models that essentially serve as foundational frameworks for developers and businesses to subsequently create more niche utilities for attorneys, financiers, advisors, and other specialists. Goldman Sachs recently declared its collaboration with Anthropic on an AI agent designed to automate functions at the bank. The company states that Uber, Netflix, Salesforce, and Allianz also deploy its models in a similar fashion.
Anthropic’s newly introduced tools, released under its Claude Cowork platform, present businesses with a unified agentic platform, which some believe could negate the necessity for multiple specialized subscriptions or costly in-house development, and potentially amplify productivity gains.
They incorporate customizable “plug-ins” for company-specific AI processes, such as a utility to automate legal contract assessments, and “subagents” for distinct assignments like data visualization. The new offerings represent an evolution of the company’s Claude Code, which leverages large language models to produce lines of computer programming. “It’s the identical potent agent, but considerably more accessible,” Guillaume Princen, Anthropic’s head of digital native businesses, conveyed to the FT following the launch.
Specialized AI enterprises have retorted, asserting that their systems possess superior checks and balances, audit trails, and additional security layers that a generic agent has yet to demonstrate it can rival. Custom developers claim their advantage resides in their capacity to transform what was once an unwieldy model into something more practical and dependable that can be more readily integrated into current operational flows.
Companies such as the advertising conglomerate Publicis and legal services provider Relx have been among those proclaiming their swift embrace of technology they have vowed to intertwine with internal data and acumen.
“It’s tempting to presume that increasingly capable general-purpose AI will merely supplant sector-specific legal instruments,” remarks Harry Borovick, general counsel and AI governance officer at Luminance, a UK-based AI document review and analysis company. He observes that his industry mandates systems capable of functioning across intricate cross-border, privacy, governance, and auditing scenarios. “This implies that consistency and confidence are paramount, and that . . . domain-specific tools only gain in significance.”
A number of legal practitioners and legal tech vendors have contended that the Anthropic tool is less efficacious than other available offerings. Harvey and Legora—the two leading legal AI firms—utilize models from Anthropic and OpenAI to power their systems but have devised their own utilities to operate upon them.
In a LinkedIn update following Anthropic’s announcement, Legora’s chief executive, Max Junestrand, clarified that he did not view the new plug-ins as a menace. “There exists a crucial distinction between a plug-in and operating a collaborative . . . production-grade platform employed by hundreds of the globe’s foremost legal teams,” he penned.
Legal personnel who have experimented with the Anthropic product conveyed comparable viewpoints on social media. One criticized the plug-in for drawing upon Wikipedia as a reference source.
Analysts at JPMorgan suggest that Claude Cowork does not alter the competitive landscape for Relx’s legal service. “Claude Cowork is merely catching up with the solutions already presented by Harvey and Relx, and considering the absence of a comprehensive legal repository, it appears improbable it will ever be able to equal the full array of agentic solutions provided by Relx.”
LexisNexis’s AI offering for legal professionals debuted in January, providing a collection of hundreds of pre-configured, adaptable workflows spanning disputes, case methodologies, and other legal procedures that can be deployed immediately or customized using firm-specific directives.
Analysts indicate that the new functionalities could present a heightened risk to the advertising sector.
This year’s Super Bowl demonstrated the degree to which AI is assuming control of advertising, with Svedka Vodka employing it to aid in the creation of a commercial.
Tools capable of transforming simple text prompts into advertisements within minutes are already accessible to the clientele of most major advertising conglomerates, despite their profoundly disinflationary impact on what was once a premium service, frequently compensated by the hour. AI assistants can help with audience targeting, media strategizing, and campaign development.
An advertising executive suggests that the “generic” models being offered by Anthropic might pose a greater threat to specific industry tools, but believes the extensive data and client intelligence within the large agencies afford them an advantage in crafting more sophisticated advertising campaigns. Major agencies such as WPP already leverage Gemini, OpenAI, and Anthropic to supply the intelligence for their proprietary models.
A more significant hazard is that clients will increasingly undertake the work themselves, as marketing divisions develop their own tools using Claude, which they could then utilize to generate their own campaigns.
Eléonore Crespo, co-chief executive of Pigment, the platform utilized by Supercell, states that specialized AI providers “achieve success because they . . . comprehend unique data frameworks, integrate into specific operational processes, and furnish the governance and auditability that highly regulated sectors necessitate.”
While “a generalist model offers a compelling, low-barrier entry point for exploration,” she adds, “in reality, we frequently perceive that as an initial step rather than a conclusive destination. The truth is that generalists are for experimentation, but specialists are for genuine work.”