LLMs like ChatGPT and Claude are killer, cost-effective tools for a ton of broad business use cases – like data analysis, drafting copy, getting strategic feedback, and creating code.

For a lot of knowledge workers, your everyday use cases are largely covered by an LLM. But the more specialized you get, the less capable the frontier models become. That’s when paying for an additional AI tool with more specific capabilities is worth it.

So here’s how to identify your high-end AI use cases – and when they warrant a new subscription.

When an LLM is great and when it’s not

LLMs excel at a wide range of standard knowledge work – but you reach the limits of their capabilities when you try to use them for high-end knowledge work. Here’s the difference:

Your use case falls under standard knowledge work if it:

• Follows a set process and is done the same way across industries
• Doesn’t typically require niche specialization or training to do
• Is possible to execute well with publicly available data
• Requires consistent execution (i.e., needs to be done the same way every week)

It falls under high-end knowledge work if it:

• Requires deep domain expertise (e.g., understanding legal frameworks or abstract mathematical models)
• References a constantly evolving knowledge base (e.g., new legal precedents or influences on financial markets)
• Relies on less available data or proprietary insights
• Is complex and rarely done the same way twice

This isn’t to say that LLMS aren’t great for many strategic use cases – they can be. But high-end knowledge work requires a few things LLMs don’t have:

1. Specialized data sets: LLMs only have access to publicly available data, not data that exists behind a paywall. If your work relies on highly specific or proprietary data, you’re not likely to get super useful answers from an LLM.
   
2. Domain knowledge: LLMs are not trained to understand and interpret the nuance of more industry- or function-specific terminology and concepts.
   
3. An understanding of highly niche workflows: If you need an output in a very specific or technical format, you’ll have to spend a lot of time prompting the LLM to get there.

5 specialized AI tools for high-end knowledge work

If enough of your tasks fall under high-end knowledge work, it’s probably worth looking into a specialized tool to add to your AI tech stack (read: not to replace your LLM). Here are a few noteworthy ones:

For enterprise efficiency: Glean

Glean helps enterprise businesses with knowledge management by aggregating company data across platforms (Google Drive, Slack, Confluence, Notion, etc.)

Why it’s better a better option than an LLM:

• Unlike an LLM, Glean has direct access to internal company files, emails, and proprietary knowledge without having to upload them to a Project or Space
• It has the context awareness to understand workplace hierarchies, user permissions, and knowledge relevance, so its results are organization-specific rather than generic

The high-end knowledge work to use it for:

• Complex business research informed by the most relevant possible data, based on your past searches and intent
• Assisting execs in synthesizing company-wide knowledge for strategic decisions
• Identifying overlapping work, redundant efforts, and previous discussions to prevent wasted time and resources

For finance pros: Rogo

Rogo is an AI research platform tailored for finance professionals.

Why it’s better a better option than an LLM:

• It has access to real-time market data, financial reports, and proprietary research that general LLMs don’t
• It understands financial jargon, risk models, and compliance considerations better than a general LLM
• Unlike an LLM, which generates free-form responses, Rogo automates structured workflows, like due diligence and portfolio analysis with verifiable citations

The high-end knowledge work to use it for:

• Aggregating research on SEC filings, earnings call transcripts, analyst reports, and financial news
• Comparing financial models and corporate performance indicators
• Creating financial summaries, company overviews, and pitch decks

For legal pros: Harvey

Harvey is an AI-powered legal research and drafting assistant built specifically for lawyers and legal professionals.

Why it’s better a better option than an LLM:

• It’s trained on millions of legal documents, case law, and regulatory filings, so it understands legal language and argumentation better than an LLM
• Unlike an LLM, which still hallucinate, Harvey provides cited legal references and auditable sources
• Harvey ensures data security, confidentiality, and compliance with legal industry regulations, whereas general LLMs may not meet legal confidentiality standards

The high-end knowledge work to use it for:

• Analyzing statutes, case law, and regulatory filings for litigation and corporate law
• Summarizing relevant precedents for attorneys
• Generating first drafts of contracts, NDAs, and legal memoranda

For HR/operations pros: Eightfold AI

Eightfold AI is an AI-powered talent intelligence and workforce planning tool.

Why it’s better a better option than an LLM:

• It has access to databases of global job market trends, hiring data, workforce skills, and career trajectories that a general LLM does not
• It’s trained on millions of resumes, job descriptions, and workforce transitions to identify hiring patterns and career path recommendations

The high-end knowledge work to use it for:

• Predicting candidate success and career trajectory based on skills and job history
• Forecasting talent needs based on business growth and industry trends
• Identifying employees who can be upskilled or reskilled for new roles

For data analysts: Databricks

Databricks is an enterprise AI and data analytics platform used for large-scale data processing and business intelligence.

Why it’s better a better option than an LLM:

• Unlike an LLM, which is text-based, Databricks is code-based, allowing you to do more with your data
• It provides full control over data storage, access, and permissions
• It can ingest, process, and analyze real-time data streams, which LLMs cannot do

The high-end knowledge work to use it for:

• Building and training machine learning models for predictive analytics
• Conducting market and customer behavior analysis for strategic decision-making
• Personalizing marketing campaigns based on historical and real-time behavior

Runner up: OpenAI’s Deep Research

If you don’t want to shell out for another tool, Deep Research is a good option. Think of Deep Research as your substitute for an MBA analyst. It’s great for doing competitive analysis, product pricing comparisons, information synthesis, and high level research.

Its biggest downside is that it only has access to data that’s publicly accessible on the internet – and that doesn’t guarantee that the data is any good. You can’t peek behind paywalls, and the sources aren’t verified.

So it’s great for parsing data you give it or getting ballpark info (e.g. the general size of a client vs. a down-to-the-penny report on revenue) but its limitations mean that you don’t always get an output you can use right away.

The ROI of going specialized

Need to pitch the cost of an extra AI tool to your boss or leadership? Here’s how to think about the ROI in expanding your AI tech stack with a specialized tool:.

1. You’re making high-value employees more high-output. These tools allow one knowledge worker to do more than several people could manually because they have access to accurate, niche resources and tools that can adapt to their highly specific workflows.
2. You get a lot closer to the right output at a fraction of the cost. Even if you’re not getting 100% of the insights you need, if you’re getting 80% of the value without paying McKinsey or some other consulting firm for their data, that’s a win.‍
3. You can keep hiring costs down. If you need less people to do more of this high-end knowledge work, not only do you not have to scale headcount, you can empower lower-level employees to take on more high value work.