Walk into any co-working space in on a given Tuesday morning, and you will overhear roughly the same conversation. Someone is describing a clever prompt they wrote to get ChatGPT to summarise a report or circumvent a limitation for research and whatnot. Someone else is sharing a screenshot of an AI-written LinkedIn post that performed surprisingly well. A third person is demonstrating how they asked Claude to rewrite their pitch deck introduction. It is all perfectly useful but surface-level.
The businesses that are genuinely pulling ahead are not the ones with the cleverest prompts. They are the ones that have moved on from prompting as a skill and started treating AI as infrastructure, the same way they treat cloud storage or payroll software. They are building systems, not sentences.
This distinction matters enormously, and it is one that most articles, webinars and LinkedIn carousels have so far failed to address with any real seriousness.
Following are ten ways organisations can attempt togo deeper.
Claude skills and custom system prompts: Rather than typing instructions from scratch each time, businesses are building persistent "skills" within Claude, essentially pre-configured instruction sets that give the AI a consistent persona, formatting rules, tone guidelines and domain expertise. A financial advisory firm might configure a Claude skill that always responds in compliance-aware language. A recruitment agency might set one that evaluates candidate summaries against a specific rubric.
Deploying Claude or ChatGPT as an internal chatbot via API: Many organisations have stopped using Claude.ai or ChatGPT as a consumer product and instead integrated Claude directly into their Slack workspaces, Microsoft Teams channels or internal portals through the Anthropic API. Employees ask questions, submit documents and receive structured outputs without ever leaving the tools they already use.
Recording and codifying workflows with Loom and AI: Loom, the video messaging tool, has become something of a quiet favourite among operations managers who use it to record standard operating procedures. When connected to AI transcription and summarisation tools, these recordings are automatically converted into written process documents like SOPs, training materials and onboarding guides without anyone having to write a single word manually.
Autonomous agent platforms— Manus.im and its peers: This is where things become genuinely interesting. Manus.im, developed by a Chinese AI startup and described by observers as the first truly general-purpose autonomous AI agent, is capable of completing multi-step tasks independently, including browsing the internet, writing code, filling in forms and compiling research reports, all without human input at each stage. Organisations are beginning to use tools like Manus for competitive intelligence gathering, market mapping and supplier research that would previously have required a junior analyst working for several days.
Marketing workflow automation: Platforms such as Make (formerly Integromat) and n8n allow businesses to connect AI models to their existing marketing stack. A triggered automation might monitor a brand's social media mentions, pass them through an AI sentiment analysis model, and then route negative feedback directly to a customer service ticket, all in real time and without any human in the loop.
AI-assisted financial research: Tools including Perplexity Finance, alongside custom API integrations with models like Claude and GPT-4, are now being used by boutique investment firms and independent analysts to scan earnings transcripts, regulatory filings and analyst reports at a pace that was not previously achievable without a significantly larger team. The output is not a replacement for judgement, but it compresses the time required to form an initial view from hours to minutes.
CV sorting and recruitment screening: Rather than relying on keyword-based applicant tracking systems, a growing number of HR teams are feeding CVs directly into AI models with structured evaluation criteria. The AI is asked to score each application against role-specific requirements and flag inconsistencies between stated experience and job history. This process, when designed carefully, reduces unconscious bias in early-stage screening whilst also cutting the time recruiters spend on initial review by a considerable margin.
Automated competitive intelligence briefings: Using a combination of web scraping tools, RSS aggregators and AI summarisation, businesses are generating daily or weekly intelligence briefings on competitor activity, pricing changes, product launches and press coverage. What was once a task reserved for strategy consultants is now something a single operations executive can set up and maintain.
AI-powered CRM enrichment: Sales teams are connecting their CRM platforms, whether Salesforce, HubSpot or otherwise, to AI tools that automatically research new leads, populate contact fields, summarise previous interactions and suggest the most appropriate follow-up action. This reduces the administrative burden on account managers and ensures that no significant client detail falls through the gaps during a busy quarter.
Document intelligence and contract review: Legal and procurement teams are increasingly using AI tools to analyse contracts, flag unusual clauses, compare terms against standard templates and generate plain-English summaries of complex agreements. Several startups, including Luminance and Lexion, have built dedicated platforms for this purpose, though organisations with access to capable general-purpose models are achieving similar results with well-structured prompts and document upload features.
What separates those who benefit from those who do not: The common thread running through each of these applications is not the AI model itself. Claude, GPT-4 and Gemini are, at a certain level of general capability, more similar to one another than their respective marketing materials would suggest. What separates organisations that extract genuine value from those that do not is their willingness to invest in the surrounding architecture: the automations, the integrations, the carefully considered instructions, and the internal culture that treats AI as a serious operational tool rather than a novelty.
The businesses sharing prompt screenshots on Tuesday morning are not wrong to do so. Starting with the basics is entirely sensible. However, there is a growing gap between those who have moved on and those who have not, and that gap is widening faster than most observers currently appreciate. The prompt box was always the entry point, never the destination.
hishamuddinkhan@gmail.com
© 2026 - All Rights with The Financial Express
