MAKIN 
Real examples, tools and a step-by-step implementation guide.
The problem isn’t AI.
Most teams that "tried AI" wired a chatbot to a help page and called it a pilot. The hours do not sit in conversation; they sit in the small, repeated hand-offs between tools — copying an order number, re-typing an address, chasing an approval. Agents earn their keep when they own one of those hand-offs end to end, not when they answer questions nobody asked.
Start with the work that repeats daily.
Write down every task somebody does more than once a day. Most lists come back with twenty items and four that matter: invoice capture, lead routing, status updates, and report assembly. Those four are where the ten hours live. Automate one, measure it for a fortnight, then take the next — a sequence beats a platform rollout every time.
Give the agent a narrow job and real data.
An agent with access to everything makes confident mistakes. One with a defined input, a defined output and read access to a single system is reliable enough to trust unattended. Draft the reply, do not send it. Propose the category, do not commit it. Once the accept rate passes about nine in ten, widen the mandate.
Measure the hours, not the novelty.
Log how long the task took before and how long the review takes now. If a human still checks every output and the check costs what the task did, you have moved the work rather than removed it. The projects that survive their first quarter are the boring ones with a number attached.
Ways one to five: the daily hand-offs
The list starts with the four jobs named above and adds six more. Each is written as the agent’s job description: what it reads, what it produces, and where a person stays in the loop. None of them asks the agent to speak to a customer unsupervised on day one.
- Invoice capture: reads supplier bills from a shared inbox, extracts supplier, amount, tax and due date, and proposes a draft entry the accountant approves
- Lead routing: reads enquiries from the website, email and marketplaces such as IndiaMART, tags product and location, and proposes an owner by your written rule for the sales lead to confirm
- Order status replies: looks up the order and drafts an answer with the dispatch detail; on WhatsApp, replies outside the service window need a pre-approved template
- Report assembly: pulls the same figures into your existing report layout and flags anything that moved sharply, so the reviewer reads exceptions rather than tables
- Support reply drafts: finds similar past tickets and the relevant policy, then drafts a reply for your support agent to edit and send
Ways six to ten: the chases and checks between colleagues
The second half of the list is less visible because it happens between colleagues rather than with customers. These are the chases, lookups and cross-checks that interrupt someone senior throughout the day. They also carry less risk while the team learns to review drafts, because a wrong draft stays inside the building.
- Approval chasing: spots requests waiting too long, sends the approver a summary with the document attached, and escalates to a named deputy by your rule
- Document matching: compares purchase order, goods received note and supplier invoice line by line, and raises only mismatches in quantity, rate or tax
- Policy lookups: answers staff questions from your own manuals and SOPs, quoting the passage it relied on so the reader can check it
- Questions to the books: who owes what, which stock is slow, how a customer’s margin looks, each answer tied to the vouchers behind it
- Call notes to CRM: turns a call summary into updated fields and a dated follow-up task, which the owner confirms before it saves
The tools involved, most of which you already own
An agent does not need to be another product for your team to learn. It is written instructions, a language model and connections to the systems that already hold the work: email, an accounting package such as Tally, your CRM, the shared drive, WhatsApp Business and the Google Sheets that quietly run the rest. It reads from those and writes drafts back into them.
Around that core sit the unglamorous pieces: a review queue where drafts wait for a person, a log of every input, output and decision so a wrong answer can be traced, and a switch that stops the agent without stopping the business. If a proposal leaves any of them out, ask why.
A step-by-step implementation guide
This is the sequence we follow for each of the ten, one at a time. It turns the principles above into a checklist your own team can run before involving anyone outside it, and it keeps each agent small enough to judge on its own record.
- Pick one task, time it as it runs today, and name the person who does it; they own the trial
- Write the job down: the input, the output, the single system it reads and the decision a person still makes
- Collect a batch of real past cases, including the awkward ones, and record the correct output for each
- Run the agent against those cases before it touches live work, and read every miss
- Go live in draft mode: the agent proposes, the owner accepts, edits or rejects
- Log the accept rate and review time next to the baseline you recorded
- Widen the mandate only once the accept rate clears the bar above, and keep logging
Mistakes that quietly cancel the gain
Watch for drift rather than a visible failure: drafts get ignored, the review takes as long as the task did, or nobody knows who fixes a wrong answer. Each pattern below is cheap to prevent at the start and expensive to unpick once the team has stopped trusting the output.
- Automating a process nobody agrees on: if colleagues would each do it differently, write the rule first
- Starting with the monthly job because it feels important; daily work gives you more cases to test against
- Granting write access early: drafts are cheap to reject, posted entries are not
- Leaving exceptions ownerless: every agent meets inputs it cannot handle, and those need a named person
- Keeping the instructions in one person’s head instead of a shared, editable document
Questions to settle before you build or buy
Whether you build in-house or bring someone in, settle the same questions about the agent before any work starts: which hand-off it removes, what it may read and write, what happens when it is unsure, where decisions are logged, and how the team works if it is switched off.
If the answers point to a task inside tools you already use, our AI agent packages set out what each covers. AI Agent Workflows starts at ₹85,000, the AI Support Copilot at ₹45,000 and the Tally AI Assistant at ₹22,000; your fixed price arrives in writing after the scope call.