Streamlining Operations: AI Solutions Checklist

Want to use AI in your business? Here's what you need to know in 2 minutes:

What AI Can Do	Real Results
Handle daily tasks	Save 30% of staff time
Analyze data	Find patterns instantly
Support customers	24/7 chatbot service
Make predictions	Data-driven decisions

Quick Facts:

66% of companies expect AI to change their work by 2033
AI market grows 37.3% yearly through 2030
Small businesses see big wins: UK firm hit £1M revenue with 30 people using AI

This guide walks you through 7 must-do steps:

Step	What You'll Learn
1. Check Your Setup	Data quality & tech needs
2. Prep Your Business	Map tasks & check team skills
3. Set Ground Rules	Data handling & tool selection
4. Connect Systems	Integration & task setup
5. Track Results	Measure success & ROI
6. Handle Risks	Security & compliance
7. Maintain Systems	Updates & support

Bottom Line: Skip the fancy stuff. Focus on:

Clean data
Right-sized tools
Clear goals
Trained team
Regular checks

Ready to start? Let's make AI work for your business.

1. Check Your Starting Point

Before jumping into AI, you need to look at two things: your data and tech setup.

Check Your Data Setup

Here's what you NEED to know about your data:

Data Area	What to Check	Why It Matters
Quality	Accuracy & completeness	60% of AI project time gets eaten up by data cleanup
Storage	Space & systems	Handles your growing data
Processing	Speed & power	Makes AI work fast
Security	Protection & access	Stops data breaches

IDC found something interesting: Companies waste 30% more time on AI projects when they skip data checks. Here's what trips most people up:

Data with missing pieces
Copy-paste entries
Old, wrong info
Data that doesn't match

Review Your Tech Setup

Your tools need to play nice together. Look for these basics:

Tech Need	Must-Have Features
Cloud Systems	Lots of space, fast processing
APIs	Tools that talk to each other
Security	Strong locks, encrypted data
Backup	Regular saves

Here's something wild: Appen's research shows 55% of companies jumped into AI too fast in 2020. Many had to start from scratch.

"Companies need to pick storage based on their AI goals and speed needs" - IDC Report 2023

Before You Start:

Figure out your data storage needs
Test if your tools work together
Check your processing power
Look up your industry's security rules

The numbers don't lie: IDC says the AI market's hitting $500 billion by 2024. But you'll only succeed if your data and tech are solid from day one.

2. Get Your Business Ready

Write Down Your Work Steps

Here's a fact that might surprise you: Companies waste 30% of their time on tasks that AI could handle (according to McKinsey). Let's fix that.

Here's what you need to map out:

Area to Review	What to List	Examples
Manual Tasks	Daily repetitive work	Data entry, report creation
Problem Spots	Where work gets stuck	Approval delays, data errors
Time Drains	Tasks taking too long	Customer email responses
Error Points	Where mistakes happen	Invoice processing, data input

Before you start mapping, do these 4 things:

Track task time: Watch your daily work for 2 weeks
Spot patterns: Mark tasks that keep coming back
Find errors: Note where mistakes pop up
Ask your team: List tasks people hate doing

Check Your Team's Skills

Here's something wild: Only 14% of companies have teams that know how to use AI (McKinsey data).

Let's see where YOUR team stands:

Skill Area	What to Check	Action Needed
Data Skills	Can team read reports?	Basic data training
Tech Know-how	Do they use current tools well?	Tool-specific courses
AI Knowledge	Do they know AI basics?	AI awareness sessions
Process Skills	Can they map workflows?	Process mapping training

Do a quick team check:

Count who knows basic data analysis
Make a training needs list
See who's pumped (or scared) about AI
Spot which teams need extra help

McKinsey found that automation could boost global productivity by 1.4% each year.

Before you jump in:

Get department heads to pick automation targets
Ask your team about their daily tasks
List must-have skills
Plan training BEFORE buying AI tools

Here's the thing: Skip these steps, and your AI project might flop. A bit of prep now saves a ton of trouble later.

3. Before You Start

Set Data Rules

Here's what you need to know about data handling:

Rule Type	What to Include	Why It Matters
Data Collection	Sources, formats, storage methods	Makes data ready to use
Access Control	Who can see/edit what data	Prevents data breaches
Privacy Rules	GDPR, HIPAA requirements	Keeps you compliant
Data Quality	Standards for input, cleaning steps	Improves AI performance

1. Data Setup Basics

Your data needs to be:

Easy to find with clear labels
Available to the right people
In standard formats
Well-documented

2. Core Team Members

You'll need:

Business owners
Privacy lawyers
AI experts

Pick the Right AI Tools

Here's what to look for in AI tools:

Factor	What to Check	Action Steps
Integration	Compatibility with your systems	Test it first
Scalability	Growth capacity	Check the limits
Data Needs	Storage and processing requirements	Plan cloud use
Cost vs Value	ROI potential	Start with small tests

Before you buy:

Test the tool with a small project
Check if your data works
Know the training requirements
Confirm privacy compliance

"AI changes how we handle business processes. You need to prepare well before jumping in." - Ulla Kruhse-Lehtonen, Co-Author

Key Points:

Start with small test projects
Read reviews and case studies
Get details on training support
Make sure it's user-friendly

4. Set Up totalremoto.com

Here's how to connect your systems and get your AI tasks running:

Connect Your Systems

First, let's hook everything up:

Connection Type	Setup Steps	Testing Method
WhatsApp Integration	1. Link business account 2. Set API keys 3. Configure webhooks	Send test message
Sales Bot Setup	1. Import contact lists 2. Set response rules 3. Define triggers	Run test conversation
Customer Support	1. Connect help desk 2. Map response flows 3. Set routing rules	Submit test ticket
Maintenance Alerts	1. Link monitoring tools 2. Define alert conditions 3. Set notification rules	Trigger test alert

You'll need these items ready:

API access tokens
Webhook endpoints
Database connection strings
Error logging system
Backup procedures

Plan Your AI Tasks

Here's what your AI will handle:

Task Type	Starting Point	Error Handling
Customer Messages	New WhatsApp contact	Fallback to human agent
Sales Follow-ups	Lead form submission	Retry sequence
Support Tickets	Help request	Escalation path
System Checks	Schedule or trigger	Alert notification

For each task, you'll need to:

Set input triggers
Define processing rules
Create response templates
Build decision trees
Map error paths

Your system will run on this schedule:

Message handling: 24/7
Sales outreach: Business hours
System maintenance: Off-peak times
Data backups: Daily at midnight

Keep an eye on these metrics:

Message delivery rates
Response times
Task completion rates
Error frequencies
System load levels

5. Track How It's Working

Here's what happens after you set up AI tools - and how to make sure they're doing their job.

Let's look at the numbers that matter:

Metric Type	What to Track	Example Results
Time Savings	Hours saved per task	Content teams cut formatting time from 2 hours to 30 minutes per post
Cost Impact	Money saved monthly	Data entry costs dropped from $500 to $200 per month
Output Quality	Error reduction rate	Content errors decreased from 5 to 1 per article
Productivity	Task completion rate	Blog output increased from 10 to 15 posts monthly
ROI	Return on investment	$3.50 return for every $1 spent on AI (Microsoft study)

But it's not just about the numbers. Here's what Allie K. Miller, AI Business Leader, says:

"The big thing that I want everyone to know is that it's not always about saving time...These gains are not just about time, it's about increasing quality, it's about increasing creativity, it could be a hack or tip that lowers your stress."

Keep Your AI Tools Sharp

Here's a simple schedule to keep everything running smoothly:

Time Period	Action Items	Goals
Daily	Check error logs	Spot and fix small issues
Weekly	Review metrics	Track progress toward targets
Monthly	Update AI models	Keep accuracy high
Quarterly	Full system check	Prevent major problems

Google Cloud's Head of AI Services, Nitin Aggarwal, points out a common issue:

"Models inherit the flaws of the data used to train them. Without proper data governance, models can easily be trained on low-quality, biased, or irrelevant data, increasing the chances of hallucination or problematic outputs."

What to Watch For:

Data drift problems
Bias in what your AI produces
Training data quality
System accuracy scores
Backup status

Most teams see their AI tools hit their stride within 12 months. Just keep an eye on those numbers and make tweaks as needed.

6. Handle Risks

Here's exactly how to protect your AI systems and stay compliant.

Safety Steps

Security Layer	What to Do	Why It Matters
Data Protection	Use encryption, limit access	Stops data breaches
System Monitoring	Set up alerts, track usage	Catches problems fast
Backup Systems	Daily backups, offsite storage	Protects your data
Testing	Regular security checks	Identifies vulnerabilities

Here's what Tal Zamir, CTO at Perception Point, says about AI security:

"AI security encompasses measures and technologies designed to protect AI systems from unauthorized access, manipulation, and malicious attacks."

The numbers don't lie:

79% of IT leaders see AI security as a top concern
73% worry about bias in AI outputs
Companies that back up daily cut data loss by 60%

Follow Rules

Rule Type	What You Need	How to Do It
Data Privacy	GDPR, CCPA compliance	Review data handling
Industry Rules	Field-specific standards	Get required certifications
Record Keeping	Audit trails	Document AI actions
Safety Checks	Regular testing	Do weekly tests

Want to keep your AI system safe? Do these things:

Screen data BEFORE it goes into your AI
Document EVERY AI decision
Check outputs for errors
Create clear usage guidelines
Back up your data

Here's your basic safety plan:

Protection: Set up data safeguards
Backups: Create backup protocols
Compliance: Get required permits
Training: Prep your team
Testing: Run regular checks

Key timeframes to remember:

Security updates: Every 30 days
Data backups: Every 24 hours
Log reviews: Every week
System tests: Every month

Stick to these guidelines and you'll dodge most AI security headaches.

7. Keep Things Running

Here's exactly how to maintain your AI systems for peak performance.

Schedule Updates

Your AI system needs regular check-ups - just like your car. Here's what to do and when:

Update Type	Frequency	Tasks
System Scans	Monthly	Check for vulnerabilities, run security tests
Storage Review	Quarterly	Check data usage, clean old files
Performance Checks	Weekly	Monitor speed, fix bugs
Data Quality	Daily	Check input accuracy, verify outputs

You'll need these four tools:

SysMonitor to spot system issues
UpdateManager for software patches
Real-time problem alerts
Automatic data backups

The numbers speak for themselves:

AI market size will reach $407 billion by 2027. And 80% of manufacturing CEOs plan to invest in AI updates by 2025.

Set Up Help Systems

Your team needs support when things go wrong. Here's what works:

Support Type	What to Include	How Often to Update
Tech Support	Phone, chat, email help	Daily coverage
Help Guides	Step-by-step instructions	Monthly updates
Training	Video guides, practice tasks	Quarterly reviews
Documents	Rules, processes, fixes	Monthly updates

Focus on these basics:

Simple problem-solving steps
Fast issue reporting
Easy-to-find guides
Regular team training

Your daily checklist:

Monitor system health
Address issues FAST
Update documentation
Back up your data

AI doesn't just need maintenance - it helps WITH maintenance. It can:

Detect weird readings
Predict part failures
Track inventory
Create work guides

Bottom line? Smooth operations = happy users. Keep your system healthy, and it'll keep your business running.

Wrap-Up

Here's what makes AI work in business - no fluff, just facts:

Key Area	What You Need	Why It Matters
Data	Clean, updated data sets	85% of AI projects fail without good data
Skills	Tech + business know-how	Teams need both to use AI well
Tools	Right-sized AI solutions	Pick tools that fit your needs
Goals	Clear targets	Know what success looks like

1. Start Small

Test one project first. Here's why:

You'll spot problems fast
Fixes cost less
Your team learns the ropes
You won't waste resources

2. Pick the Right Tasks

Look for work that:

Eats up staff time
Has clear steps
Uses lots of data
Happens every day

3. Track Everything

Keep tabs on:

Speed of work
Error rates
Cost savings
Team feedback

"Beware of implementing AI just for its own sake without a solid business rationale." - Zohar Bronfman, Co-founder and CEO of Pecan AI

Here's your game plan:

Step	Action	Time Frame
Check Data	Count what data you have	Week 1
Pick Tasks	List what AI can help with	Week 2
Test Run	Try one small project	Month 1
Train Team	Get everyone ready	Month 2
Track Results	Measure what works	Ongoing

The bottom line? AI works when you:

Set clear goals
Use clean data
Train your people
Test small first
Check your results

That's it. No magic tricks - just solid planning and smart execution.

FAQs

What is an example of a business process automation?

Let's look at BPA in action with three common examples:

Process Type	What Gets Automated	Results
Purchase Orders	• Order creation from requests • Approval workflows • PO generation	Tipalti Approve handles everything - from employee requests to PO creation
AP Processing	• Supplier onboarding • Invoice data capture • Payment approvals	Tipalti manages the full AP cycle, from vendor setup to payment
Expense Reports	• Receipt uploads • Report filing • Approval routing	Software runs the process from receipt photo to final approval

Here's what makes these systems work:

They connect with your existing software
They use clear approval rules
They store everything digitally
They eliminate paperwork

Let's break down AP automation:

Your team uploads bills to a portal. The system grabs the important info, checks the numbers against your rules, notifies the right people, and sends payments when they're due.

Want to know which processes to automate first? Look for tasks that:

Run regularly
Follow fixed steps
Need several approvals
Use many forms

This approach helps your team work faster and make fewer mistakes - without hiring more people.

Streamlining Operations: AI Solutions Checklist