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Top Opportunities for Personal AI Productivity this Year
Four tips to work smarter, not harder in 2025
Mark Hinkle
December 27, 2024 • Estimated Reading Time: 14 minutes
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If you took time this year to spend time with family and friends and celebrate the holidays I hope it was epic.
I am writing this edition from my little RV parked in the driveway of my parents’ house in central PA, it’s been a good holiday but as with every holiday, it’s about to end.
So I am taking my time thinking about how I can go about planning my 2025.
Notice I said, thinking. More on that later.
For those of you who have been subscribed for a while to The Artificially Intelligent Enterprise, you probably have noticed that I am always tinkering with the format.
For those new subscribers, I’ve been trying to distill down what I think are the most important tips, strategies, and tools for AI business success.
I also have been looking at our survey results from our readers and will continue to try to improve.
What you’ve told me is that you want more tools. So I am adding more tools to the AI Toolbox. I am also working on merging the AI Efficiency and Prompt of the Week as nine times out of ten they are complementary anyhow.
If you’d like to share your thoughts on the content, and what you want to see in 2025 from the AIE. Then please take this quick six-question survey.
The Artificially Intelligent Network (The AIE) is a collection of newsletters for business people looking to leverage AI. Here are this week’s top stories.
🎯 The AI Marketing Advantage - Key Insights Marketers Need in the AI Search Age
💡 AI CIO - CIO's Must-Have AI Tool for 2025
Top Opportunities for Personal AI Productivity This Year
Four tips to work smarter, not harder in 2025
If 2024 was the year AI made everyone curious, 2025 is when it proves its value. Boards have tested automation pilots, generative apps, and data initiatives. Now, they want to see measurable gains.
However, as a solopreneur, I am both the board and the front lines, so I have identified the the following four areas as most ripe for productivity personally—automation with agents, refined prompting, streamlined email, and AI-powered finance—offer a clear path to higher productivity in the coming year.
Automate Mundane Work with Agents
Why It Matters
Repetitive tasks drain time. Agents—autonomous task executors—handle these without constant oversight.
Action Steps
Identify Low-Value Tasks: Look for data entry, scheduling, or bulk updates.
Pick an Agent Framework: I am currently looking at Taskade and MindStudio. But there are so many to choose from, my criteria, are ones that allow for workflows to be created from natural language not some type of other interface.
Iterate and Improve: Track progress and refine logic as you learn.
Practical Example: I use holiday downtime to set up newsletter research agents I want them to comb through the AI news and identify the trends that are most prevalent along with sorting out the noise.
Tune-Up Your Prompts
Why It Matters
AI models follow instructions. Better prompts produce accurate, concise outputs. Also, these prompts may form the basis for the instructions for your agents.
Action Steps
Use Few-Shot or Many-Shot Examples: Showcase the desired format and tone. This is really how you guide prompts to make them amazing, take the time to show the model what an ideal output looks like and you’ll be getting more accurate results that match them.
Maintain a Prompt Library: Keep track of what works for repeat use.
Test Different Structures: Vary your approach. Each change can raise success rates by double digits
Pro Tip: Use a prompt library where you keep your prompts drafted, I use Notion. Then I copy and paste them into PromptForge (a Chrome extension) so I can cut and paste them into ChatGPT, Claude, Midjourney, etc. as I am doing my work.
Reduce Email Overload
Why It Matters
Email can chew through a large chunk of your workday. AI-driven tools free you to focus on bigger tasks.
Action Steps
Enable AI Email Filters: Tag and prioritize incoming mail automatically.
Adopt Smart Auto-Responses: Approve or tweak suggested replies.
Check Usage Data: See which contacts or topics fill your inbox the most.
Efficiency Boost: A Harvard Business Review study found advanced email triage reduces response times by 25%. I dove deep into this topic last month and I think you should too, if you want to save a ton of time.
Automate Business Finance Tracking with AI
Why It Matters
Manual invoices, approvals, and reconciliations stall productivity and introduce errors. AI-powered tools reduce these burdens by spotting anomalies, predicting cash flow trends, and streamlining financial reporting.
Action Steps
Leverage AI-Enabled ERPs
Systems like SAP Concur, NetSuite, and Bill.com integrate with your banking data to automate accounts payable and receivable.
Best Practice: Maintain clean data to ensure AI algorithms classify expenses and flag issues accurately.
Set Smart Alerts
Advanced expense platforms—such as Ramp—use machine learning to highlight out-of-pattern spending.
Best Practice: Tune alert thresholds to align with historical spending behaviors and company policies.
Automate Reconciliations
Tools like AvidXchange use optical character recognition (OCR) and AI-driven matching to expedite monthly or quarterly closings.
Best Practice: Validate the reconciliation logic periodically and document exceptions for regulatory compliance.
Strengthen Forecasting and Cash Flow Management
AI forecasting tools in QuickBooks Advanced or Xero provide rolling predictions on revenue and capital requirements.
Best Practice: Compare forecasts with actual results to refine the model and identify early warning signs of cash flow gaps.
Looking Ahead
As the new year begins, these four areas can drive tangible results and free your teams to tackle strategic goals. AI isn’t a cure-all, but it’s a proven accelerator when aimed at specific pain points. So set your roadmap, measure what matters, and use technology as a force multiplier in 2025.
Aimfox - Launch AI-personalized outbound campaigns and streamline lead management on LinkedIn. Automate unlimited LinkedIn accounts, unify their conversations and sync their connections within a single Dashboard.
AISmartCube - Low-code platform to build, automate, and enhance AI tools and assistants. Access a rich library of ready-made solutions, streamline workflows, and integrate public knowledge bases to create smarter tools effortlessly.
Plus AI for PowerPoint - Create PowerPoint decks with AI and edit slides with AI. Plus AI works where you do, so you don't have to learn how to use a new app.
(I like this approach better than another editor that generates slides outside of PowerPoint or Google Slides).
ChatGPT Prompt Tune-Up
Do you have tasks you repeat, often? My biggest and most effective piece of advice is to codify that task into a prompt and then save it for reuse.
Do you want to make the prompt for that more effective? You can use the following CustomGPTs in the ChatGPT store, Prompt Engineer by AiToolReport or Prompt Engineering by Rafeal Bittencourt.
Below is a structured meta-prompt designed to enhance an existing prompt. It includes adding a role, defining a clear objective, incorporating examples, and making the interaction dynamic for better user engagement.
You can use this as a template or you can cut and paste this and your existing prompt into ChatGPT to create a better combination of the two prompts.
# Meta Prompt: Improving Your ChatGPT Prompt
## Step 1: Define the Role
Specify the role ChatGPT should adopt for the task. The role provides context and tone, ensuring the responses are relevant and aligned with expectations.
**Template:**
"You are a [specific role, e.g., 'marketing strategist', 'data scientist', 'AI consultant'] tasked with [brief description of responsibility]."
**Example:**
"You are a marketing strategist tasked with creating a social media content plan for a technology startup."
---
## Step 2: Clarify the Objective
Clearly state the purpose of the task or the desired outcome. The objective should be specific, measurable, and actionable.
**Template:**
"Your objective is to [specific goal, e.g., 'write a professional summary', 'generate 5 creative ideas for...', 'analyze the impact of...']."
**Example:**
"Your objective is to draft a professional LinkedIn post highlighting the benefits of AI for enterprise users."
---
## Step 3: Provide Examples for Reference
Include examples or guidelines to set expectations for the response. These help ChatGPT understand the desired structure, tone, or content style.
**Template:**
"Here are examples to guide your response:
1. [Brief example 1]
2. [Brief example 2]"
**Example:**
"Here are examples to guide your response:
1. 'AI simplifies complex workflows, saving enterprises time and resources.'
2. 'Discover how machine learning drives innovation in logistics and supply chain management.'"
---
## Step 4: Make It Interactive
Encourage iterative refinement by asking the AI to seek clarification, suggest alternatives, or evaluate its output.
**Template:**
"Engage interactively by:
- Asking clarifying questions if any part of the task is ambiguous.
- Suggesting additional ideas or angles for consideration.
- Reviewing the response and identifying areas for improvement."
**Example:**
"Engage interactively by:
- Proposing multiple headlines for the LinkedIn post.
- Highlighting different benefits of AI for enterprise users.
- Asking if additional context or detail is needed for better alignment."
---
## Step 5: Compile the Final Prompt
Combine all elements into a cohesive prompt for ChatGPT.
**Example:**
"You are a marketing strategist tasked with creating a LinkedIn post for enterprise executives. Your objective is to highlight how AI can improve productivity in large organizations. Here are examples to guide your response:
1. 'AI simplifies complex workflows, saving enterprises time and resources.'
2. 'Discover how machine learning drives innovation in logistics and supply chain management.'
Engage interactively by:
- Proposing multiple headlines for the post.
- Highlighting different benefits of AI for enterprises.
- Asking if additional context or detail is needed for better alignment.Get Your Models to Thinking Longer
Extending Processing Time for Large Language Models (LLMs)
Large Language Models (LLMs) are typically optimized for rapid responses, balancing performance and speed. However, recent advancements suggest that allowing LLMs extended "thinking time" during their response generation can improve results, particularly for complex or nuanced queries. These techniques simulate a more deliberate reasoning process, yielding outputs that are better structured, more accurate, and contextually aware.
Techniques for Extending Processing Time in LLMs
Chain of Thought (CoT) Prompting
What It Is: CoT prompting encourages the LLM to articulate intermediate reasoning steps before concluding. Instead of directly answering a question, the model "thinks aloud," breaking complex tasks into smaller, manageable parts.
Example:
Prompt: "Explain step-by-step how to calculate the net present value of an investment project before providing the answer."
Outcome: The model will outline the formula, discuss discount rates, and identify cash flows, leading to a more accurate final answer.
Self-Reflection Prompts
What It Is: Self-reflection prompts instruct the LLM to evaluate and refine its response. This technique mimics an iterative editing process, where the model generates an answer, reviews it for accuracy or clarity, and improves upon it if needed.
Example:
Prompt: "Provide your response to the following question. Afterward, critically evaluate your answer for any gaps or errors and suggest improvements."
ReACT Framework (Reasoning and Acting)
What It Is: ReACT combines reasoning with decision-making. The model alternates between reasoning about a problem and taking a specific action to solve it. This structured interplay reduces errors and enhances contextual accuracy.
Example:
Prompt: "For the following task, alternate between reasoning steps and suggested actions until you reach a solution. Clearly indicate when each phase starts."
For a deeper understanding of the techniques that enhance the reasoning capabilities of Large Language Models (LLMs), consider the following resources:
Chain-of-Thought Prompting: This method encourages LLMs to generate intermediate reasoning steps, improving their ability to handle complex tasks.
ReAct Framework: ReAct integrates reasoning and acting by prompting LLMs to produce both reasoning traces and task-specific actions in an interleaved manner, enhancing decision-making and problem-solving abilities.
Automatic Chain of Thought Prompting: This approach automates the generation of reasoning chains for LLMs, eliminating the need for manual crafting of prompts and improving performance on various reasoning tasks.
ChainLM: An advanced model that employs improved chain-of-thought prompting to enhance the reasoning capabilities of LLMs, demonstrating superior performance on complex reasoning problems.
How did we do with this edition of the AIE? |
 | Your AI Sherpa, Mark R. Hinkle |
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