Creating your AI strategy: Where to start without getting overwhelmed
Your 8-week plan to implement AI for your team
Summary
January 2025. Your CEO wants an "AI strategy". Your peers keep throwing terms like agents, copilots and LLMs. But all you really want to know is - where do I start on Monday? 😅
Don't worry - we've got you covered with a concrete plan. From our experience, successful AI implementations follow an 8-week cycle: 2 weeks to test approaches, 2 weeks to measure impact, 2 weeks to build the business case, and 2 weeks to train your team. Teams following this approach have seen dramatic improvements - cutting their workload in half for tasks like sending personalized outbound messages and creating social media content at scale.
To help you navigate this journey systematically, we'll break down AI implementation into three distinct phases, each building on the success of the previous one.
Building your team’s AI strategy
We recommend a three-phase approach to implementing AI:
Phase 1: Start by automating tasks that consume >4 hours of your team's time every week
Phase 2: Connect successful automations from Phase 1 and automate “end-to-end workflows”
Phase 3: Build “autonomous systems” that can plan and execute complex workflows by themselves
Let's dive into how to execute each phase.
Phase-1 (Now): Automate specific tasks with AI
What is it?
For Phase 1, start by identifying tasks that meet these four criteria:
They are repetitive, frequent and manual (think about tasks your team hates doing for 4+ hours every week)
They are well-defined i.e. following a clear and predictable process
It is easy to measure if the output is good or not
The risk is low if AI makes a mistake
Let us look at three real-world examples that are good candidates for such AI point solutions:
Sales: Personalizing 50+ LinkedIn connection requests to prospects (we explained this in detail in our previous post)
Before: 5+ hours per week researching and personalizing
After: 30 minutes using AI assistant + human review
Tools: Custom GPT or Claude project, with advanced prompts
Marketing: Creating tens of social media posts every week
Before: 10 hours/week writing + editing
After: 2 hours with AI assistant + human editing
Tools: Claude or GPT project with multiple prompts
Operations: Weekly analysis of customer support tickets
Before: 6 hours collecting + summarizing tickets
After: 1 hour reviewing AI-generated summaries
Tools: Claude Project
How to implement?
Note that implementing this will need you to know a few things: advanced prompt engineering, and how to set up projects in Claude or GPT.
Once you have your first use case identified, you can create a focused 8-week plan:
Week 1-2: Test different prompts and approaches. Document your learnings - what works, what doesn't, and any challenges
Week 3-4: Focus on measuring impact. Track how much time you're saving and whether the quality meets or exceeds your manual process. This data will be crucial for building your business case
Week 5-6: By this time, you should have enough evidence to justify investing in enterprise tools. While the $25-30 monthly per-user cost for Claude or ChatGPT Enterprise might seem expensive, compare it to the documented time savings from your pilot to build the business case
Week 7-8: Identify 4-5 team members who are enthusiastic about AI and train them as power users. Document your proven workflows to enable smooth scaling across the entire team
To validate your progress, track these key metrics:
Calculate manpower cost savings by multiplying hours saved per employee per month by average employee cost
Monitor output quality through the number of revision requests
Track team adoption rate to ensure the solution is actually being used
Measure team satisfaction through regular NPS surveys
Phase-2 (Next 12-18 months): Automate complete workflows with AI
Once you've successfully set up several automations in Phase-1, the next logical step is to connect these individual pieces into multi-step workflows to unlock more efficiency while maintaining appropriate human oversight.
What is it?
Let’s break this down with an example. Consider a Marketing team that has already automated their social media posts for LinkedIn, X and Instagram. They can now expand this into a complete social media workflow with AI and humans working in tandem.
Some other examples are:
Sales team automating outbound sales campaigns with personalized messages
Marketing team automating SEO-website content creation
Product team analyzing customer feedback to inform the product roadmap, etc.
How to implement it?
To implement such workflows in your organization, you have two options:
Option 1: Set up a small team (1-2 internal team members + AI consultant) and build a lightweight no-code prototype. See an example of this in our previous post
Option 2: Purchase an enterprise-grade AI application (higher investment)
We recommend starting with Option 1. This helps you build a business case before asking for a larger investment.
Next, we'll explore how these automated workflows can evolve into truly autonomous systems.
Phase-3 (18+ months): Implement autonomous AI systems
What is it?
Taking our social media marketing example further, imagine an AI system (shown below) that doesn't just execute workflows but proactively manages your entire social media strategy.
The key difference from Phase 2 is that the system makes decisions and takes actions independently, only escalating specific scenarios to humans (e.g. sign off on big ticket campaigns, crisis management, etc.).
How to implement it?
⚠️ A word of caution: We highly recommend you focus on mastering Phases 1 and 2 first. Jumping to Phase 3 can take more time and deliver worse results. Keep in mind that unlike previous phases, autonomous systems are still an evolving technology. They require:
Clear problem definition and guardrails
Robust orchestration to manage multiple AI agents
A strong engineering and product team well-versed in building such systems
Extensive testing and monitoring frameworks
Common risks to manage
While technical implementation is crucial, successful AI adoption requires thoughtful change management. Here are some suggestions to address common concerns:
Address fear of job loss: Position AI as an assistant that handles repetitive tasks while humans focus on higher order, strategic tasks. Your team should evolve from doers to reviewers and decision-makers
Tackle AI skepticism: Begin with a human-in-the-loop approach for all critical decisions. Track and showcase quality improvements and time saved to build confidence
Show quick wins: Start with small, measurable pilots that demonstrate clear ROI within 30 days
Lower technical barriers: Leverage no-code tools and APIs that require minimal technical expertise
Remember: The key is to start small, stay focused on measurable impact, and let AI elevate your team's work from tactical execution to strategic leadership. 💪
What’s next?
We'd love to hear about your 8-week AI journey! Share your first AI implementation story with us on LinkedIn by tagging Henna and Somya with #AIstrategyin8weeks. What task did you start with? What surprised you? The best stories get featured in our next post. 🚀
See you next time! 👋