PROMT: You are the greatest CTO in the world, and you can predict the future of artificial intelligence technology for the next 2 years. What would be your action plan?
Google Gemini free produced the following plan of action.
Excellent. The pace of change in artificial intelligence is unprecedented. As CTO, my role isn’t just to keep up; it’s to ensure we are the ones setting the pace. My action plan for the next two years is not about chasing every new model but about building a durable, strategic advantage.
The guiding principle is this: The future isn’t about having AI; it’s about becoming an AI-native organization. This means AI is not a feature or a tool, but the fundamental operating system for our business.
Here is my two-year action plan, broken down into three core pillars.
Pillar 1: The “Intelligence Engine” — Building Foundational Capability
This is the bedrock. Without a superior, efficient, and adaptable engine, all our applications will be mediocre. We will not be beholden to a single provider; we will be model-agnostic and data-centric.
- Vision: To possess a proprietary, cost-optimized AI platform that can leverage the best global models (open-source and proprietary) on our unique data, giving us a defensible competitive moat.
- Key Initiatives:
- Establish a Model Agility Framework (MAF): Within 6 months, we will build an abstraction layer that allows our developers to seamlessly switch between different LLMs (e.g., OpenAI, Anthropic, Google’s Gemini, open-source models like Llama 3) with a single API call.
- Why: This de-risks our platform from dependency on any single vendor, prevents vendor lock-in, and allows us to use the most cost-effective model for each specific task (e.g., a small, fast model for summarization; a powerful model for complex reasoning).
- KPI: Reduce per-task inference cost by 40% by selecting the optimal model. 100% of new AI features built on the MAF by Q4 2025.
- Deploy a “Corporate Brain” using Advanced RAG: We will move beyond simple Retrieval-Augmented Generation. By Q2 2026, we will deploy a sophisticated, multi-modal RAG architecture that indexes not just our documents and wikis, but also our code repositories, Slack conversations, and structured database data.
- Why: The next breakthrough in AI value is context. An AI that has deep, real-time knowledge of our company’s history, decisions, and data will provide exponentially better insights and automation.
- KPI: Reduce time-to-information for internal queries by 75%. Enable AI agents to autonomously handle 50% of internal support tickets (IT, HR).
- Future-Proof our Compute Strategy: Immediately begin a dual-track strategy for compute. Secure GPU capacity with major cloud providers for the next 18 months, but simultaneously launch an R&D initiative into more efficient hardware, including smaller, specialized models that can run on-device or on CPU.
- Why: The GPU bottleneck is real and expensive. The future belongs to those who can achieve 90% of the performance for 10% of the cost.
- KPI: Run 30% of our total inference workload on non-GPU or optimized, smaller models by Q4 2026, significantly reducing our TCO.
- Establish a Model Agility Framework (MAF): Within 6 months, we will build an abstraction layer that allows our developers to seamlessly switch between different LLMs (e.g., OpenAI, Anthropic, Google’s Gemini, open-source models like Llama 3) with a single API call.
Pillar 2: “AI-Native” — Product and Operational Integration
This is where the engine meets the road. We will systematically re-imagine our products and internal operations through the lens of agentic, multimodal AI.
- Vision: To transition from “AI-assisted” features to truly “AI-native” products and workflows that are impossible without this technology.
- Key Initiatives:
- Launch the “Agentic Workforce”: By Q1 2026, we will identify the top 5 most expensive and repetitive internal workflows (e.g., financial reconciliation, marketing campaign analysis, software QA testing). We will build and deploy AI agents that can execute these multi-step tasks autonomously, using tools just like a human employee would.
- Why: This is the shift from a conversational chatbot to a digital team member. It’s the single biggest driver of operational leverage.
- KPI: Automate 200,000 person-hours of manual work annually by the end of Year 2. Increase deployment speed of new software by 25% through AI-driven QA.
- Develop a Multimodal Experience Lab: Today’s AI is largely text-based. The next 24 months will be defined by seamless interaction across text, voice, images, and video generation. We will create a dedicated lab to build prototypes for our next-generation customer interface.
- Why: Imagine a customer support session where a user can show their problem via phone camera, and an AI agent provides a custom-generated video tutorial to fix it, live. The first to market with this wins the customer experience battle.
- KPI: Launch one major customer-facing multimodal feature into public beta by Q3 2026.
- Hyper-Personalization as a Standard: All customer-facing products will incorporate our “Corporate Brain” to deliver a level of personalization that feels like a dedicated concierge for every user.
- Why: Generic experiences are dead. An AI that remembers every past interaction and proactively suggests solutions before the user even asks is the new standard for engagement and retention.
- KPI: Increase customer retention by 5% and engagement metrics by 15% in products with hyper-personalization enabled.
- Launch the “Agentic Workforce”: By Q1 2026, we will identify the top 5 most expensive and repetitive internal workflows (e.g., financial reconciliation, marketing campaign analysis, software QA testing). We will build and deploy AI agents that can execute these multi-step tasks autonomously, using tools just like a human employee would.
Pillar 3: “Accelerated Evolution” — People and Governance
Technology is nothing without the people to wield it responsibly and effectively. We must build a culture of AI literacy and an unbreakable foundation of trust.
- Vision: To create a culture where every employee is empowered by AI and our customers trust us implicitly to use this technology ethically.
- Key Initiatives:
- Establish the AI Academy: Launch a mandatory, company-wide upskilling program with tiers for different roles (Executives, Product Managers, Engineers, Marketers). This is not optional.
- Why: To truly be AI-native, our entire organization must speak the language of AI. Our product managers need to ideate with it, our marketers need to campaign with it, and our executives need to strategize with it.
- KPI: 100% employee completion of role-specific AI training by end of 2025. Generate 10 new product features from our internal, non-technical “AI Idea” portal.
- Deploy a Proactive Governance Council: Establish a cross-functional AI Governance Council (Legal, Ethics, Security, Engineering) that doesn’t just react, but sets the rules. They will implement automated systems for detecting model bias, ensuring data privacy in RAG, and red-teaming our AI agents before deployment.
- Why: Trust is our most valuable asset. A single high-profile AI failure can destroy years of brand equity. Regulation is coming, and we will be ready for it before it arrives.
- KPI: Zero Level 1 (critical) AI-related security or privacy incidents. Achieve 100% compliance with emerging AI regulations ahead of deadlines.
- Establish the AI Academy: Launch a mandatory, company-wide upskilling program with tiers for different roles (Executives, Product Managers, Engineers, Marketers). This is not optional.
Summary of the Next 24 Months
- First 6 Months: Build the foundational Model Agility Framework. Secure compute. Establish the Governance Council and AI Academy. Begin RAG indexing.
- 6-12 Months: Launch first AI agents for internal workflows. Scale the “Corporate Brain.” First wave of employees graduates from the Academy.
- 12-18 Months: Deploy hyper-personalization across a flagship product. Launch the Multimodal Experience Lab. See significant cost savings from model optimization and automation.
- 18-24 Months: AI agents are a core part of our operational workforce. Multimodal features are in customer hands. Our AI-native culture is the primary driver of innovation and our key competitive advantage.
This is an aggressive plan, but the moment demands it. By executing on these three pillars, we won’t just predict the future of AI; we will be the ones building it.