AI is clearly here to stay. But while the excitement is real, so is the confusion. Many companies have experimented with AI by running proof-of-concept (POC) projects, only to see them stall before reaching full-scale implementation. In fact, industry data suggests that only a small percentage, anywhere from low single digits to 15%, of AI POCs actually make it into production.
Why? Because too often, AI adoption lacks a clear business case, a well-defined economic model, and an understanding of how these technologies truly fit within an enterprise environment. The big LLM vendors have dominated the conversation, and while their capabilities are impressive, they don’t always align with real business needs or budget realities. And let’s not forget, LLMs aren’t always the right tool for the job; for many use cases, they’re overkill.
So how do you turn AI from an intriguing experiment into a real, value-driving asset? It starts with the right approach. Today, we’re diving into Phase 0: Experiment and Plan and Phase 1: Pilot and Validate – the critical first steps to laying a solid AI foundation. With the right strategy and a deep understanding of both technology and business needs, AI success is possible. Let’s get started!
Before implementing new technology, you need to make sure your business is prepared for it. Phase 0 is where take a step back and evaluate AI’s potential within your organization. This is your opportunity to assess feasibility, identify challenges, and strategize AI adoption with purpose rather than diving in blindly.
At Concord, our data scientists focus on three fundamental steps during this phase:
It’s important to determine whether your business is truly ready for AI. You need to understand what’s required to support the technology, which includes evaluating your current infrastructure, data quality, and regulatory and compliance needs. The goal is to create a strategy that aligns AI with business objectives while identifying potential roadblocks early.
Concord’s AI Readiness Assessment helps you build a strong AI strategy by:
Once you have a strategy, it’s time to put AI to the test. Not all AI initiatives are created equal, so experimentation helps you determine what works best for your business. By testing small-scale implementations, you can validate AI’s potential before making significant investments.
At Concord, we use fixed-time rapid iterations—6-8-week sprints—to figure out what’s effective:
After testing and assessment, it’s time to map out the bigger picture. Not all AI initiatives should be tackled at once. You should focus on the most impactful and low-risk AI projects first. By defining the scope and estimating required investments (in terms of time, resources, and training), you can make sure your AI adoption is both scalable and manageable.
Concord’s second set of 6-week sprints focuses on:
Every new initiative comes with its share of challenges, some expected and some not. Recognizing these obstacles early on can help you navigate the AI adoption process more smoothly. During Phase 0, you’re evaluating AI’s potential and defining a strategy. Here are some of the most common challenges:
Now that you’ve completed Phase 0, it’s time to test AI initiatives in a controlled environment. This phase focuses on validating AI models before scaling them. Here’s how it works:
Develop an MVP, which is a stripped-down version of the AI solution that focuses on core features, to quickly test its viability. This helps you assess the AI’s performance and its potential to meet your objects while avoiding heavy investments in unproven concepts.
Once the MVP is developed, the next step is launching it as a pilot project. This involves deploying AI in a live, production-like environment but on a smaller scale. Through pilot iterations, you’ll collect insights and can tweak the AI models for necessary adjustments.
AI models need continuous refinement. Once the pilot is launched, use feedback and data from initial deployments to iterate on the solution. You’ll adjust based on performance and over time, you can scale the solution to encompass more complex use cases and larger operations.
The timeframe for this phase depends on the complexity of the pilot use case. However, a 4–6-month period is generally recommended for developing and launching an MVP, including 1-2 iterations of the pilot in a production setting. This gives you enough time to assess performance, refine models, and make sure the AI can deliver true value.
Once AI initiatives move from strategy to implementation, new challenges emerge:
These challenges may seem daunting, but they’re not dealbreakers. With the right partner and a structured yet flexible approach, you can navigate disruptions and set a solid AI foundation.
At Concord, we help you navigate the early stages of AI adoption, from validating use cases to seamlessly integrating AI into your core operations. We start with a focused pilot and gradually expand AI’s impact across critical areas like customer experience, inventory management, and pricing strategies.
By leveraging automation and AI-driven insights, we enhance decision-making, efficiency, and business performance. Our team ensures you get real value from AI by analyzing pilot results, assessing risk and reward potential, and addressing feasibility, compliance, and long-term scalability.
If you’re looking for a partner with a proven track record of driving AI success, Concord is here to help. Let’s connect for a customized AI Journey Workshop, where we’ll focus on business value creation, implementation planning, and unlocking AI’s full potential. Together, we’ll bridge the gap between AI’s hype and its real-world impact.
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