Why the AI Adoption Model Is Needed Now
Generative AI (LLMs) has brought AI adoption in business within easy reach. However, on the ground there are many failures: “we did a PoC but it never gets used,” “data isn't available so we hit a wall,” “security review blocked us,” and so on.
AI adoption is not merely about selecting and deploying a model. From business problems → data → governance/organization → evaluation → operation must be designed as a seamless flow to yield value. Here we outline the steps enterprises should follow in order, with notes on what to watch out for at each stage.
Overview of AI Adoption: Start with 7 Steps
- Define Objectives and KPIs (What to improve? How much to gain?)
- Audit Business Workflows (Where can AI be inserted?)
- Check Data and Constraints (What data can be used, what cannot)
- Choose Approach (generative AI / prediction / classification / RPA integration, etc.)
- Build Small, Test (PoC → Pilot)
- Production Design (Operations, monitoring, permissions, governance)
- Adoption and Improvement (Education, rules, continuous tuning)
Step 1: Articulate Objectives and KPIs (This Is 90% of It)
AI adoption tends to become an aerial exercise when the goal becomes simply “to use AI.” First, quantify the problems that the business and frontline are facing.
Examples of KPIs (Concretely)
- Customer support: reduce the average time to first response by 30%
- Sales proposals: reduce proposal creation effort by 100 hours per month
- Fraud detection: reduce false positives by 20%
- Demand forecasting: improve stock-out rate by 1.5 percentage points
For generative AI, measuring quality is particularly important. For example, for FAQ answers, you can use accuracy (human-evaluated), first-contact resolution rate, and escalation rate.
Note: Stating “productivity should improve” may get the approval, but operations can stall. Keep KPIs unambiguous so that everyone sees the same thing; it prevents disputes later.
Step 2: Audit Workflows and Decide AI’s Place
Next, break down the target tasks into steps, even roughly. AI isn't magic, so it helps in parts where inputs and outputs can be defined.
Tasks Generative AI Fits Well For (Examples)
- Text generation: emails, proposals, meeting minutes, internal notices
- Search and summarization: extract key points from internal rules, knowledge, contracts
- Classification and routing: categorize inquiries, assign priorities
- Dialogue assistance: operator support, internal help desk
The key is not to aim for full automation from the start. A human-in-the-loop design, such as “AI drafts; final decision by humans,” raises the odds of success.
Step 3: Identify Data and Constraints (Decide What Can and Cannot Be Used)
AI is governed by data. In enterprises especially, personal data, confidential information, copyright, and contractual constraints come into play.
