Business strategies for a successful gen AI solution · Topic 1 of 4
Choosing and Implementing a Gen AI Solution
Types of gen AI solutions, the business and technical factors behind them, how to choose the right one, and the steps to bring gen AI into an organization.
- 4 min
- Solution types: text, image, code generation and personalized user needs.
- Factors: business requirements (goals, users, risk, budget) and technical constraints (data, integration, latency, security, skills).
- Choose to buy, adapt or build based on value, speed, cost, risk and control.
What it is
Successful gen AI starts with a business problem, not a model. The exam expects you to recognize the types of gen AI solutions, the factors that influence gen AI needs (business requirements and technical constraints), how to choose the right solution for a need, and the steps to integrate gen AI into an organization.
How it works
Types of gen AI solutions
| Type | What it produces | Example |
|---|---|---|
| Text generation | Written content, summaries, answers | Drafting proposals, summarizing calls |
| Image generation | Images from descriptions | Product visuals, ad variations |
| Code generation | Code, tests, explanations of code | Developer assistants |
| Personalized user needs | Content and experiences tailored to each person | Personalized recommendations, tailored learning paths |
Many real solutions combine types, and agents add the ability to take actions.
Factors that influence gen AI needs
| Business requirements | Technical constraints |
|---|---|
| The goal and value: revenue, cost, speed, quality, customer experience | Data availability, quality and format |
| Users and their workflow | Integration with existing systems |
| Risk tolerance and regulation | Latency and scale requirements |
| Budget and timeline | Security, privacy and data residency |
| Required accuracy | Skills available in the team |
When a scenario says the company has "no ML team and needs results quickly", the constraint points to a prebuilt offering. When it says "unique process and proprietary data", it points to customizing or building.
Choosing the right solution
| Option | When it fits | Google Cloud examples |
|---|---|---|
| Use a prebuilt solution | Common needs, fast value, limited skills | Gemini for Google Workspace, Gemini Enterprise, Customer Engagement Suite |
| Adapt a model or platform service | Need company data or style, moderate skills | Agent Search, grounding, prompt design and tuning on Agent Platform |
| Build a custom solution | Unique, differentiating process with proprietary data | Custom agents and models on Gemini Enterprise Agent Platform |
The best choice balances value, speed, cost, risk and control. Many organizations start with prebuilt solutions for productivity and build custom solutions where they differentiate.
Steps to integrate gen AI into an organization
- Set a clear vision and executive sponsorship, tied to business strategy.
- Identify and prioritize use cases by business value and feasibility (data, risk, effort).
- Prepare data and platform: make data accessible and governed; choose a secure platform.
- Start with a pilot: a focused use case with success metrics agreed in advance.
- Put guardrails in place: security, responsible AI, human review where needed.
- Build skills and manage change: train people, redesign workflows, communicate why.
- Measure and iterate, then scale what works across the organization.
People and process matter as much as technology. A tool nobody adopts creates no value.
A worked scenario
A manufacturer wants faster responses to supplier questions. Business requirement: answer in minutes instead of days, without errors on contract terms. Technical constraints: contracts are scanned PDFs, and the team has no ML engineers. The company starts with a pilot using Agent Search over its contracts (adapt, not build), sets a KPI for response time, keeps a human review for contract commitments, trains the procurement team, and plans to scale to other departments after three months.
How the exam asks about this
- "What should the organization do first?" Identify a business problem and prioritize use cases by value and feasibility.
- "No ML skills and an urgent common need." Use a prebuilt solution.
- "Which factor is a technical constraint?" Data quality, integration, latency, security, skills.
- "The pilot succeeded; what next?" Measure, refine and scale, with change management.
What to remember for the exam
- Solution types: text, image, code generation and personalized user needs.
- Factors: business requirements (goals, users, risk, budget) and technical constraints (data, integration, latency, security, skills).
- Choose to buy, adapt or build based on value, speed, cost, risk and control.
- Integration: vision and sponsorship, prioritized use cases, data and platform, pilot, guardrails, skills and change, measure and scale.
Starting with "which model should we use?" is the wrong first question. Start with the business problem and how success will be measured.
Common mistakes
Building custom solutions for common tasks. Prebuilt offerings are faster and cheaper.
Skipping the pilot. A small pilot proves value and reveals risks before large investment.
Ignoring change management. Adoption depends on training and workflow redesign.
Check yourself
What should come first in a gen AI initiative?
A clear business problem and use cases prioritized by value and feasibility.
Name two technical constraints that influence gen AI needs.
Any two of data availability and quality, integration, latency and scale, security and privacy, and team skills.
When does building a custom solution make sense?
When the process is unique and differentiating and relies on proprietary data, and the team has the skills.
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