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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTo turn GenAI experiments into business value, start with a specific business problem, supply the data and context needed to address it, and measure whether the resulting workflow improves a meaningful outcome. Bill Schmarzo’s TLADS framework—“Thinking Like a Data Scientist”—combines data science, design thinking and economic principles to keep AI work tied to real value. It is a way to guide innovation, not a guarantee of return.
What does a value-creation framework change?
Without a value framework, teams can spend time testing prompts or models without knowing which business decision or process should improve. TLADS shifts the starting point from “What can this tool do?” to “What valuable problem should we solve, and what evidence would show that we solved it?”
That means treating GenAI as part of a business workflow, rather than as an isolated chatbot. A useful opportunity has a defined user, an outcome the organization cares about, relevant information the model can use, and a way to assess risk and results. Schmarzo describes TLADS as blending data science, design thinking and economic principles to align AI efforts with real business value. Read the TLADS overview.
Use a five-step workflow to move from prompting to insight
Contextual continuity—the practice of carrying relevant context through a sequence of interactions—can make prompts more useful and work more repeatable. This five-step workflow helps turn a broad question into an answer grounded in the right problem and knowledge. The contextual-continuity method illustrates it with a farming decision about crop selection, profitability and climate variability; that example is illustrative, not proof of general performance.
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- Define the problem and boundaries. State the decision or process to improve, the objective, constraints, and the perspective the answer should take. For example, distinguish a request to compare options from a request to make a recommendation, and specify which costs, time period or operating conditions matter.
- Provide relevant organizational knowledge. Gather approved documents, policies, process descriptions, historical information or other “tribal” knowledge the model needs. Check that the material is accurate, current and appropriate to share with the chosen service; do not assume a model can access internal knowledge simply because it is available somewhere in the organization.
- Build a sequence, not a one-off question. Establish a narrative: ask the model to clarify the situation, identify factors that affect the decision, compare alternatives, and then explain the implications. Each step should add or refine context rather than rely on an unstated assumption from an earlier answer.
- Request a useful perspective. Use a persona or role to direct the kind of analysis—for example, ask for a finance, operations or risk perspective. A persona can shape the response, but it does not make the model a qualified professional or verify its claims.
- Review, refine and summarize. Check key statements against trusted sources and people with relevant expertise. Correct missing or mistaken context, ask follow-up questions where the answer is uncertain, and produce a concise summary of the decision, assumptions, open questions and next action.
Connect the workflow to business value
A prompt is not a business case. Before expanding a successful experiment, define what value means for the use case and how the organization will recognize it. That could involve a financial outcome, a process measure, a service outcome or a reduction in a known operational burden; the appropriate measure depends on the work being changed.
- Name the beneficiary and decision. Identify who will use the output and what they will do differently because of it.
- Establish a credible comparison. Decide what current practice or result the experiment will be compared with, and avoid attributing an improvement to AI when other changes could explain it.
- Include the cost and risk of operating the workflow. Consider data preparation, model use, human review, integration, governance and ongoing maintenance—not just the apparent speed of generating an answer.
- Set a scale-up threshold. Specify what evidence, reliability and controls must be in place before a pilot becomes part of regular operations.
The canonical sources do not establish an independent market-size or ROI figure validating TLADS. Its value is as a practical discipline for choosing and evaluating work, rather than a numeric promise that every GenAI project will pay off.
Choose an implementation pattern that fits the work
Organizations commonly encounter three ways to consume GenAI: AI embedded in existing software, a third-party model or service used directly, or an AI platform used to build and govern tailored solutions. The right choice depends on the use case and the organization’s requirements; speed alone is not enough to judge it.
| Approach | Where it can fit | Key trade-off to assess |
|---|---|---|
| AI embedded in software | Adding AI features within a product or service the organization already uses. | Assess how much control the organization has over data handling, governance, customization and the resulting workflow; these details depend on the specific product. |
| Third-party model or service | Experimenting with a provider’s model or using its service for a defined task. | Assess data terms, auditability, model transparency, customization options, operating costs and whether the workflow creates meaningful differentiation. |
| AI platform | Building solutions that combine organizational data, governance and multiple models. | Can support more tailored solutions and differentiated workflows, but requires the organization to take responsibility for platform choices, data, governance and implementation. |
Compare options on seven practical dimensions: control over proprietary data, governance and auditability, speed to experiment, ability to customize or tune models, differentiation of workflows, operating cost and inference efficiency, and readiness to move from assistant use to automation or agents. Not every task needs the most customizable setup; the goal is to match the level of control and investment to the value and risk of the work.
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Build data and governance into the value equation
The handbook AI Value Creators: Generative AI Handbook for Business expresses the success equation as “AI SUCCESS = MODELS + DATA + GOVERNANCE + USE CASES.” The components reinforce one another: a capable model cannot compensate for irrelevant or unreliable data, and good data does not make an ungoverned use case safe. The authors argue that proprietary data can distinguish business AI because commonplace large language models contain only about 1% of enterprise data, at most—an assertion by the handbook’s authors, not an independently established measurement. See the handbook’s framework.
For each use case, establish who can access the information, which sources are approved, how sensitive material is handled, and who is accountable for outputs and decisions. The handbook flags hallucinations, poor-quality data, rights-managed content, inadvertent leakage and unclear accountability as issues organizations must address. With opaque third-party models, organizations may have less control over how business data is stored or used, so investigate the provider’s terms and safeguards before submitting sensitive information.
Model choice can also affect operating economics. The handbook’s preface reports that fit-for-purpose models produced up to thirty-fold reductions in inference costs in the authors’ IBM work in 2025. This is the authors’ reported experience, not a general benchmark or a cost reduction every organization should expect.
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The handbook describes an AI Value Creation Curve that progresses from experimentation through modernization and automation toward AI+ and agentic operations. Read this as a direction for capability-building, not an automatic maturity path: organizations should advance only when the use case, data and controls can support the next level.
Best Value
- Experimentation: Explore a bounded question with approved information, human review and a clear learning objective.
- Modernization: Redesign an existing workflow so AI contributes consistently, with defined inputs, review points and ownership.
- Automation: Automate suitable steps only when outputs and exceptions can be managed reliably and the organization has appropriate controls.
- AI+ and agentic operations: Consider broader, more autonomous workflows only when data access, permissions, monitoring and accountability are designed for the consequences of action.
At every stage, retain a human decision-maker where judgment, financial consequences, legal obligations or customer impact make review necessary. A useful workflow records assumptions and exceptions, has a route for correction, and makes clear who is responsible when the model is wrong.
Further reading
For a deeper treatment of business use cases, data, governance and AI value creation, see AI Value Creators: Generative AI Handbook for Business by Rob Thomas, Paul Zikopoulos and Kate Soule, published by O’Reilly Media in April 2025. Find the handbook.
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