AI Adoption Framework: A 6-Phase Playbook for Getting AI Into Production

Prototype construction is relatively simple. It's more difficult to scale it throughout a business. If a pilot is successful in a controlled test, but does not succeed when deployed against real company data, real systems and security requirements, actual operating costs, and users who must trust the pilot enough to change the way they operate, then it is a failure.

So, the strategy-only approach does not work. Identifying where the technology can have value does not necessarily tell engineering and transformation leaders which use cases to invest in, when a pilot is ready to go to production, what is needed to change, or when an experiment should be ended.

Every stage of a workable adoption framework requires decision-making. The business case should be in line with engineering, people, governance, data readiness, and quantifiable outcomes.

This guide outlines 6 phases of the process, from selecting the appropriate use cases, through running and improving at scale. Concrete activities, owners, deliverables, and exit criteria are provided for each phase. There is also a maturity model with five levels that can help you gauge your organization's maturity level and what needs to be done next to make it better.

What Is an AI Adoption Framework?

An AI adoption framework is a series of steps that can be followed to successfully implement a successful AI use case. It assists teams in determining what to construct, whether the business and technology are ready, carrying out pilot tests, and expanding successful initiatives.

It also establishes definite decision-making points. Just because a pilot works in a demo doesn't mean it should proceed. Before getting additional financing, it must achieve predetermined goals for business value, output quality, cost, security, user acceptance, and operational readiness.

AI Adoption Framework vs. AI Strategy vs. AI Governance

The three areas have different functions:

All three are in practice working together. The direction is set by strategy, the way to execute is via the adoption framework and the boundaries are set by governance. Even with a solid strategy and policies in place, companies can have projects ready to go and not be getting them into production because of the execution gap.

Why Businesses Need an AI Adoption Framework

The actual problem is not to establish whether a technology is viable. It's about determining the value that a use case is going to bring to the product, whether the organization would want to contribute to it, and what to do when the system fails.

These choices are made too late in the absence of a framework. A team develops a great pilot and finds out that production cannot rely on production data, integrations are not straightforward, the design is not secure, it is too expensive to run, or the employees don't trust the results. AWS also suggests making risk management a part of the adoption process, not simply an activity performed after it.

There are four major ways that a practical framework modifies the process:

It forces pilots to work up the steps. Goals must be established prior to development. For instance: cutting processing time by 30%, keeping the per-transaction cost under an agreed limit, maintaining a quality cut-off, and ensuring the target number of users is achieved. If they are not fulfilled, the team either enhances or terminates the project. This leaves pilot numbers as measures of progress out of the way.

It is an assessment of production readiness, not model performance. You can have a good system during testing and suck during real use - with real user data. Teams should also try out failover actions, security, data quality, integrations, monitoring, and whether failures can be tracked. The evaluation should use the production setup rather than a clean demo setup whenever the system retrieves data or performs any other action.

It relates adoption to technical success. If employees ignore the suggestions or confirm all the responses, the system is technically accurate, but of minimal value. In addition to accuracy and latency, user trust, workflow change, training, and override rates should be monitored. Google's PAIR is an example of human-centered approaches, as are other approaches that focus on user needs, explainability, control, feedback, and graceful failure.

It offers a reliable route to production. The same standards can be agreed upon for use-case selection, data readiness, evaluation, security, deployment, monitoring, and ownership, as opposed to every pilot being treated differently. These practices can be repeated in the future as reusable capabilities to get good ideas to production consistently.

A good adoption framework is thus a decision-making system: what to build, what to stop, what is now available, what needs to be adapted prior to scaling it.

6 Pillars of an AI Adoption Framework

There are six areas that must work together before moving through the phases of adoption. A fault with one can cause an otherwise good use case to fail; for example, a user may not buy the new process, even if the technology is strong, the data is correct, and the relevant people control the other problem.

These pillars are not 6 workstreams run in isolation and then once. They are the areas teams should look at during the adoption process. The following six phases transform them into a viable series of decisions, from use case selection to scaling up.

6-Phase AI Adoption Framework: From Business Case to Production Scale

The six phases have decision gates between the idea and the scaled production system. A project progresses because it has the required criteria and not just because the last stage has been finished.

Phase 1: Align: Build the Business Case Before You Build AI

Begin with business problems and don't start with the model. Outline areas for improvement and evaluate use cases by impact and feasibility.

Objective: Find opportunities worth investing in.

Owner: Business or transformation leader.

Key activities: Identify the business problem, establish baseline performance, estimate expected value, rate impact level vs feasibility, define dependencies, and designate a business owner.

Artifacts created: Use case portfolio, ranked by order of importance, with expected outcomes, name of the owner for each.

Exit criteria: There is a measurable outcome, baseline, owner and value, and sufficient feasibility to continue investing in the use case.

Typical duration: 1-3 weeks.

How this phase fails: Instead of considering the importance of the business challenge, projects are chosen based on the potential of the technology. Having dozens of pilots without any direct link to revenue, cost, risk or productivity is also a warning sign.

Phase 2: Assess: Find the Gaps Before AI Hits Production

If a use case appears to be valuable, see if the organisation can support the use case.

Objective: Identify technical and organizational challenges that might hinder production deployment.

Owner: data/platform engineers, security, and business.

Key activities: Review the data quality, data availability, data lineage, data permissions, infrastructure, legacy integrations, security requirements, and team skills. Therefore, deeper data engineering work might be required prior to development here.

Artifacts created: A gaps, risks, dependencies, and necessary improvements readiness scorecard.

Exit criteria are: Critical gaps are either solved, or there is an approved plan, owner, cost, and timeline.

Typical duration: 2-4 weeks, depending on data and on system complexity.

The way this phase goes wrong: Teams try to test using clean, clean data stuff, and then, it turns out that the data in the production environment is missing, stale, inaccessible, or poorly managed.

Phase 3: Prove: Run AI Pilots and Validate Value Before You Scale

A pilot is not a proof of technology; it's a proof of concept for a business question that needs an answer. Prior to development, record what success and failure would look like.

For example:

Scale if processing time is reduced by 30% or more, quality is kept at or above the agreed level, cost is kept below $0.20 per completed task, and users are willing to accept the outputs without too much manual adjusting.

Objective: Demonstrate Business Value & Technical Feasibility using realistic conditions.

Owner: Product/business owner with engineering.

Key activities: Develop limited pilot, test inputs at production level, check for quality & cost, review user feedback, monitor overrides/corrections, test critical failure cases.

Artifacts created: Pilot specification and results based on success criteria, cost ceiling, user feedback, risks, and recommendations.

Success criteria: Success defined prior to testing by the pilot. Improve or stop if they're missed.

Typical duration: 4-8 weeks

How this phase fails: There is no kill condition. A team continues to develop an idea that is not “good enough” or “enough” without clarifying exactly how good/useful it should be. A pilot is a research project if it lacks a kill condition.

Phase 4: Productionize: Turn AI Pilots Into Production-Grade Systems

This is where lots of good demos fail. Real users, changing data, failures, security requirements, unpredictable inputs, latency limits, and operating costs are introduced into production.

Objective: Ensure that the proven use case is reliable, secure, measurable, and supported in production.

Owner: Engineering/CTO, platform, security, product, and business owners.

Key activities: Design production architecture, create automated evaluations, configure access controls, include observability, validate adversarial and real-world inputs, set cost limits, and establish fallback behavior and human approvals.

The evaluation harness should be there in advance of launch. In a RAG application, this might be how well the context is followed and completed. For an agent, it might encompass tool selection, tool mistakes, and if actions drive the task towards its resulting target.

After a problem is identified, guardrails should help clarify the responses:

Failure alone recorded by a guard rail is not a failure. High-risk controls should be in place prior to the output or tool call generating another system.

Artifacts created: Production architecture, Evaluation suite, Monitoring configuration, Fallback procedures, Production runbook.

Exit criteria: The system meets quality, security, reliability, cost, and operating test criteria with production-type inputs. Failure can be identified and attributed, and there's some ownership for incidents occurring during production.

Standard time frame: 4-12+ weeks based on integration and risk.

How this phase goes wrong: Teams make the pilot available for use with an API wrapper and refer to it as production. Evaluation, monitoring, behavior strategies to go to, security, and operational ownership are only added in response to problems.

Phase 5: Scale: Build the Platform, Not Another One-Off AI Project

Once it's built, it starts to cost more and more to continually replicate the same structure.

Objective: Transform well-established practices into reusable capabilities.

Owner: Platform/engineering leadership and transformation/business teams.

Key activities: Develop common services to access the models, retrieve information, perform evaluations, access identity, monitoring, security, and tracking costs.

Change management is also a key aspect of scaling. Monitor employees' use of new workflows, areas where outputs are overridden, and areas where manual processes are still in place. A system that the employees have to work around is technically successful but not scaled.

Artifacts created: Common platform features, reusable development patterns, enablement resources, and adoption dashboards.

Exit criteria: Common infrastructure and controls can be reused if there is a new project, and business outcomes and positive adoption trends can be measured if the workflow is successful.

Typical duration: Ongoing; starting platform will take 2-6 months to standardise.

What is wrong with this phase: Each business unit develops their own stack, evaluations, integration, and governance process. The number of pilots goes up, yet the effort and expense of introducing each project stays needed and significant.

Phase 6: Govern & Optimize: Keep AI Reliable, Compliant, and Cost-Effective

A deployment will not lock the system. Models change, prompts change, retrieval data changes, costs move, regulations evolve, and failure cases due to user behavior are introduced.

Objective: Maintain production systems which are useful, safe and sustainable in an economic way.

Owner: Product and engineering owners and risk, security, compliance and operations.

Key activities: Track quality and drift, re-run evaluations after significant changes, evaluate access and compliance, investigate incidents, gather user feedback, optimize model and infrastructure cost.

Evaluation should be integrated into the CI/CD. Some tests are supposed to be triggered prior to release based on a prompt update, model change, retrieval change, or important data update.

Artifacts created: Evaluation Reports, Incident Reports, Optimization Backlog, Compliance Evidence, Production Runbooks.

Exits: No final exit. The systems are kept in this stage till they are replaced or retired.

Typical duration: Continuous.

What happens when teams aren't successful with this phase: Users don't measure output quality, user behavior, drift, and business value. The system is kept online, but its usefulness or reliability gradually decreases.

AI Adoption Maturity Model: The 5 Levels

Not all organizations will achieve the highest maturity level right away. The valuable question is where you are right now and what capability do you need to acquire next that will enable you to produce reliably.

Use this matrix to evaluate maturity for the 6 pillars of the framework.

Level 1: Ad Hoc

Projects are primarily dependent on a single team or team member and have minimal collaborative process.

Ask yourself:

Are projects being initiated without a clear business owner and measurable outcome?

Is each team's tool, data, and controls different?

Would the success of experiments be hard to replicate in other locations?

Next priority: Work out the owner and develop a set of simple and measurable use cases.

Level 2: Experimenting

Structured pilots are currently being conducted, with each project having a mostly independent operation.

Ask yourself:

Are there success criteria and stop criteria for pilots before they start?

Is it possible that data, security, cost, and usability are being tested in addition to the model quality?

Do good pilots have a clear path to production?

Next priority: Develop common readiness checks and production exit criteria.

Level 3: Repeatable

Teams have come to understand what works and are starting to apply these to other situations.

Ask yourself:

Are there any patterns that can be reused for new projects?

Are all the use cases prioritized by value and feasibility?

Is it possible to take a successful pilot and ramp it up into production without refitting the process?

Next priority: Normalise common engineering, governance and delivery capabilities.

Level 4: Operationalized

Production systems are managed by using processes, not by making individual decisions.

Ask yourself:

What monitoring is done following the launch of the service for quality, cost, adoption, drift and incidents?

Are there clear technical/ business owners of production systems?

Are the evaluations and controls automatically performed when a model or prompt change or a data change is made?

Next priority: Make portfolio-wide measurement, automation, and efficiency improve.

Level 5: Transformative

The technology is no longer a collection of unrelated initiatives. It forms a part of the operation and investment decisions of the organization.

Ask yourself:

Are basic functions or products being rethought instead of being automated?

Are teams able to start new use cases on existing platforms, controls and operating models?

Is the investment made on measurable business results versus pilot activity or tool usage?

Next priority: Improve the value, cost, reliability and governance of the business as its capabilities evolve continuously.

It is not the objective to achieve level 5 in all areas. This is a more effective assessment of why production adoption is not occurring and seeks the weakest link in the chain. Having a well-developed engineering capability, for instance, may still be immature for an organization, but the issue of ownership, user adoption, or governance is still addressed on a project-by-project basis.

Governance and Risk Inside the Adoption Framework

Governance shouldn't be a last step after a system is developed. Risk requirements should be translated to delivery requirements during the design, testing, deployment and production process. This makes sure that teams do not find out at the end of the pilot that it fails security, privacy, and compliance checks.

Map NIST AI RMF to Delivery

The NIST AI Risk Management Framework (AI RMF) has four functions: Govern, Map, Measure, and Manage. According to NIST, governance is a cross-cutting activity, and managing risk needs to be done continuously during the system lifecycle.

These functions can be directly turned into delivery work by teams:

It's the go/no-go decision that's important. NIST's Manage function is particularly focused on measuring if a system realizes its expected goals and whether to move forward with development or deployment. This aligns well with the exit criteria of each stage of this adoption framework.

EU AI Act & ISO 42001 in the SDLC

The EU AI Act is based on a risk-based approach. If the systems are subjected to its high-risk requirements, then control is in the form of risk management, data quality, activity logging, documentation, human oversight, robustness, cybersecurity, and accuracy.

These requirements should be aligned with development work, not delegated to a compliance team when it is launched. For instance, data governance should be in the data phase, human oversight should be in workflow design and logging, and cybersecurity should be in production architecture.

ISO/IEC 42001 takes a different angle of the issue, at the organizational level. It outlines the criteria for setting up and continually improving an AI management system with a Plan-Do-Check-Act approach.

They both convey the same practical concept: Record choices, delegate tasks, evaluate risks prior to deployment, and track after launch.

AI Guardrails That Belong in Code

Systems can be directed by policies. The applications control their abilities. As such, a guardrail should be implemented via code/infrastructure for more critical workflows. Examples include:

• Preventing access to data beyond users' permission.
• Providing agents with scopes rather than user credentials.
• Blocking tools from using their unauthorized use.
• Requiring human approval by humans for transactions above defined thresholds or risks.
• Validating arguments to tools prior to execution.
• Obscuring sensitive details prior to they end up in a model.
• Resisting or declining to alter when there's an outcome that has been identified as below an acceptable standard.

A helpful guardrail has three components: measure value - threshold value - system response when the threshold value is reached. For instance, if the retrieved information is of such poor quality that the app cannot produce an appropriate reply, the transaction could be canceled and not executed; if there is a high-value transaction made by an agent, then it may be canceled until it is approved by a person.

How to Measure AI Adoption and ROI

The use of AI should not be judged by the number of pilots or licenses issued, or by the number of users who can access it. The use of AI should not be judged based on the number of pilots or licenses issued, or the number of users who can access it.

Record a baseline before the pilot. If the objective is to lower customer support time, record the existing resolution time, cost per case and error rate. If there is no baseline, it is impossible to say what system improvements were shown.

Monitor four aspects: business impact, user uptake, technical performance and cost. Business metrics could be income generated, number of hours saved, reduced errors, or shortened cycles. Adoption metrics include usage (repetitive), task completion, override rate, etc. Technical measures can include quality scores, latency, failure rates and errors in the tools.

Take the total amount into account. Development, infrastructure, model usage, data prep, integrations, monitoring, human reviews, maintenance and training.

ROI = (Business value created – Total cost) ÷ Total cost × 100

Analyse these metrics as a group. High usage is not a successful adoption until the business is improved. If users make frequent corrections or ignore the output, this isn't of much benefit if the accuracy is high.

Post-deployment measurement continues. When a system can no longer achieve its business, quality, adoption or cost goals, work to improve it, or determine if it should continue to be produced.

Reasons AI Adoption Frameworks Fail

Teams can have a good framework, but fail to make decisions clearly. The majority of problems are caused by selecting the wrong use cases, poor testing, ambiguous ownership and deployment into production too early.

Beginning with the technology. It's possible that teams select a model or a tool to solve a problem. Rather, start with a measurable outcome and a business challenge.

Conducting pilots with vague indicators for success. Establish objectives for a pilot before it starts. Establish business value, quality, cost and user adoption objectives. Improve it or cease it if it can't meet them.

Only take tests with clean data. It is possible that a pilot might perform well with prepared data, but not with actual data from a company. A system shouldn't be considered ready until it functions with practical inputs.

Holding onto the governorship to the end. Security, privacy, access and compliance considerations can impact the way a system is constructed. Handle them during the design stage rather than just before launch.

Trying a working demo as if it were ready to go into production. A demo is a trial that shows an idea is viable. There needs to be monitoring, evaluations, backup plans, cost control, security and accountability if something goes wrong in production.

Not considering the users of it. Technical success is not enough without trust and use by the employee. Monitor use, disregard, correction and override of its output.

From scratch to building each project. Scaling becomes slow and expensive if each team develops their own integrations, evaluations, security controls and monitoring. Utilize existing good components and processes to re-use them in other projects.

Judging by activity not by results. Business value is in no way implied by the number of pilots, pilots' licenses or pilots' queries. Monitor outcomes such as increased customer satisfaction, revenue creation, time saved, cost savings, and error prevention.

Adoption frameworks should be clear about what should be eliminated, what doesn't work, and what works.

Need Help Moving AI From Experimentation to Scale? Let Coding Craft Help

At Coding Crafts, we help companies do that from the beginning. In collaboration with teams, we look for practical use cases, do technical readiness assessments, develop solutions and test and validate those solutions, and make the successful pilots into systems that function reliable at scale.

The focus is not on trying to run more experiments. It is on building solutions, which address a measurable business problem, are based on real data, fit into existing workflows, and will be reliable when deployed.

Looking for more than pilots?

For ideas that are going from validation to secure and scalable production systems, Coding Crafts can help you get it done.