An AI Specialist is responsible for ensuring that a company uses artificial intelligence for the right business needs, with controlled costs, understood risks, and capabilities that each department can apply in practice. The role is becoming more relevant because AI is developing faster than many organizations can select, test, and manage it.
Designers, marketers, operations staff, analysts, and sales teams all need to understand the AI tools related to their work. It is not realistic, however, to expect every employee to monitor dozens of models, compare subscription plans, manage credits, review data practices, write company standards, and train other departments. When all of these responsibilities are added to an existing job, the result is often not transformation. Core work gets delayed, while AI adoption remains fragmented.
Roku Studio sees the need for a new corporate function: the AI Specialist as the bridge between technology, people, and business goals.
Why do companies need an AI Specialist now?
Companies need clear ownership of AI because adoption is expanding while decisions about tools, costs, skills, and risk are often scattered across the organization.
The Stanford AI Index 2025 reported that 78 percent of organizations used AI in 2024, up from 55 percent in 2023. The use of generative AI in at least one business function also rose from 33 percent to 71 percent. These figures show that AI is no longer a small experiment owned only by technology teams.
The World Economic Forum placed AI and big data among the fastest growing skill areas through 2030 in its Future of Jobs Report 2025. The same report listed AI and machine learning specialists among the fastest growing jobs.
Widespread adoption does not automatically create value. A company may hold many AI accounts without knowing which ones are actively used. Different departments may pay for tools with overlapping functions. Employees may enter sensitive information into services that have not been reviewed. An AI Specialist is needed here, not as a trend hunter, but as the person who manages the company’s AI operating system.
Why should this not simply be added to a designer or marketer’s job?
Every department should learn to use AI in the context of its work, but it should not carry the entire burden of evaluating technology for the company.
A designer needs to understand AI for visual exploration, documentation, or idea testing. A marketer needs to understand AI assisted research, segmentation, content production, and analysis. A finance team may use it for classification, early reconciliation, or explaining data.
The problem begins when those teams are also expected to:
- Track model and feature changes every week.
- Compare pricing, usage limits, credits, and licenses.
- Review data retention terms and the use of customer inputs.
- Write usage rules for every department.
- Train colleagues with different levels of skill.
- Measure whether tool costs produce business value.
These responsibilities constitute a separate job. When they are treated as an extra assignment without dedicated time, authority, and success measures, core work suffers. The company also becomes dependent on personal initiative instead of an accountable system.
What does an AI Specialist actually do?
An AI Specialist turns business needs into an executable portfolio of tools, processes, training, and governance. This is broader than being a prompt engineer, an IT technician, or a data scientist.
1. Analyze the needs of each department
An AI Specialist does not begin by asking, “Which AI tool is popular?” The starting point is a business constraint. Examples include proposals that take too long, limited visual exploration, delayed reporting, documents that are difficult to search, or repeated customer questions.
Each need is then assessed by impact, frequency, data quality, risk, and user readiness. The company can understand the problem before buying a solution.
2. Select tools and manage subscriptions
The AI Specialist compares capability, cost, security, integration, and vendor dependence. The objective is not to own the largest number of tools. It is to build the smallest useful portfolio that covers the company’s important needs.
The role also records account owners, plans, renewal dates, active users, credit limits, and overlapping tools. This basic control can prevent forgotten subscriptions and duplicate purchases by separate departments.
3. Control credits and usage costs
The AI Specialist assigns budgets according to function and value instead of distributing unlimited access. Credit usage is monitored so the company can understand the cost of each workflow, project, or output.
Cost control does not mean preventing experimentation. Experiments still receive room to operate, but they have an objective, a time limit, and success criteria.
4. Establish safe usage standards
The AI Specialist works with IT, legal, security, HR, and data owners to define which information may be entered, which tools are approved, when outputs require verification, and who remains accountable for the final decision.
The NIST AI Risk Management Framework helps organizations incorporate trustworthiness into the design, use, and evaluation of AI systems. Its central principle is relevant to any company: AI risk should be managed as an ongoing process, not reviewed once at the point of purchase.
5. Train departments according to their work
Effective AI training is not identical for everyone. Creative teams need examples and limits that differ from those needed by finance, HR, or customer service.
The European Commission explains that AI literacy measures should consider people’s knowledge, experience, education, training, use context, and system risk. Its guidance also makes clear that there is no single training format that fits every organization.
An AI Specialist turns this principle into practical material, work examples, verification checklists, and guided sessions for each department.
6. Measure outcomes and stop what does not work
The AI Specialist measures changes in delivery time, quality, error rates, user adoption, output costs, and progress toward business goals. A tool that fails to create value should be stopped even when it is popular.
The Roku Studio PETA Framework
Roku Studio proposes the PETA Framework to keep the AI Specialist focused on organizational needs rather than tool trends. PETA stands for Mapping, Evaluation, Governance, and Activation.
Mapping
Map business problems, workflows, data, process owners, and the capabilities of each department. The result should be a prioritized list of needs, not a shopping list of tools.
Evaluation
Test several solutions at a small scale. Compare quality, speed, cost, security, integration, and ease of adoption. Every pilot needs a baseline and measurable success criteria.
Governance
Define approved tools, data policies, account owners, budgets, credit limits, verification processes, documentation, and incident reporting. Governance should be strong enough to protect the company but simple enough that employees do not search for workarounds.
Activation
Train users according to their jobs, support adoption in real workflows, and improve standards using evidence from daily use. Activation is complete when teams can use the tools correctly and know when they should not use them.
Does every company need to hire one full time?
Not every company immediately needs a full time AI Specialist, but every company using AI needs clearly assigned responsibility.
In a small company, the function can begin as an official responsibility assigned to one person with protected time and cross departmental support. In a larger company, it may grow into an AI enablement team or center of excellence.
A full time role becomes reasonable when:
- Several departments are already using different AI tools.
- Subscription and credit costs are difficult to track.
- AI is being used with customer data or internal information.
- Experiments are frequent but rarely become stable workflows.
- Teams require repeated training and support.
- Management needs consistent measures of benefit and risk.
It is important to be precise. Regulations such as the EU AI Act do not require every organization to appoint a specific AI officer. The AI Specialist described in this article is an organizational design recommendation from Roku Studio, not a claim of universal legal obligation.
What skills does an AI Specialist need?
An AI Specialist does not have to be a model researcher, but must be able to connect technology, processes, risk, and human behavior.
Core capabilities include:
- Process mapping and business needs analysis.
- Practical knowledge of generative models, automation, data, and API integration.
- Vendor, cost, license, and platform dependence evaluation.
- Foundational knowledge of privacy, information security, copyright, and AI governance.
- The ability to train nontechnical users.
- Change management and cross departmental communication.
- Measurement of productivity, quality, adoption, and business value.
The role also requires the confidence to say “no.” Not every job needs AI, not every feature deserves a subscription, and not every output is safe to use.
A practical first 90 days
An AI Specialist should begin with an audit, move to controlled pilots, and then standardize what works. A large purchase should not be the starting point.
- Days 1 to 30: inventory existing tools, subscriptions, needs, users, data, risks, and spending.
- Days 31 to 60: select two or three high value workflows, run pilots, document baselines, and measure results.
- Days 61 to 90: approve useful tools, stop low value tools, write guidance, train users, and report benefits and risks.
The company does not need to wait for a perfect system. It needs a clear decision cycle, reviewable evidence, and learning that continues as the technology changes.
Conclusion
The AI Specialist will become an important role because companies need someone accountable for tool fit, cost, training, risk, and the outcomes of AI use. The role does not take AI away from designers, marketers, or other departments. It enables each team to use AI without abandoning its primary work.
AI is moving too quickly to remain a side task, but it is too important to be left entirely to vendors. Companies need an internal bridge that understands the business and can translate technological development into a safe and useful way of working.
For organizations that want to map AI needs, build workflows, and improve team capability in stages, Roku Studio helps connect strategy, creativity, technology, and real implementation.
Frequently asked questions
What is the difference between an AI Specialist and IT staff?
IT staff focus broadly on infrastructure, devices, networks, access, and technical support. An AI Specialist focuses on AI needs assessment, tool evaluation, cost control, user training, governance, and outcome measurement. The two functions should work together.
Does an AI Specialist need to build AI models?
Not always. Many companies need someone who can select, integrate, manage, and teach existing systems. Deeper technical capability becomes necessary when the company develops its own models or AI systems.
When does a company need a full time AI Specialist?
A full time role is worth considering when many departments use AI, costs are difficult to control, sensitive data is involved, training demand is increasing, or experiments need to become measurable workflows.