Organizations are turning to generative AI to boost productivity and fuel innovation. While purchasing a model or application will not inherently create value, the right use case, data, employee readiness, governance, and measurement initiatives will position businesses to harness AI powerfully. Many companies are struggling to scale beyond pilots and deliver demonstrable value, according to McKinsey.
For businesses exploring the use of generative AI, the focus should be on finding the right use cases and a viable pathway from experimentation to production that delivers value.
Focus On The Business Problem, Not The Model Or Tool
Many companies fall into the trap of determining what AI can do based on what problem they have decided to solve. A better approach is to start with the business challenges the organization is facing and determine what AI can do to address them more effectively than current solutions.
Consider repetitive processes that require extensive knowledge or expertise, are very time-consuming, or involve massive amounts of information. The first viable applications of generative AI often come from customer service, knowledge management, marketing, software development, research, and document preparation.
It is also important to understand what specific problem the organization hopes to address with a given application of generative AI. Ask yourself these questions before selecting a use case:
- What business problem are we attempting to solve?
- How will implementing AI enhance or improve the current process?
- What objective will measure the success of the initiative?
Select High-Value, High-Impact Generative AI Use Cases
Not every business process is a viable candidate for generative AI, and companies must prioritize use cases based on value and impact. It is important to start with simple, low-complexity applications where the benefits are obvious.
Strong candidates typically include a clear process and data or knowledge sources, high-frequency transactions, significant business value, human review, and clear criteria for measuring improvements in speed, cost, quality, or business impact. It is easier to justify investing resources in an AI system that makes information searches more efficient than one that makes multimillion-dollar business decisions autonomously.
A small-scale pilot can also help an organization gain experience with AI applications prior to committing extensive resources. Many organizations partner with a generative AI development company to help identify and validate these use cases before scaling.
Lay The Groundwork For Data And AI Infrastructure
The effectiveness of AI is directly related to the quality and richness of the data with which it is trained. Ineffective or poor-quality prompts, as well as low-quality training data, can lead to erroneous outputs, limiting the business value of generative AI. Before organizations begin using AI in production, it is important to analyze the data available to determine if it can deliver the expected results.
Businesses must also take steps to organize and govern the information to help AI applications function correctly and support the intended business purpose. For example, retrieval augmented generation models can provide more relevant responses by connecting language models with information from permitted data sources.
Organizations must also determine the infrastructure, models, integration requirements, monitoring and maintenance capabilities, and other factors that comprise effective AI infrastructure. Businesses building this foundation often work with an experienced AI development services provider to design reliable data pipelines and model infrastructure.
Ensure Robust Security And Governance Policies Are In Place
Security and governance risks can offset the potential benefits of generative AI.
A strong governance model will govern the responsible use of information and AI within the confines of appropriate policy and control environments.
Organizations must determine what information employees can input into AI applications, what tools they can use, and when human review is necessary. It is also important to establish guidelines for validating and verifying model outputs and specify who owns the AI systems and their results. These policies should cover security, data privacy, intellectual property, and control issues.
AWS has identified business alignment, people, governance, platform, security, and operations as key pillars of a comprehensive governance model for generative AI. Organizations strengthening these policies frequently rely on a trusted Artificial intelligence development services partner to establish security and compliance frameworks.
The organization should also invest in employee education, awareness initiatives, and controls to reduce the risk of inappropriate or unauthorized use of AI technologies.
Empower Your Employees To Use AI Responsibly
While strong governance practices are critical, employees must understand how AI impacts their jobs and use it responsibly to ensure positive business outcomes. Training initiatives should enable staff to adopt AI confidently and empower them to make responsible decisions. Businesses can also build a network of AI ambassadors that help ensure proper use while reducing internal resistance by addressing employee concerns. According to the World Economic Forum, effective communication, pilot programs, knowledge sharing, and employee engagement initiatives help organizations reduce risks and accelerate AI adoption.
Employees who regard AI chatbots and similar tools as a means of empowering themselves rather than a threat will be more receptive to its use in the workplace.
Accelerate The Transition From Pilot Programs To Production
Most companies struggle to move from a compelling proof of concept to an actual working application. An organization must have a clear path from the pilot to production, taking into account technology, processes, people, governance, costs, user support, and other issues.
Rather than rushing to implement AI across the entire business, companies should invest in a clear rollout strategy, including continuous improvement and optimization. One helpful strategy is to divide the rollout into phases (pilot → validate → optimize → expand), accelerating the transition from experimentation to full production. Companies scaling pilots into full applications often work with an AI app development services provider to build production-ready systems.
The company should invest sufficient time in the pilot program to collect user feedback and determine if the results meet the expected criteria for production use. A rollout strategy based on continuous iterations is likely to yield better results.
Measure The Success Of The Initiative
The ultimate test of an organization's ability to adopt generative AI is its ability to measure the business value that it creates. It is imperative to move beyond mere adoption and focus on the value delivered to the organization through well-defined key performance indicators (KPIs). Specific indicators will depend on the anticipated use of AI, but common examples include increased speed, reduced costs, improved customer experience, quality improvements, enhanced productivity, and higher revenues for given applications.
McKinsey notes that establishing well-defined business KPIs is one of the adoption practices most strongly associated with bottom-line benefits from generative AI.
KPIs also help ensure that the company focuses and concentrates its efforts on the most compelling applications while balancing the benefits delivered by each use case.
Create A Continuous Adoption Strategy
Using generative AI technology does not happen just once, but is an ongoing process that calls for ongoing review of the model performance, user needs, corporate objectives, and security needs. It is important for corporations to have an ongoing review and improvement process that would guarantee the maximum benefit from the usage of AI technology.
Moreover, well-funded companies need to make sure they constantly improve their existing applications while looking for new tools and technologies to adopt. It is all about making sure that investments in AI technology bring real value to the corporate strategy and that the governance, models, security, and other aspects are up-to-date.
At the same time, organizations need to ensure that they optimize business processes in combination with AI applications. According to McKinsey, workflow optimization results in noticeable revenue growth and is mostly associated with those companies that report improved business value through the usage of generative AI technology.
This all comes down to making sure that there is a proper approach, use case, data, AI readiness, employee empowerment, and value metrics in place.
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