Generative AI (GAI) stands to be a transformative force in the business world, promising to revolutionize everything from product development to data analysis.
As companies rush to adopt this technology, they face a critical challenge: how to effectively implement and leverage generative AI, while managing an evolving world of risks.
This article explores 10 questions every company should consider when embarking on their GAI journey.
1. What Are Your Specific Goals for Using GAI?
Identify how you think generative AI can create value for your business, whether through enhancing customer experiences, increasing process efficiencies, or innovating products and services.
Generative AI can be used to spark creativity, save time, automate work, and help team members develop their skills, particularly in technical areas.
Determine what you want to use GAI for and educate yourself on the different tools available for different needs.
2. Where Do You See the Opportunity to Leverage GAI?
Identify opportunities to leverage GAI by examining what you’re doing frequently and where processes are bogging down. Focus on repeatable tasks that occur at significant volumes. Look for bottlenecks.
These tasks may involve creating content, such as writing reports, drafting emails or social media posts, or answering simple queries. Ideal tasks for generative AI are those that do not require advanced thinking or complex decision-making.
Focus on areas where a personal touch is not essential. For example, AI can efficiently handle initial customer queries, but a human should manage more nuanced or sensitive interactions.
By identifying such tasks, businesses can streamline operations, reduce human workload, and improve overall efficiency.
3. Are You Ready and Organized to Roll Out a Strategy?
Assess whether your organization has the necessary resources, structure, and policies to implement generative AI effectively. This includes training staff, developing and/or implementing solutions, testing, benchmarking, and monitoring use.
If you’re hoping to leverage company knowledge as part of the solution, take a hard look at how and where content is stored, who has access to it, and how best to identify strong examples.
Robust information governance is critical for making institutional knowledge accessible and usable by GAI systems. Proper information governance ensures that company data and knowledge are also well-structured, secured, and managed.
It involves categorizing information, establishing access controls, and implementing data quality measures. Without strong information governance, organizations risk feeding their AI systems incomplete, outdated, or irrelevant data. This can potentially lead to inaccurate outputs or missed opportunities.
As a bonus, well-implemented information governance can help identify and curate the most valuable knowledge assets.
4. What Are the Potential Risks and Ethical Considerations?
We’ve all read stories of data leaks, hallucinations, and inappropriate responses to GAI queries. While we won't rehash those here, it's important to note that careful usage of content created by GAI—adhering to a comprehensive information governance framework—could have prevented many of these issues.
Your framework should include guidelines for privacy, security, accountability, and transparency. If you’re sharing client information with any of these tools, your policy needs to specify how and when that information can be shared. You must also ensure you’re complying with each customer’s policy.
Additionally, focus on mitigating biases in AI-generated content and preventing disinformation. Form an ethics working group to oversee the deployment and use of generative AI. The group can help ensure adherence to the organization's values and guidelines.
Having an explicit “safe and effective use” policy, communicating that policy, reiterating it periodically, and ensuring that anyone using generative AI understands and abides by it is essential.
5. How Will You Integrate Generative AI Integrate Into Existing Systems and Workflows?
GAI needs to be integrated into daily work to be effective. Encourage employees to identify opportunities where GAI can assist them.
How will you train employees to work effectively with AI tools? What support systems need to be in place during the transition?
Identifying possible integration points between GAI tools and existing software. Are your current vendors introducing GAI into their offerings?
Once implemented, how will you monitor and evaluate the performance of different GAI tools to ensure they meet your operational standards and improve overall efficiency?
6. How Will You Prioritize AI Implementations?
How are you going to decide what GAI should do first – and next?
The path to implementing AI in an organization can have many simultaneous branches. Consider the options of leveraging free tools, implementing existing software with new AI capabilities, building a private large language model (private LLM), or utilizing retrieval-augmented generation (turning to your institutional knowledge for answers).
Your answers to questions 1 and 2 above will help with this prioritization, as will understanding the resources and investments required to roll out each tool. With a policy and training in place, there are tools you can roll out today at no cost.
Other tools will require more time, preparation, and investment. An AI steering committee can assist with this prioritization, serving as both a resource and a corporate control mechanism.
7. What Financial Investment Will You to Make?
Determining the cost for licensing, development, deployment, and maintenance of the solutions you envision may be your biggest challenge.
Estimates for what it costs to implement different tools and technologies vary widely, as do estimates of ongoing costs. While some vendors have begun offering “all-in” monthly pricing for these toolsets, most are not there yet, and those with specific monthly prices are subject to change.
Perform due diligence with different vendors. Leverage organizations where members freely share information on their efforts and experiences.
8. Do You Have Access to the Expertise Needed to Succeed?
When considering the implementation of generative AI in your business, it's crucial to assess whether you have the necessary expertise to succeed.
This involves evaluating the skills and knowledge required to develop, deploy, and maintain AI solutions effectively. Do you need a rocket scientist on staff? Probably not, but do you need a data scientist, a GAI developer, a data governance expert, or a prompt engineer?
The answers to the previous questions—along with the size of your organization and budget—will determine whether you develop expertise in-house or need to turn to trusted partners.
Smaller organizations with limited budgets might find it more feasible to partner with external experts rather than building an in-house team, especially for highly specialized or complex applications.
In cases where you want to implement quickly, partnering with external experts can significantly accelerate the deployment process.
9. How Will You Measure the Success of Generative AI Initiatives?
As with any organizational implementation, establishing metrics and key performance indicators (KPIs)—beyond just cost savings—will you help you measure the impact of GAI.
Quantify the increase in output or efficiency of tasks automated or augmented by AI to track productivity gains. Monitor time savings to gain insights into how AI is streamlining workflows and reducing time spent on repetitive tasks.
Assessing improvements in employee satisfaction can indicate how well AI tools are integrated into daily operations and whether they enhance the work experience. Similarly, measuring customer satisfaction can reveal the impact of AI on service quality, response times, and overall customer experience.
Regularly reviewing these metrics and adjusting your toolsets and processes will ensure your generative AI initiatives deliver tangible value to the organization.
10. How Will You Stay Up to Date with Advances in GAI?
The landscape of generative AI is continually evolving. Staying informed about advances in the field could be a full-time job, one of the roles of an AI steering committee member, or the responsibility of a small department.
At a minimum, appoint an individual or form a team responsible for tracking the latest trends, research, and technological advancements. This will involve continuous learning and engagement with the AI community.
Plan to attend industry conferences and workshops to learn from experts, network with peers, and gain firsthand knowledge of emerging technologies. Subscribe to AI-related publications, newsletters, and blogs for updates on the latest developments.
Share what you learn organization-wide regularly. When opportunities arise, engage in pilot projects to test new AI technologies and evaluate their potential benefits for the organization. This hands-on approach can provide valuable insights and help guide your organization to better informed decisions.
Navigating the complex landscape of GAI requires a thoughtful and strategic approach. Embracing generative AI with a clear roadmap and a commitment to ongoing learning and improvement will position businesses at the forefront of innovation and competitiveness.
