Generative Artificial Intelligence or GenAI has emerged as a revolutionary tool in business, promising to transform the way we operate and make decisions.
According to a recent Gartner study, only 10% of organisations experimenting with AI have achieved an advanced level of implementation and success. However, the lessons learned by these organisations can provide an invaluable roadmap for those looking to adopt GenAI effectively.
In this blog, we will explore how enterprises can make the most of generative AI, based on the recommendations and findings of the Gartner study.
Generative AI is a type of artificial intelligence that focuses on creating new content from existing data. Unlike other forms of AI that merely analyse and classify data, GenAI can generate text, images, music and other types of content that are original and useful for a variety of purposes. It uses advanced models such as deep neural networks to learn patterns and structures in training data and then applies that knowledge to produce new content. This has potential applications in areas such as content creation, product design, code generation and more, making it a powerful tool for business innovation.
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The first step in leveraging GenAI is to establish a clear vision of how generative artificial intelligence will drive business objectives.
Gartner stresses the importance of aligning GenAI objectives with the corporate vision of the company. This involves answering key questions such as:
According to Gartner, it is critical to define how GenAI will contribute to the achievement of specific business objectives such as revenue growth, improved customer satisfaction, cost reduction or increased staff productivity.
For example, generative artificial intelligence can drive business model changes that generate new business initiatives, or it can be used to improve the analysis of customer behaviour, increasing customer satisfaction.
To measure the success of GenAI initiatives, Gartner recommends establishing clear metrics that relate to business objectives. Suggested metrics include customer satisfaction index, revenue growth by product line, number of new business initiatives and reduction in processing time.
Once you have established a clear vision of the why and how of your GenAI investment, the next step is to identify and remove organisational barriers that could hinder GenAI success.
According to Gartner, this involves documenting objectives and taking a portfolio approach to AI opportunities, aligning projects with corporate goals.
Selecting metrics that act as proxies for financial and risk outcomes is crucial to establishing project credibility. Gartner recommends collaborating with data and analytics managers to determine the most appropriate metrics for future projects.
Implementing formal accountability structures, such as a RACI (Responsible, Approving, Consulted and Informed) matrix, can strengthen IA results. This matrix helps to clearly define who is responsible, who should approve, who should be consulted and who should be informed at each stage of the project.
The adoption of any type of artificial intelligence carries a number of risks, and GenAI is no exception. Gartner identifies several key types of risk, including regulatory, reputational and competitive. All must be properly assessed and mitigated.
Understanding the evolving regulatory landscape is critical. Gartner suggests enabling collaboration between those at the helm of AI investment and members of legal, risk and security teams to assess use case feasibility and acceptable risks.
In addition, it recommends the creation of an AI governance office to serve as an independent audit committee to review results.
Safety and security are crucial to prevent potential threats to AI. This includes both malicious and benign threats within the organisation.
Gartner suggests strengthening security across all enterprise security controls, data integrity and AI model monitoring, and leveraging external resources to secure AI systems.
Aligning AI strategy with cloud strategy can be an effective solution to reduce technical debt. Creating a technology roadmap to modernise data and analytics infrastructures and a startup accelerator programme to innovate incrementally are other key recommendations from Gartner.
Finally, it is essential to prioritise GenAI projects based on their value and feasibility.
Gartner suggests using simple criteria to evaluate and score each project, allowing projects to be ranked against each other.
Some of the technical feasibility factors include access to labelled data, feasibility of architecture and technology, and availability of skills and people to execute the project.
On the other hand, business value factors include alignment with the company's mission and values, sponsor support and the ability to measure KPIs.
Projects should be ranked according to their commercial value and technical feasibility.
Projects with high contribution to commercial value and high technical feasibility are the most promising. However, it is important to avoid projects where the feasibility is so low as to make the project impossible.
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Enabling Generative AI in an organisation is not just a technology issue; it requires a cultural and strategic shift.
In its study, Gartner defines a series of steps that companies should take to successfully enable Generative AI:
It is essential that employees understand what generative artificial intelligence is and how it can benefit the organisation. Providing training and workshops on the basic concepts of GenAI and its practical applications can help build a solid knowledge base.
GenAI should not be seen as a solution separate from the organisation's infrastructure, but as a tool that integrates into existing processes and workflows.
To this end, it is crucial to identify areas where GenAI can improve efficiency and productivity.
The implementation of GenAI in the company must be a collaborative effort involving different departments of the organisation, including IT, marketing, sales and human resources.
This collaboration will ensure that GenAI is used in a way that is consistent and aligned with the company's goals.
Starting with pilot tests and smaller-scale projects allows companies to test the technology, assess its impact and make adjustments before wider implementation.
This iterative approach helps minimise risks and maximise benefits.
Conclusion
Adopting generative AI can be a transformative process for enterprises, but it requires careful, strategic planning.
Based on the Gartner study, we have explored how companies can make the most of GenAI by establishing a clear vision, removing organisational barriers, assessing and mitigating risks, and prioritising valuable and feasible projects.
By following these recommendations, companies can not only implement artificial intelligence successfully, but also make a significant impact on their business and operational goals.
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