Eyad Tachwali, the Sr. Director Advisory at Gartner, says AI-based applications are already revolutionising functional areas such as marketing, HR, and customer service
We’re excited to see the growing adoption and interest in General Artificial Intelligence (Gen AI) across various industries. According to recent Gartner data, one in five organizations report that they are either piloting or have already deployed Gen AI solutions. Additionally, 70% of organizations are currently in the investigation stage, exploring the potential applications and benefits of implementing this technology.
Among the most prevalent use cases are content generation—spanning creative content development in text, images, code, and video—and stakeholder engagement. These applications are already revolutionizing functional areas like marketing, HR, and customer service. Additionally, Gen AI is playing a critical role in analytics and decision intelligence.
For example, some emerging functional use cases include:
- In Sales and Customer Service:
- Digital sales humans: Augment sales bots and avatars — they have some human seller characteristics
- Generative value messaging: Rapidly turn customer data into customized content
- Generative business intelligence: Interpret and synthesize sales data faster — add traditional AI techniques (i.e., ML and optimization) to uncover hidden correlations and gain prescriptive insights
- In Supply Chain:
- Complex model explainability: Textbased explanation of recommended production, route planning or customer fulfilment decisions
- Vendor engagement and compliance: Summarize contracts, ongoing contract compliance, vendor onboarding/Q&As
- In HR:
- Tailored job descriptions: Quickly create more compelling and inclusive text to better attract diverse talent with in-demand skills
- HR chatbots: Use these tools, with enhanced NLP capabilities from generative AI, to • handle routine employee requests using human-like text • provide a better user experience
- In IT:
- Coding assistance: Translate code from older coding languages, like COBOL, into modern programming languages, like SQL, KnowledgeSQL, and Python
We’re also seeing specialized industry-specific applications such as:
- In Financial Services:
- AI frontline co-pilot: Chat interface helps client-facing employees get important information faster
- Claims management: Individualized suggestions/explanations on claims coverage and applicant-friendly reasons for denials
- In Education:
- Student tutors: Conversational UI to support personalized learning
- Language training: AI reading and speaking companion
- In Healthcare:
- Conversational patient self—triage and checking symptoms: Chatbot makes suggestions and guides patients regarding acute symptoms, chronic condition management, health and wellness activities, or behavioural health needs
- Auto-composition of clinical messages: Automatic replies based on the content and tone of patient message, accessible clinical data, and clinician’s tone and preferences
Why according to you should companies leverage generative AI?
Enterprise leaders cannot ignore generative AI because their rivals won’t. As the hype swirls — will it save untold hours of work? will it help organizations revolutionize their business models boost their revenues and deliver on their mission?— only one thing is clear: It won’t go away.
Nearly half of more than 2,500 executives we polled said they have been planning to spend more on AI of all kinds since ChatGPT rolled out in late 2022. Separately, dozens of executives have told us in conversations that they want to move faster than they did on past AI initiatives and get something into production within a year.
Generative AI is also important for companies as it provides new and disruptive opportunities to increase revenue, reduce costs, improve productivity and better manage risk. In the near future, it will become a competitive advantage and differentiator.
- Revenue opportunities
- Product development: Generative AI will enable enterprises to create new products more quickly. These may include new drugs, less toxic household cleaners, novel flavours and fragrances, new alloys, and faster and better diagnoses.
- New revenue channels: Gartner research shows that enterprises with greater levels of AI maturity will gain greater benefits to their revenue.
- Cost and productivity opportunities
- Worker augmentation: Generative AI can augment workers’ ability to draft and edit text, images and other media. It can also summarize, simplify and classify content; generate, translate and verify software code; and improve chatbot performance. At this stage, the technology is highly proficient at creating a wide range of artefacts quickly and at scale.
- Long-term talent optimization: Employees will be distinguished by their ability to conceive, execute and refine ideas, projects, processes, services and relationships in partnership with AI. This symbiotic relationship will accelerate time to proficiency and greatly extend the range and competency of workers across the board.
- Process improvement: Generative AI can derive real, in-context value from vast stores of content, which until now may have gone largely unexploited. This will change workflows.
- Risk opportunities
- Risk mitigation: Generative AI’s ability to analyze and provide broader and deeper visibility of data, such as customer transactions and potentially faulty software code, enhances pattern recognition and the ability to identify potential risks to the enterprise more quickly.
- Sustainability: Generative AI may help enterprises comply with sustainability regulations, mitigate the risk of stranded assets, and embed sustainability into decision-making, product design and processes.
What are the challenges companies face in terms of adopting and using Gen AI and how can they be overcome?
Several factors still challenge the widespread adoption of generative AI in business operations. Also, it varies based on the use case you are pursuing and how you plan to implement them.
You might use off-the-shelf implementation to enable use cases like coding assistants, knowledge workers (brainstorming, drafting, and editing documents), and marketing content creation for quick wins.
There are fewer challenges in this scenario, but there are potential risks, for example: IP and confidentiality Risks: Staff will need training and guidance not to upload sensitive information into a public large language model (LLM), and organizations will need to draft acceptable use guidelines with general counsel to address the particular IP and confidentiality risks associated with public LLMs
For a more competitive differentiating use case, you might need to use more custom implementation to enable use cases like personalized sales content creation or GenAI-assisted customer support. For this, challenges, and risks to consider include:
- Cost exposure with the expectation of opaque and high-margin pricing for the LLM platforms to which your custom software will connect
- Scope risk that significant benefits in some use cases will create a demand for other use cases that would see productivity gains, but at unworkable costs. There will be increasing internal pressure to invest in unproven or negative business cases, which must be avoided
- Workforce anxiety among workers likely within some job families, which could diminish productivity and indirectly impact the business case
- Accessibility to skilled talent that can implement and manage these use cases
To address this, we recommend the following best practices for using generative AI:
- Start inside. Before using generative AI to create customer- or other external-facing content, test extensively with internal stakeholders and employee use cases. You don’t want hallucinations to harm your business
- Prize transparency. Be forthcoming with people, whether they be staff, customers or citizens, about the fact that they are interacting with a machine by clearly labelling any conversation multiple times throughout
- Do your due diligence. Set up processes and guardrails to track biases and other issues of trustworthiness. Do so by validating results and continually testing for the model going off-course
- Address privacy and security concerns. Ensure that sensitive data is neither input nor derived. Confirm with the model provider that this data won’t be used for machine learning beyond your organization
- Take it slow. Keep functionality in beta for an extended period of time. This helps temper expectations for perfect results
Are companies aware of regional and global policies surrounding the use of Gen AI?
It varies by company but technology leaders must work with general counsel and regulatory affairs to understand the impact of existing and potential new regulations and legislation on proposed GenAI use cases, particularly in the areas of bias and intellectual property; data retention and disposal; and regulation of this new technology.
Currently, there are regulatory changes in the U.S. (and separately in California), the U.K., the EU and Brazil. There are national governance frameworks in Canada, Japan and Singapore; strategies to become world leaders in the UAE and China; bans in Russia, China, North Korea, Cuba, Iran and Syria; and Italy briefly banned ChatGPT. In addition, does not permit certain countries from accessing its APIs. The global regulatory framework will become more complex.
How can companies use their resources on using Gen AI to create a competitive advantage?
Companies who do want to leverage generative AI to create business value should:
- Run a workshop to generate use-case ideas with the business, focusing on the disruptive potential of generative AI and the way in which it can enable strategic objectives
- Prioritize the use cases for your pilot against their potential business value and feasibility. Focus on no more than a few use cases for your generative AI pilot
- Assemble a small but diverse team, including business partners, software developers and AI experts. Dedicate this cross-functional team for the duration of the pilot
- Create a minimum viable product to validate each use case. Identify the target business key performance indicator (KPI) improvement hypothesis, and define the deployment approaches and risk mitigations required to quickly test this hypothesis
- Deliver the minimum functionality required to test the use cases, and refine your assumptions on the cost and value of scaling them. Decide whether to stop, refine or scale each use case. Build upon initial successes to expand the generative AI pilot