Generative AI is finding its place across all industries, from retail and finance, to telecommunications and travel. In many cases organizations are rushing to deploy generative AI across their business but are then hampered by implementation challenges or struggle to find the right technology, or use case, to derive business value.
According to Gartner, at least 30% of generative AI projects will be abandoned after the proof of concept stage by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. This leaves many business executives impatient to see returns on investments in generative AI.
However, it's likely these generative AI projects are often dismissed without getting to the crux of why things are not working as expected and not realizing that a tailored, industry-specific technology strategy is required to really see success.
Alongside technology vendors, the channel is critical in getting to the bottom of the true individual business challenges that generative AI can help to solve or improve for maximum ROI.
By identifying the top internal and external pain points and use cases in each industry, the channel can help shape where organizations can best adopt technologies and adapt processes to meet existing and future demands.
The channel is in the envious position that means they can get really close to organizations like no technology vendor really can – to drive analysis, consultation, deployment, and operations to allow a business to focus on the much needed change management towards the right business outcomes.
The market opportunity
For consumers, there is widespread acceptance of generative AI solutions, such as intelligent automated agents across different industries and demographics, from helping them with retail returns, to choosing the right mortgage.
In Kore.ai’s 2024 CX Benchmark report, 72% of UK consumers said they had chatted with an automated assistant of some kind in the past year. The majority (72%) of consumers said they would be interested in having an automated assistant for banking to help them with transactions and spending.
A further 79% said they would be interested in an automated assistant for loyalty services in the retail sector, and over two thirds said the same for finding product or brand information. The appetite for generative AI solutions across industries is clearly there.
Revolutionizing key sectors
So, what sectors can make the most of generative AI and what use cases can the channel showcase to aid buy-in? Additionally how can the channel play its part in increasing its adoption?
For a number of industries, an AI-powered search assistant provides instantaneous support to users whether they ask a question or search for information. Gartner reports that 47% of employees find it difficult to locate the information they need because the data is scattered across various databases. The traditional keyword searches often fail to retrieve the most relevant information, leading to wasted time and effort. In contrast, generative AI, particularly through Retrieval-Augmented Generation (RAG), offers a groundbreaking solution by transforming knowledge management and research into a competitive advantage.
Generative AI-powered automated assistants can extract precise answers from vast datasets in real-time, transforming static knowledge repositories into dynamic, proactive assistants. These assistants continuously learn, adapt, and deliver information that is not only accurate but also timely and relevant. For example, a leading investment bank developed an AI assistant using an advanced RAG-based Search AI platform to support its financial advisors with research and analysis. This AI bot scans over 100,000 research reports within its knowledge repository, synthesizing recommended answers with citations. The result is less time spent on tedious tasks and more focus on understanding client needs and providing tailored financial advice – creating true value for customers.
For employees or external visitors it offers unified information discovery across all content sources e.g., webpages, FAQs, blog posts, forums, resources, etc., and allows users to filter the results too. Its deep learning capabilities also monitors user behavior and proactively presents personalized and contextual recommendations to aid quick answers and better decision making.
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In retail, this might help to reduce product returns of white goods, or a bank’s contact center to help an agent support a customer request with personal information. Personalized recommendations powered by AI can lead to a 10 – 30% increase in sales, demonstrating the significant impact of these technologies on retail growth and customer loyalty.
Digging into some other industry-specific use cases, let’s take the telecom industry first. Generative AI isn’t just a trend, it’s rewriting the rules of customer interaction. From eliminating hold times to delivering personalized responses, the industry is rapidly adopting AI to enhance customer experiences, streamline processes, boost productivity, and optimize network operations.
One global telecom leader recently deployed a versatile virtual assistant that operates around the clock in multiple languages to swiftly address customer queries across different topics. Through collaborative efforts with technology and channel partners, substantial improvements were achieved in journey testing and gap analysis methodologies.
When it comes to the travel industry, AI can help airlines to provide a better, more comfortable experience during a flight, for example. With voice-activated assistance, personalized health and safety information, and adaptive meal options, airlines can tailor preferences during a flight to make them more convenient, enjoyable, and secure.
The channel’s role in generative AI adoption
To be successful drivers of generative AI adoption, channel partners must have a broad range of expertise, access to a combination of technology solutions, the services to manage and maintain this technology, as well as the capacity and capability to allow for scale.
Furthermore, a good channel partner will know the right time to introduce a new solution and can formulate tailored positioning and projected outcomes. Channel partners can also build and integrate new systems without sacrificing advantages of existing systems.
But it's the partners’ unique ability to be able to get under the skin of enterprises and find the root cause of inefficiencies and find where automation can help and make the biggest difference. Providing the right technologies that offer responsible AI frameworks and low-code/no-code platforms to transform customer and employee experiences, will make them indispensable.
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This approach to strategic channel partnerships fosters a unified relationship between partners and customers, enabling them to navigate the challenges of generative AI rollouts together. By addressing implementation hurdles and identifying the right technology, or use case, this coordinated effort paves the way for successful AI integration and drives long-term growth.
Partners are placed in a prime position to create and offer an array of technologies and services under the ‘generative AI as a Service’ umbrella becoming an ideation and innovation hub for their customers, creating multiple new revenue streams and bolstering growth of existing revenue streams.
