As technologies like ChatGPT exemplify, generative AI (gen AI) is rapidly evolving, prompting businesses across industries to refine their application strategies. The challenge in 2024 is to leverage these new technologies to drive positive business outcomes and enhance customer satisfaction effectively.
Since its introduction, one of the main revelations has been the distinct roles this new generation of AI can fulfill, transitioning from the traditional focus on analysis and classification to creative content generation. Generative AI uses complex algorithms and neural networks to mimic human creativity, producing diverse outputs such as text, images, and music.
Distinct from artificial general intelligence (AGI), which seeks to replicate full human intellectual capabilities, generative AI is task-specific. It provides practical solutions within its trained areas, adeptly handling various tasks and adapting to new situations based on incoming data.
Practical Uses and Limits of Generative AI Technology
In practice, generative AI is a potent productivity tool, enabling rapid content generation across mediums such as text, images, sounds, animations, and 3D models. It not only learns and retains patterns and nuances in language but also remembers past interactions, leading to more coherent and contextually relevant exchanges with users.
However, gen AI currently falls short in decisions involving numerous complex factors, particularly those requiring deep contextual or emotional understanding. While it excels at data-driven suggestions, integrating and managing nuanced human factors remains beyond its reach, at least for now.
According to Will Devlin, vice president of marketing at customer engagement platform firm MessageGears, business and industry adopters can leverage AI without fear of failure.
“Any marketer who has ever conducted a standard A/B test can tell you that failure isn’t always something to be avoided. In our careers, we constantly learn new tools, technology, and techniques. Fear of failure is always going to be a necessary part of that learning and growing process. As with anything new, there are concerns around AI that are relevant and real,” he told TechNewsWorld.
Understanding the AI Path Forward
Michael Fisher, chief product officer at digital compliance and data management firm Complykey (formerly Waterfield Technologies), has four predictions addressing those areas.
Over the past year, contact centers, primary adopters of this technology, have rapidly integrated generative AI. Fisher predicts that in 2024, the focus will shift towards a deeper understanding of generative AI’s ROI.
He expects contact center leaders and other AI adopters to increasingly focus on calculating the cost of AI more meaningfully. This effort includes a better understanding of how the deployment cost can be optimized related to scale and cost per transaction.
Managing Risks in Fast-Paced AI Adoption
Gen AI will continue to be adopted the fastest this year in marketing and customer prospecting, which is cross-industry, Fisher offered as a second prediction. In the lead generation business, you must consider the value, the cost, and the risks.
The inherent risks are slowing adoption in highly regulated industries like health care, government, and finance. The back end of the contact center in these industries will be aggressive about using generative AI for summarizing data and reporting.
“But on the customer-facing front end, those verticals will all move slower and more deliberately. The further you get away from industries that are already highly regulated, like retail, the faster generative AI adoption we’ll see,” he observed.
Advancements in Cloud and Video AI Solutions
Many companies have continued offering on-premises and cloud-based contact center solutions catering to customer preferences. However, keeping both solutions live creates a technology cost drain for vendors. So, leverage one over the other.
Fisher’s third prediction was that “in 2024, more companies will sunset their on-premises solutions or raise the price significantly to make an on-premises solution commercially unviable for customers — essentially forcing cloud adoption and innovation on customers.”
The insurance industry uniquely uses video-based communications for things like collaborative document signing or showing accident damage to a vehicle. Most industries have been slow to adopt video as a customer service channel.
“This will change in 2024. We expect video to be more broadly deployed as a customer service channel across industries, especially for companies that sell a physical product that benefits from a show-and-tell,” Fisher noted as his fourth leveraging prediction.
Specific use cases will help drive demand for this feature. Changing consumer preferences, led by Gen Z’s comfort and familiarity with video-based content, may also help, he shared.
Precision in Handling Massive AI Data Sets
MessageGear’s Devlin thinks it is vital that as brands start to harness AI — particularly generative AI — they put guardrails in place and develop standard operating procedures and guidelines for their teams to follow.
That will be a learning process. Companies must realize that Gen AI is not a one-size-fits-all solution.
“I expect that AI technology will only get better as we get more hands-on with it,” he cautioned, adding, “Because AI is such a new technology, brands are still navigating how to manage it and ensure they use it responsibly and to its fullest potential.”
A recently conducted survey by MessageGears of marketers at enterprise brands showed that the most significant challenges brands face when implementing AI solutions are limited expertise, staff training, and integration complexity.
“AI modeling is only as good as the data you put into it. Conversely, AI can be a powerful tool, helping brands improve conversions and ROI, save time, reduce time-to-value, and improve testing and learning,” Devlin told TechNewsWorld.
Integrating Human Insight with AI Technology
Shahid Ahmed, group EVP for new ventures and innovation at digital consulting firm NTT Data, revealed that his company’s 2023 Global Customer Experience Report found that the majority of CX interactions still require a form of human intervention.
According to this report, executives agree this will remain a critical part of customer journeys. Despite 80% of organizations planning to incorporate AI into CX delivery within the next 12 months, the human element will be central to its success.
“As enterprises turn their attention to how automation can complement and enhance human capabilities, they will place greater emphasis on closing the mounting skills shortages that will challenge AI aspirations,” Ahmed told TechNewsWorld.
He cautioned that the fundamentals of AI and big data analytics will become baseline skills for most jobs across industries, and new hires will not be the only pathway.
“Research by NTT Data uncovered that business leaders are more likely to have seen profitability of more than 25% over the last three years because of investments in reskilling and upskilling initiatives. This trend will continue in 2024, with more curated teaching experiences to help close skills gaps and meet the needs of organizations,” he advised.
The Risks of DIY AI Implementation
AI’s best leveraging approach might well be in a managed cloud combination. AI is everywhere today. Adopters should ponder what numbers chart this explosive growth.
A report by cloud security provider Wiz shows a key connection between using AI services via a managed cloud platform. Its analysis of aggregate data related to a large sample of organizations provides a comprehensive overview of how generative AI and machine learning are being used in the cloud and its implications for organizations.
According to that research, AI is rapidly gaining ground in cloud environments. Over 70% of organizations now use managed AI services. At that percentage, the adoption of AI technology rivals the popularity of managed Kubernetes services, which Wiz sees in over 80% of organizations.
Another noteworthy view is many organizations experiment with AI but do not go beyond that step.
Only 10% are power users who deployed 50 or more instances in their environments. While the adoption of AI in the cloud is soaring, many organizations (32%) still appear to be in the experimentation phase with these tools, deploying fewer than 10 instances of AI services in their cloud environments., according to the report.
Enhancing Gen AI With Predictive Analytics
For most folks, 2023 was the year that AI came into focus, with adopters asking how to utilize it best, observed MessageGear’s Devlin. Now, if they have not already started using AI regularly, most brands are, at the very least, AI-curious.
“They want to test and see how it can help them and are ready to explore. As brands become more comfortable with the idea of AI, I think we’ll see certain roles grow in complexity while others are made more efficient using AI tools,” he noted.
Generative AI becomes especially powerful when paired with insights from predictive AI. Not only do you know when and where a customer wants to hear from you, but you also know the likelihood that they will make a purchase and what language and imagery will likely sway them to act.
“It’s a combination that brands are only beginning to take advantage of, and it has almost infinite potential,” he concluded.
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