AI...Beyond Cost Savings
It’s Time To Talk Revenue Generation
Aside from this week’s introduction of Meta’s Threads as a possible “Twitter Killer,” no subject has gotten more attention than the rise of generative artificial intelligence in business circles. It’s too early to comment on the depth of discourse about Threads. But at this point, it must be said that the commentary and back-and-forth conversations about generative AI have been sorely limited to cost-cutting.
I have nothing against productivity. Anything that can save me and my advisory clients time and staffing bloat is an obvious winner.
But reducing a few hours and hands in a given week, month, quarter is certainly not going to differentiate a brand or platform in the marketplace. It’s not going to produce greater creativity. It’s not going to attract greater investment, forge stronger partnerships, or attract more customers. It’s not going to lead to growth.
Yes, there’s been a lot of talk about the ways generative AI is “unlocking and democratizing creativity.” But for the most part, the current discussions about the potential of generative AI are too devoid of how these tools can generate revenue right now.
The Next Word
It’s not that I’m expecting anyone to have all the answers about the specific ways generative AI can usher in a renaissance of revenue gains. These tools are still in their infancy. It’s going to take a while to figure out how businesses can adopt generative AI into their production arsenal. It’s a hallmark of any hype cycle that anything involving automation or even widening a creative canvas starts with cost efficiencies.
There are simply more interesting conversations to be had about the actionable changes businesses can focus their attention on these subjects.
According to a recent report by Bloomberg Intelligence, the generative AI market is projected to experience explosive growth, reaching a remarkable valuation of $1.3 trillion within the next decade. This substantial surge comes as the market expands from $40 billion in 2022.
BI's research indicates a robust compound annual growth rate (CAGR) of 42% for generative AI. Initially, the growth will be fueled by training infrastructure (it’s a good time to be an AI prompt coach), followed by a gradual shift towards “inference devices” that input information into large language models (LLMs).
Moreover, the rising demand for generative AI products is expected to generate approximately $280 billion in new software revenue. This general surge in revenue will primarily result from the popularity of specialized assistants, enhanced infrastructure products, and copilots that accelerate coding.
Key industry players such as Amazon Web Services, Microsoft, Google, and Nvidia are poised to benefit significantly as enterprises increasingly migrate their workloads to the public cloud. But they shouldn’t be the ones benefiting from the lion’s share of revenue growth tied to generative AI.
Changing Weather Patterns
A consumer packaged goods brand could use generative AI to help launch a new food or beverage based on analyzing historical information on what's appealed to certain audiences. There is a clear cost-cutting advantage here, too. Brands wouldn’t have to rely on the laborious process of creating a sample product line and testing it in a designated market. The AI engine could help generate the idea and provide an initial analysis of success.
Then there’s looking minutely at product strategies tied to a variety of weather changes.
Machine learning tools have long had the ability to game plan all kinds of market conditions and match them to shopping patterns. Predictive, hyperlocal product analysis has remained in the hands of a small number of players for years. But with generative AI, all marketing and product teams should have a much greater access to this technology in-house to inform decisions — and the creative output — tied to how products are best positioned, advertised, and are sold in real-time.
It still might take consumer-facing companies to make the leap fully. At the moment, I expect B2B companies to make the initial strides into generative AI. Cybersecurity is one area I’m looking at for these brands. There are ample opportunities for companies to use AI to better detect where breaches or threats to their systems and data might occur and to address the problem with much greater effectiveness and immediacy.
Advances In Healthcare and Education
Perhaps the greatest opportunity at the moment in how generative AI can boost revenues is in healthcare. From doctor’s offices to hospital administration to even insurance companies, the need for “digital transformation” has been lagging versus other industries. And nowhere is the need for the speed and analysis of generative AI more necessary than in the varied and interlocking parts of America’s healthcare system.
AI can automate administrative tasks, like pre-authorizing insurance, following-up on unpaid bills, and maintaining records. Easing the workload of overburdened healthcare professionals would be a massive boon to the industry.
AI’s ability to process big data sets can also lead to consolidating patient insights that can lead healthcare providers to uncover patterns where services are lacking.
There’s a spatial computing role here as well — another area ripe for driving revenues to new heights.
Wearable healthcare technology also uses AI to better serve patients. Software that uses AI, like FitBits and smartwatches, have been embraced not as luxury items or fashionable accessories, but as tools for giving people more insight into managing their own health issues. The data these devices gather can alert users and their healthcare providers on potential physical problems.
It’s clearly good for businesses that are even just remotely related to healthcare. But in this case, it’s not just about saving providers and companies money from lawsuits, it’s about saving lives. And that means better living for businesses and people across the board.
Now take education.
The applications are almost too numerous to mention. From translation to language lessons, generative AI can inspire everyone from students to tourists to executives to speak like a local anywhere in the world.
Consider the worries investors had that generative AI would decimate companies like language lesson app Duolingo — why pay for a single platform to teach you Italian when ChatGPT will do it for free? Proving doubters wrong, the company launched Duolingo Max, which incorporated GPT-4 technology to add two new features to Duolingo Super.
Those features explain wrong answers to users and offer a role-playing feature that allows users to practice conversations in different scenarios. It’s not something that most users would turn to OpenAI for. But a company with insights into anticipating what its users need, as opposed to just waiting for them to ask, is one that will see higher sales figures.
As students, teachers, and administrators continue to struggle post-pandemic, the introduction of generative AI could open the school doors to a wave of new services that will add rather than subject from the learning experience. Whether it’s tutoring or tracking classroom success and failures and prescribing custom fixes to students and instructors in need, there’s a lot to learn from the ways generative AI can truly reform education while delivering results that lift finances and test scores.
These are still the learning days for generative AI. But the lesson plan for all observers needs to focus on the new revenue that can be produced and all the other value that it can provide.
It's intriguing to consider how these advancements can be leveraged in other fields as well. Speaking of which, an area not deeply explored here is the educational sector. There's vast potential for AI to revolutionize how learning and teaching are approached, enhancing both efficiency and engagement. For those interested in this aspect, you might find the exploration of how can AI be used in education (https://productive.fish/blog/ai-in-education/) particularly enlightening, as it delves into the transformative impact AI could have on educational practices and methodologies.