generative AI Archives - Page 2 of 2

In News

Welcome to the final phase of software eating the world. SaaS companies beware, you’re on the menu this time.

June 8, 2023 No Comments

With its coding capabilities, generative AI is making it easier to develop software.
This could disrupt the way software is created, distributed, and used, VCs and startup founders say.
However, the death of the traditional SaaS company still seems a long way off.

While ChatGPT has been wowing the public, behind the scenes investors and technologists are beginning to talk about a deeper disruption to the inner workings of the established software industry.

A new potential framework for software, whose earlier iteration was coined “malleable software” by researcher Philip Tchernavskij, describes a future where generative AI and humans work together to customize tooling and even create entire applications.

This outcome would flip the traditional software industry on its head, calling into question the value of SaaS companies in a world where everyday people can build software themselves.

“No-code was the first step,” said Matt Turck, a partner at venture capital firm FirstMark. “This is the final chapter of software eating the world, where a bunch of people can create enterprise software within the enterprise.”

This would represent quite a reversal for the industry. Software-as-a-Service companies have been the disruptors for a decade, not the disruptees. They have sky-high valuations because investors are betting their subscription revenue will continue steadily rising for many years to come. If generative AI really catches on, though, that future may look very different.

Democratizing tech creation

Venture capitalists and startup founders have been obsessed with the idea of democratizing tech creation for years, as seen by the rise of low-code and no-code startups like Airtable, last valued at $11 billion, and Webflow, which landed a $4 billion price tag last year.

Some technical knowledge was still required to build most software. Now, though, the emergence of generative AI tools like GitHub Copilot has opened up the ability to generate code using just natural language, Ethan Kurzweil, a partner at Bessemer Venture Partners, told Insider.

For Jake Saper, a general partner at Emergence Capital, the use cases that stand to be disrupted first are simple, low-risk tasks and applications in small and midsize businesses. These instances offer the lowest chance of business disruption and require the least cross-company coordination, he said.

Vertical software companies taking existing technologies and making them easier to use in antiquated industries could also be under threat of replacement if their value-add is more around convenience versus actual product differentiation, Fika Ventures senior associate James Shecter said.

Already, technologists have begun to use generative AI tools like Copilot to build simple apps, including a trivia game and a site for discounted Amazon items.

Some later-stage tech startups are trying to get ahead of the curve by sharing the power of creation with their customers. One example can be found in knowledge base startup Guru’s AI writing assistant, which lets customers create their own custom tones of voice using generative AI. This challenges the traditional idea of software as a rigid tool with a fixed set of available actions for users, Guru cofounder and CEO Rick Nucci told Insider.

“We’ve talked about ‘platforms’ in the SaaS world for a long time, the idea that someone can create a set of foundational building blocks that customers can configure and shape to be what they want,” he said. “This is a step change that’s actually happening.”

A new era for software

Some VCs and founders believe that generative AI could not only transform the way we create technology but also the way we interact with it through ultra-personalization.

For instance, new generative AI technology could help startups create user interfaces customized to each person’s exact preferences, Bessemer partner Talia Goldberg said. Already, ChatGPT is showing early signs of this by choosing to provide certain responses in data table format, even when users don’t specifically ask for that, she explained.

In more extreme cases, entire tools could be generated by AI on the fly to replace common actions a user takes, CRV principal Brittany Walker said.

In the long term, VCs like NEA partner Vanessa Larco and investor Elad Gil believe that autonomous AI agents, rather than humans, will be the main entities interacting with software. One potential scenario could be a world where individuals have a primary AI agent that coordinates and manages a number of “micro-agents” capable of doing everything from text messaging to scheduling dinner reservations, Larco told Insider.

These types of connections and interactions — the technical plumbing that currently makes different software programs work together — is the bread and butter business of many SaaS companies. If generative AI models can do this work automatically, what will happen to these SaaS businesses?

A ‘healthy pressure’ for traditional SaaS providers

To be sure, the death of the traditional software company still seems a long way off.

First, the choice between building software yourself or buying from a third party brings with it a substantial opportunity cost.

“I don’t necessarily want to sit on my computer for 10, 12, 15 hours developing this when I can go and find something that’s ready out-of-the-box,” CRV’s Walker said. “The barrier would need to drop very low for a critical mass of people to start creating their own bespoke software.”

Additionally, paying an outside software vendor helps people put the burden of safety, maintenance, and accountability onto a third party, Emergence Capital’s Saper said.

However, even skeptics admit that the threat of generative AI to traditional SaaS will push established software companies to prove their worth.

“It’ll probably be healthy pressure because the ‘build’ decision may be more tempting because it’ll be theoretically easier to do,” Saper said. “It’s going to put pressure on software vendors to really deliver value.”

Feature Image Credit: Bing image creator

By Stephanie Palazzolo

Contact Stephanie Palazzolo using a non-work device on encrypted messaging app Signal (+1 979-599-8091), email ([email protected]), or Twitter DM @steph_palazzolo.

Sourced from INSIDER

In News

Generative AI for Email Personalization: A Hallucination Wrapped in Confusion

May 30, 2023 No Comments

By Chad S. White

The potential for generative AI creating highly personalized emails may be premature, as current AI technology falls short in several areas.

The Gist

Scaling limitations. Generative AI struggles to efficiently create highly personalized emails without significant manual intervention, limiting its practicality at scale.
Traditional superiority. Existing personalization methods and machine learning models are more effective, reliable and risk-averse than current generative AI approaches for email personalization.
Unproven potential. The performance and return on investment of generative AI personalization in emails remain uncertain, with unclear benefits and possibly diminishing novelty.

Let me be blunt: We are so far away from generative AI writing personalized emails in any meaningful way. So very far. Yet, I keep hearing people suggest that generative AI will be writing highly personalized emails to individuals in the not-too-distant future.

I don’t think this is a realistic expectation at any significant scale, with any degree of automation and with a reliable expectation of increased performance — much less a positive return on investment. Let me explain why.

Not at Scale

The best example I’ve seen of generative artificial intelligence (AI) writing a one-to-one email was a meeting request for a salesperson. First, that’s a pretty generic request — and, in the example, amounted to 10 words. Not a big time-saver. Second, the salesperson is chaperoning the AI, and if they don’t like what’s written, they either have to edit it or write a follow up prompt for changes, which further reduces the time-savings. In that example, the degree to which it was personalized was achieved through manual intervention and not automatically driven by the recipient’s past behaviours, demographics or other criteria — which is the traditional definition of personalization.

While generative AI will get much better in the years ahead, that example is a very realistic example of how generative AI would be used in a “blank page” email situation right now, given generative AI’s many faults and limitations, including its propensity to “hallucinate.” That use case appropriately minimizes the risk by (1) asking for a routine request, (2) keeping the text short and (3) having the output monitored and approved by an employee before it’s sent.

Not Automated

To date, I’m not aware of any brands using ChatGPT or any other large language model (LLM) to incorporate personalized content into an email. And there’s a good reason for that: It’s unnecessary.

Traditional methods of personalization are highly effective and are far from fully utilized — due to siloed and unreliable data. And machine learning models for product recommendations and send time optimization are even less utilized. These existing tools are tried and true and offer solid returns with much more control — which is to say, with far less risk — than generative AI.

In fact, when generative AI is ultimately used for personalization, I predict it will be fed content that’s been personalized using traditional methods. Put another way, any personalization done by generative AI will be done on top of traditionally personalized content. That approach would minimize generative AI’s opportunities to introduce inaccuracies, biases, plagiarized material and other problematic content while simultaneously focusing generative AI on the kind of personalization that only it can do.

Just what are these new forms of personalization that only generative AI enables? Here are a few:

Tone. Some subscribers are receptive to more aggressive pitches while others are much less so. And, of course, there’s a whole spectrum of potential tones that could be used. Generative AI could rewrite copy on the fly to better align with what subscribers respond to best.
Vernacular. Language varies in significant ways by geographic region, education level, religious beliefs and other factors. Especially if it’s informed by sales and support correspondence, generative AI could adapt a brand’s messages to match the recipient’s language usage.
Image backgrounds. Generative AI for images can enable brands to create highly personalized images based on the subscriber’s location, industry and more. For example, an outdoors retailer could place a model in any number of national parks depending on the location of the subscriber or knowledge of where they like to hike or camp.

As with all personalization, a big part of the challenge will be securing enough data to make sound decisions. But even if that hurdle is cleared, there’s the question of performance and generating a return on investment.

Unproven Performance

Oracle Marketing Consulting has identified more than 170 segmentation and personalization criteria that can be used in digital marketing campaigns. However, just because you can personalize a message using a particular data point doesn’t mean your subscribers will respond positively to it. Through experience and testing, each brand must discover which factors truly move the needle for them. The same is true for generative AI personalization.

Even if you have the data to try to personalize the vernacular of your emails, for example, it’s currently unclear if this would be a winning approach. It’s likely that at least some subscribers would find this kind of personalization creepy and manipulative.

It’s also likely that some performance boosts likely wouldn’t be sustainable as the novelty wears off. The history of first-name personalization likely provides a good example. A decade and a half ago when it was new, first-name mail merges moved the needle for a while. But it quickly became a hollow gesture and a gimmick because it generally didn’t signal that the email’s body content was any more relevant to subscribers. First-name personalization got brief bumps when brands gained the ability to personalize images with a subscriber’s name and again when they could personalize videos with their name. But subscribers still view these as technological stunts that don’t signal anything meaningful.

Generative AI personalization may have the same effect, drawing attention away from your message and to the technological stunt of personalizing an image with the skyline of the person’s home city, for example. Sure, it will be cool the first time you encounter it, but the novelty will fade quickly because it’s based on relatively easy-to-obtain geographic data that doesn’t speak to the person’s needs or wants. Plus, attention-grabbing tactics often don’t translate into better performance.

Uncertain ROI

Even if generative AI personalization can boost performance, a positive return on investment is unlikely — definitely not at current “per token” price levels. And given email volumes, it’s possible that generative AI personalization may never make financial sense except for highly targeted campaigns.

It’s worth noting that many of our clients have seen lacklustre returns when using predictive subject line writing tools like Phrasee and Persado. These tools have been around for many years and are primarily driven by machine learning models where wording recommendations are based on the historical performance of a brand’s subject lines and other copy. If models that are trained on historical performance can’t reliably generate a strong ROI, marketers should be deeply sceptical that generative AI tools with no access to performance intelligence can.

Muddied Terminology

A big contributor to this premature idea of generative AI personalization is that some people are using the terms machine learning, AI and generative AI almost interchangeably. While they’re all loosely related, they’re far from the same. They operate using different algorithms and models, have different goals, are built on different datasets and are appropriate for different use cases.

With generative AI and AI in general being so hot right now, brands need to work extra hard to be clear-eyed about what’s possible now and what may perhaps be possible at some point in the future. Just like in the late ’90s when adding “.com” to company names was all the rage, now .ai domains are super popular. Just like then, in some cases these are just aspirational or opportunistic marketing moves.

Generative AI will ultimately be huge, and brands should absolutely experiment with and get comfortable with it. However, we’re in the hype-iest part of the hype cycle, so proceed with caution and ask lots of questions.

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle.

Sourced from CMS Wire

In News

Google Unveils Generative AI Capabilities In Gmail, Docs Ahead Of Reported GPT-4 Release

April 17, 2023 No Comments

By Laurie Sullivan

Google today announced several tools to allow businesses to use generative AI as a way to discover and use corporate data. It also showcased how its productivity suite, Google Workspace, will incorporate AI to help write emails in Gmail and create marketing materials in Google Docs. Other apps include Sheets, and Slides.

The PaLM API, included in the announcement, is a way to build on top of Google’s language models. The API comes with an intuitive tool called MakerSuite that lets developers prototype ideas and, over time, it will have features that prompt engineering, synthetic data generation and custom-model tuning. Select developers can access the PaLM API and MakerSuite in Private Preview.

“We’re now at a pivotal moment in our AI journey,” Thomas Kurian, CEO of Google Cloud wrote in a post. “Breakthroughs in generative AI are fundamentally changing how people interact with technology — and at Google, we’ve been responsibly developing large language models so we can safely bring them to our products.”

The latest Gartner data shows that Google held 13.7% share of the global enterprise email and authoring market in 2021, with $3.4 billion in revenue. The analyst firm also expects the email and authoring market to grow to $27.9 billion in 2023.

AI will provide a platform to start, but Johanna Voolich Wright, Vice President, Product, Google Workspace, wrote in a post that is the technology is no replacement for the ingenuity, creativity, and smarts of real people.”

A list of AI-powered features that will come to Workspace apps in the future include:

Draft, reply, summarize, and prioritize your Gmail
Brainstorm, proofread, write, and rewrite in Docs
Bring your creative vision to life with auto-generated images, audio, and video in Slides
Go from raw data to insights and analysis via auto-completion, formula generation, and contextual categorization in Sheets
Generate new backgrounds and capture notes in Meet
Enable workflows for getting things done in Chat

Google’s news comes in advance of Microsoft’s virtual Future of Work with AI event on Thursday.

Microsoft Germany CTO Andreas Braun said last week the event will likely include the release a multimodal GPT-4, which OpenAI released today, as well as a ChatGPT upgrade for Microsoft 365 applications such as Word and Outlook.

Some media sites have already reported that Microsoft GPT-4 will be “multimodal” to allow AI to translate a user’s text into images, music, and video. A call canter, for example, could use the AI program to automatically convert phone conversations between employees and customers into text, according to one report.