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Zillow, Redfin aim to use OpenAI ChatGPT-4 to find your next house

Zillow, Redfin aim to use OpenAI ChatGPT-4 to find your next house

OpenAI's ChatGPT-4 may be coming to a real estate traction near you as Zillow and Redfin start testing the generative AI technology in their applications.

The two moves highlight how companies are integrating ChatGPT-4 into their customer experience roadmaps. Zillow and Redfin customers have to join OpenAI's ChatGPT plugins waitlist to try out the new tools.

In Zillow's shareholder letter that came with the company's first quarter earnings, the company said:

“The past few months have marked a tectonic shift in the broader technology landscape with the introduction of OpenAI’s ChatGPT-4. The arrival of conversational generative artificial intelligence (AI) may be a platform shift on par with the introduction of the graphical user interface or the touch interface on the first smartphones. “

Zillow noted that it has been using AI, machine learning, computer vision and data science since 2006. The company's well-known Zestimate for properties uses machine learning algorithms to crunch data. In February, Zillow outlined its Neural Zestimate, which is based on deep learning to reflect market trends nationally.

Generative AI guide: ChatGPT: Hype or the Future of Customer Experience

The company launched its alpha-version plugin for Zillow real estate searches with ChatGPT. According to Zillow, the alpha-version plugin is "a small sandbox in which we will learn and iterate rapidly."

Redfin also announced that its ChatGPT plugin is now available. Redfin said the ChatGPT plugin will enable customers to describe ideal neighborhoods to find listings to suit their needs. The results will drive Redfin users to listing pages that meet their criteria.

Both Zillow and Redfin are navigating a volatile housing market amid inflation and increasing interest rates. The bet for the real estate rivals is that they can capture more demand by investing through the downturn.

Here's how Zillow sees the ChatGPT integration working. 

And here's Redfin's approach. 

 

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Tech News, Executive Board Experience | ConstellationTV Episode 56

Tech News, Executive Board Experience | ConstellationTV Episode 56

Episode 56 features Constellation analysts Liz Miller & Holger Mueller sharing the latest #tech news, Holger recaps IBM’s 2023 Hybrid #Cloud virtual event & Liz interviews an #AXS2023 panel about #CXOs at the board level (feat. Cindy Zhou and Helena Verellen).

On ConstellationTV <iframe width="560" height="315" src="https://www.youtube.com/embed/cRqePOXOHzw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

What is generative AI? Definitions, use cases and the future of work

What is generative AI? Definitions, use cases and the future of work

Generative artificial intelligence (AI) is best known for providing answers quickly and creating AI art on the fly, but business use cases abound. Here's a primer on generative AI and its application.

What is generative AI?

Generative AI is a type of artificial intelligence that can produce content such as text, imagery, audio and data based on what it has learned from a massive training set of data. Generative AI has reached a tipping point as technologies such as OpenAI's ChatGPT and DALL-E have become popular.

In addition, technology vendors are racing to include generative AI into products and services. Businesses are also exploring how to integrate generative AI into multiple use cases.

Generative AI takes inputs from training data and produces similar outputs with a unique spin. As a result, generative AI can seem creative. More specifically, generative AI relies on a few different models.

Here’s a look:

  • Generative Adversarial Networks (GANs), which have two neural networks. One is a generator and the other is a discriminator. The two networks compete as the generator creates new data instances and the discriminator values the quality. The generator improves its output based on feedback from the discriminator, which aims to distinguish between generated data and the real training data. You could think of a GAN as a model that somewhat replicates the writer and editor relationship. 
  • Variational Autoencoders (VAEs), which introduces a probabilistic component to create diverse and realistic outputs.
  • Transformer models, which include GPT (Generative Pre-trained Transformer). Transformer models are a type of deep learning architecture used for natural language processing tasks. These models generate text with context based on patterns from training data. 

Github, which offers Github CoPilot generative AI, has more on the various models as does Nvidia.Generative AI models have limitations including the need to compute at scale and outputs that are only as good as the quality of training data. Nevertheless, generative AI is showing it can create new content such as marketing content, social media posts, scripts and books to name a few. Beyond content, generative AI can create new data to train other AI systems, compress data by removing redundant information and create new data as well as programming code. 

Where is generative AI being used?

The bigger question is where generative AI isn't being used. Technology companies are moving quickly to integrate generative AI into productivity applications. For instance, Microsoft is integrating ChatGPT throughout its applications, Salesforce is doing the same and Slack has plans to use generative AI and consumer apps from the likes of Redfin and Zillow are doing the same. When you consider search engines such as Microsoft's Bing and Google have generative AI plans it's likely that most of the software you touch will have ChatGPT or a similar technology embedded. 

Simply put, technology vendors are embedding generative AI and related tools everywhere. However, enterprise buyers are wary of AI and its implications for compliance, first party data and security even as boardrooms push for rapid adoption.

MARKET OVERVIEW: Analytics and Business Intelligence Evolve for Cloud, Embedding, and Generative AI

The list of companies leveraging generative AI is expanding:

How is generative AI different than AI?

Generative AI creates new content, chat responses, designs, images and programming code. Traditional AI has been used for detecting patterns, making decisions, surfacing and classifying data and detecting anomalies to produce a simple result.

Machine learning is a type of artificial intelligence. Machine learning is used to learn from data patterns without human support. Given the scale of data, machine learning enables the models used for AI.

Generative AI guide: ChatGPT: Hype or the Future of Customer Experience

What's a Large Language Model (LLM)?

A LLM is a machine learning model that's trained on text data from multiple sources at scale. LLMs can complete natural language processing tasks and answer questions in a conversational way. Vendors are creating proprietary LLMs as well as tuning for specific use cases and behaviors.

LLMs are trained with books, articles, code and forms of text. This training data is then used to generate text, translate languages and answer questions via natural language processing (NLP). While LLMs are still being developed, CXOs have noted that they can be used for code generation, technical document creation, marketing and data analysis. 

How will generative AI impact work?

For now, generative AI is being seen as a grand experiment rolling out in real time. However, as numerous companies--Microsoft, Google, Salesforce to name a few--look to embed generative AI in productivity tools the technology's reach will be broad.

In a research note, Constellation Research analyst Dion Hinchcliffe said the impact of generative AI will advance industry use cases as well as work in general.

He said:

"The broadest and most impactful area of AI will be in general purpose capabilities that quickly enable the average professional to get their work done better and faster. In short, helping knowledge workers work more effectively to achieve meaningful outcomes to the business. It's in this horizontal domain that generative AI has dramatically raised the stakes in the last six months."

Companies are likely to put resources behind creating generative AI models, algorithms and tools for competitive advantage. CXOs should spend time exploring OpenAI's ChatGPT 4 subscription service as well as Google's Bard to find use cases.

What are the use cases for a generative AI model?

Constellation Research CEO Ray Wang recently outlined five emerging use cases. They are:

  • Marketing. Diffusion models will dynamically generate content, provide translation capability, and run A/B and experimentation tests for user experiences. Personalization models will gain greater context, enabling hyper targeting for campaigns, ad networks, and polling with ChatGPT.
  • Sales. Sales specific tasks such as pipeline reviews, scheduling meetings, install base analysis, and forecasting will move from manual to automated.
  • Service. Crawlers inside one’s internal systems can scan knowledge bases, augment case history, and hasten issue resolution. The AI can create new case tickets, augment missing information, and predict customer satisfaction.
  • Commerce. Speed of product catalog creation will improve as diffusion models will take prompts from regulatory requirements enabling faster global rollouts of new products and services content.
  • Customer success. Generative AI will identify accounts with low adoption and automatically identify at risk customers based on their level of interaction to increase the frequency of engagement.

There will be other personal use cases too. For starters, generative AI tools can be entertaining. They can also write thank you letters, email responses and data profiles.

How are companies using generative AI?

In earnings conference calls, executives typically get a question or two about generative AI. The answers fall into a few key categories.

  • Technology vendors are integrating generative AI rapidly. Whether it's ServiceNow, SAP, C3 AI or Google there's a generative AI story that revolves around integration with their respective platforms. If these tech vendors hold their timelines, you'll be using generative AI indirectly through your personal and business productivity apps.
  • Broad industry usage. Generative AI is turning up in financial services, insurance, healthcare, hospitality, fast food and a bunch of industries. CEOs are watching generative AI closely, pondering integration scenarios, new experiences and compliance issues. These CEOs are very interested in generative AI but do want more transparency into the models.

 

What are the benefits of generative AI?

Generative AI is likely to have a bevy of benefits including automating manual tasks, augmented writing, increased productivity and summarizing information and data.

The technology can also be used to explore new markets, enhance products, personalize experiences, create new knowledge, educate, boost decision making, gather information and optimize processes.

These benefits may become more evident as technology vendors embed generative AI into their applications. Amazon CEO Andy Jassy said on the company's first quarter earnings conference call:

"I think if you look at what’s happened over the last 9 months or so is that these Large Language Models and generative AI capabilities, they’ve been around for a while, but frankly, the models were not that compelling before about 6, 9 months ago. And they have gotten so much bigger and so much better, much more quickly that it really presents a remarkable opportunity to transform virtually every customer experience that exists."

What are the risks of generative AI?

There are also risks to balance out the benefits. For starters, generative AI may replace human workers to some extent. Workers will also have to upskill and reskill due to automation and generative AI, according to Coursera. Other risks include:

  • Data biases. Generative AI algorithms can only be as good as the data set it is being trained on. If generative AI is being trained on a flawed model it'll only scale mistakes.
  • Transparency. Generative AI models are complex, and it will be hard for businesses and consumers to understand how an answer was generated. This problem will become more important as various generative AI technologies and algorithms are integrated. 
  • Ethics. Generative AI applications are trained by data provided by humans. There's the potential to scale unethical behavior and bias.
  • Business models. Sourcing of material has been abstracted in more popular generative AI technologies. In other words, it's unclear how intellectual property owners will get paid.
  • Black box thinking. Humans will still need to offer expertise for decision making.
  • Model sprawl. It's increasingly clear that enterprises will use multiple generative AI technologies. At some point (probably soon), these early adopters will have to wrangle these tools and make them work together.
  • Security. Enterprises are concerned about sharing first party data with LLMs. Speaking at Domino Data Lab's Rev 4 conference in New York City, Jan Zirnstein, Director of Data Science at Honeywell Connected Enterprise, said the company has been looking at generative AI use cases but questions remain. "Generative AI has tipped the public perception of what AI is, but tipped it a little too far," said Zirnstein. "There's nothing in the actual training model and architecture that's tied to truth and factual correctness. We're looking at use cases tied to where factualness isn't imperative like saving time on the creative side. There are also use cases on the summarization side."
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AMD holds line on data center revenue in Q1

AMD holds line on data center revenue in Q1

AMD's data center business held up well in the first quarter as the company topped estimates a week after Intel reported its results.

The company reported a first quarter net loss of $139 million, or 9 cents a share, on revenue of $5.4 billion, down 9% from a year ago. Non-GAAP earnings for the first quarter were 60 cents a share.

Wall Street was expecting AMD to report a first quarter non-GAAP profit of 56 cents a share on revenue of $5.31 billion.

AMD CEO Lisa Su said AMD rounded out its AI roadmap and sees "significant growth opportunities as we successfully deliver our roadmaps, execute our strategic data center and embedded priorities and accelerate adoption of our AI portfolio."

Like most chip makers, AMD sees opportunities in providing infrastructure to power the compute needed to train AI models. It’s a race that includes Intel and Nvidia as well as others.

AMD is gaining cloud service provider traction with its AMD EPYC processors.

Oracle’s Data Platform Gets an EPYC™ Boost

It's also notable that AMD's Data Center and Embedded units represented more than half of the company’s revenue. AMD expects sequential growth for those units in the second quarter.

However, AMD's PC business was thumped in the first quarter with sales falling 65%. Here's the breakdown:

AMD said it is expecting second quarter revenue of $5.3 billion, give or take $300 million. Wall Street was expecting second quarter sales of $5.5 billion. 

Su said on an earnings conference call:

"We have significant growth opportunities ahead based on successfully delivering our road maps and executing our strategic data center and embedded priorities, led by accelerating adoption of our AI products. We are in the very early stages of the AI computing era, and the rate of adoption and growth is faster than any other technology in recent history.

And as the recent interest in generative AI highlights, bringing the benefits of large language models and other AI capabilities to cloud, edge and endpoints require significant increases in compute performance. AMD is very well positioned to capitalize on this increased demand for compute."

A week ago, Intel reported a first quarter loss of 66 cents a share on revenue of $11.7 billion, down 36% from a year ago. The chip giant reported a non-GAAP first quarter loss of 4 cents a share.

Intel also forecasted second quarter revenue of $11.5 billion to $12.5 billion with a loss of 62 cents a share, or a loss of 4 cents a share on a non-GAAP basis.

In the first quarter, Intel's Data Center & AI unit had revenue of $3.7 billion, down 39% from a year ago. Client Computing Group was down 38% in the first quarter compared to a year ago.

CEO Pat Gelsinger said on Intel's first quarter earnings call:

“As the industry continues to navigate through multiple global challenges and headwinds, we remain cautious on the macro outlook even as we expect some modest recovery in the second half. We are seeing increasing stability in the PC market with inventory corrections largely proceeding as we had expected. However, the server and networking markets have yet to reach their bottoms, as cloud and enterprise remain weak. As a result, our Q2 revenue guide embeds continued inventory corrections in our core markets, and a range of normal seasonal to better-than-seasonal growth off depressed Q1 revenue levels.”

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Rootstock launches embedded analytics for manufacturing

Rootstock launches embedded analytics for manufacturing

Rootstock, a cloud ERP provider for the manufacturing industry, launched Enterprise Insights, a set of analytics tools built into its Spring '23 release.

The company, which has built its ERP system on top of the Salesforce platform, shares the same data model as Salesforce and connects to Salesforce Commerce Cloud, Manufacturing Cloud and Service Cloud.

Rootstock's Enterprise Insights highlights how software providers are increasingly embedding analytics tools into their applications. For some customers, these embedded analytics modules may make more sense than investing in a new platform.

Constellation Research analyst Doug Henschen said in a blog post outlining a research report on embedded analytics:

“There are other considerations that break one-size-fits-all, top-right analysis. For example, Constellation believes that organizations that are deeply invested in an enterprise applications portfolio from the likes of Infor, Microsoft, Oracle, Salesforce, SAP or Zoho should naturally have Infor Birst, Microsoft Power BI, Oracle Analytics Cloud, Salesforce CRM Analytics, SAP Analytics Cloud or Zoho Analytics, respectively, on their shortlist. The availability of pre-built analytic content, built-in data integration, and data model consistency tied to these applications makes these offerings very compelling.”

Rootstock's argument is that it's better for manufacturers to leverage dashboards that delve into their already-integrated data stores.

Here's a look at the five Enterprise Insights modules for manufacturers in its latest release.

  • Sales Analytics, which can track trends to improve revenue generation, set goals, improve processes and break down profitability in regions and product.
  • Spend Analytics, which allows manufacturers to improve procurement processes to cut costs. The module compiles data across its Procure-to-Pay process.
  • Inventory Analytics, which tracks products through the purchase of raw materials to logistics to final sales.
  • Manufacturing Analytics, which is aimed at shop floor performance and continuous improvement.
  • Financial Analytics, which monitors revenue and costs.
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An Update on Enterprise Low-Code and AI: Appian World 2023

An Update on Enterprise Low-Code and AI: Appian World 2023

I'm in San Diego this week for Appian World, the annual confab of the well-known enterprise low-code and citizen development platform provider. Low-code has come a long way from the early days, when drag-and-drop app builders were just getting capable enough to use by the average IT staff member or business analyst. Now data fabrics, process mining, industry templates, and artificial intelligence are standard fare in the large enterprise low-code suites.

For its part, Appian has helped the industry revolutionize the way businesses create and deploy applications, greatly lowering the cost of development and accelerating time to value. Since its inception in 1999 by co-founders Matt Calkins, Michael Beckley, Marc Wilson, and Robert Kramer, the company has sought to provide organizations with an effective and user-friendly platform that streamlines app development, allowing for quicker innovation and improved efficiency. Today, Appian is known for its transformative impact on the business applications and process execution landscape, with an extensive portfolio of clients ranging from government agencies to Fortune 500 companies.

Over the years, Appian has reached many key milestones that have solidified its position as an industry frontrunner. In 2007, the company unveiled the first version of its low-code platform, which would ultimately become the foundation for its modern product offerings. Appian's growth was further accelerated by venture funding in 2008, which enabled them to scale up and serve an even larger market. The company has continued its upward trajectory, going public in 2017 with a successful IPO on NASDAQ. The past two decades have seen Appian reguarly push the boundaries of low-code development. With its long heritage and industry staying-power, the company is poised to continue shaping the future of application development for years to come.

Live Blog of Appian World 2023: Day 2 (First Full Day)

What follows is the the unfolding events of Appian World today in San Diego:

8:53am: I'm in the front row at the general session keynote. We're waiting for co-founder and CEO Matt Calkins and team to come on stage.

9:09am: A rousing video kickoff is showing that was co-generated with AI, about low code and its relation to artificial intelligence. It's a decent overview that helps set the stage for the morning.

9:12am: Now April DelFavero is starting off the main keynote session: "This event presents an incredible opportunity to bring together our global community of digital transformation leaders Appian delivers end to end process automation and is purpose built to help you solve your most complex processes."

9:13am: Founder and CEO Matt Calkins is now onstage making the case that Appian's message to the market is simpler than ever before: "Anything you need to do with a process you can do on our platform. It's a complete, end-to-end  software layer for your processes."

9:19am: Now talking about data fabric and how it unleashes enterprise data: "We're respecting the the architecture of your enterprise. We're leaving the data where it is. We're treating it all as a unified entity and allowing you to have access to it as if it was one with vast data source that is exceptionally valuable." Noting that traditional approaches are very burdensome. 

9:21am: Cites specifically productivity gains they're seeing with Appian customers; "I can tell you that some Appian customers have seen a 50% leap in productivity and output per customer hour."

9:22am: Now talking about artificial intelligence, which is the hottest topic in the industry right now. "The other thing I primarily want to talk about in this speech is AI. Appian uses AI throughout its architecture. We use it for design of a new process and we use it for execution of that process. You really can't escape artificial intelligence. If you're using AI, it's everywhere." Teases an annnouncement.

9:25am: "It's really astonishing what AI can do. It has peaks, and it has valleys and the natural question we might ask is, how can we get the upside of AI without suffering the downside? That would be a smart question for all of us to be asking in 2023. And luckily, there's an easy answer. The answer is that people have to be involved." Although I'd argue people can't be involved in the details. You cannot scale AI if people are involved in every step. But Matt has a lot to say on AI, so let's follow the argument.

9:30am: Matt is now diving deep on AI, making a distinction between public AI and private AI. Makes a major case for private AI as key to competitive advantage. "Our intention is to be sure that we're supporting your vision, your freedom, your freedom to choose the best of breed products that you want your freedom to keep data where you put it, your freedom to keep your information and train your own AI algorithm instead of sharing it with your competitors and with your vendors." Making a strong case for not over-exposing data and AI models to public scrutiny, although there is going also be a broad industry/government push for just that at some point. But that particular but necessary privacy/regulatory/ethical discussion is for another day for now for most vendors.

Private AI vs. Public AI

9:35am: Now here's probably the core of Matt's vision for AI for Appian and for enterprise low code in general: "Private AI means every organization every major organization in the world has its own AI algorithm. It's based on some publicly available models of open source AI, but then you train it yourself. You train it internally based on your own information. And it becomes your AI purpose built focused on the things you need, trained on the data you have the forms you have the behaviors you want.

Key point (my emphasis): The data never escapes your control. and all of the models stay unique and you preserve the totality of the benefit for your own organization as you should. I believe that custom AI is going to become like custom applications. Every organization is going to want it for themselves. and we'll end up with a situation where it's, it's second nature to expect that everybody's going to cultivate their own AI algorithms based on popularly available AI technology. This is the future that I predict."

9:37am: Now talking about the maturity of low code and the scale of solutions that can be developed for it. "Solutions have reached a high stage of maturity, such that it would make sense for everyone in this room to be thinking about using it. Low code apps are very broadly used. They do an exceptionally high volume of work.

Such as a procurement system, which is a government acquisitions management system that's doing 10s of billions of dollars of acquisitions every year at this point. It's very mature, a very high test solution. A solution has a lot of built in expertise and so you can benefit from all the expertise we have stocked our solutions with. It has the latest features, of course, like the Data Fabric, the automation solutions are up to the moment and they stay up to the moment because we keep updating them and that makes it easier for these new offerings to stay fresh with your solution. It has integrations already built-in."

9:42am: Now announcing Process HQ -- see my video of him providing a detailed explanation of the forthcoming solution -- Matt explains what it is and how it works: "What we've got here is low code process mining using our data fabric to connect to data across the enterprise. This is very different already from the typical way process mining would work. Generally it's a hard hard struggle at the beginning where you seek out data, parse it and and try to integrate many different sources. " 

Process HQ Announced at Appian Today

9:45am: "Because we've got this real time awareness of data why not make process process mining a real time phenomenon? Instead of one survey that gives you one set of results after a quarter of work? How about just every minute you can log in and see how your efficiency is going?" Basically, they are making process mining a continuous, real-time workflow that enables organizations to mine and optimize their organizations as a seamless, ongoing endeavor.

9:47am: Cites early figures from a new study on Appian's low code benefits: "90% reduction in development time. Less than 6 months to pay back the initial investment in Appian. 95% acceleration in operational processes." I've long cited the potential of low code to dramatically improve both development time and operational processes, now we are indeed seeing the hard data that shows this is the case.

9:50am: Matt wraps his opening keynote.

Customer Story: Food and Drug Administration (FDA)

Now Vid Desai, the Chief Information Officer (CIO) of the FDA, is up on stage at Appian World 2023 talking about low-code at that vital U.S. government agency.

11:03am: Vid is exploring the huge remit of the FDA and all the aspects of the food and drug supply chain. "We're part we're a regulatory agency. That's part of the Health and Human Services. Part of our mission is to ensure that the products that we regulate, are safe and effective. And we regulate a lot of products."

Vid Desai, CIO of the FDA

11:06am: Vid talks about the extremely rapid changes happening in how the FDA works: "The science is changing. It used to be the case that we relied a lot on chemistry. But now increasingly, we're in the world of genomics and genomics datasets. And we're rapidly moving away from looking at products and developing products based on PDF documents submitted to us. Now we are analyzing very complex large genomic datasets. The technology is changing. All of you are familiar with the IoT, AI, cloud. There seems to be a new technology almost every day that's obviously changing the world around us." Engaged in a multi-year modernization process to transition to this future that is far more data-infused and AI-powered.

11:08am: "But anyone that tells you that they have a cookbook for modernization is essentially lying to you" on the enormous challenges of changing a large organization like the FDA.

11:14am: Now Desai gets down to the real value-prop and motivation for low-code: "Frankly, IT so far has been a bit of a bottleneck."

The Power of Citizen Development from FDA CIO, Vid Desai

11:16am: Desai also makes of the best statements on the enormous potential of citizen development that I've heard from a major CIO: "Enabling citizen developers I believe is a very powerful thing. Now IT can become an enabler. We empower the business folks to go develop their own solutions. They understand their problems the best, they are in the best position to solve them.

So we equip them with the right tools and the right governance structures around that. And they can create solutions, which frankly from an IT solutioning perspective is exponentially greater than what we're able to do right. So I think this whole concept of citizen development.... some people are scared of it... but I think it is a very powerful movement is going to really increase the effectiveness of IT."

11:20am: Talks about the high stakes of changing and the traditional processes of procurement which can take years to see if they've even gotten anywhere. Seeks to move to more real-time use of data, process mining, and process optimization: "You can develop a whole new solution and kind of cut over our business. We can't shut down 1/5 of the economy while we cut over to a new environment. We've got to fix our processes while they're in use. And so the process mining way is a very important way that we're now creating our new solutions." Says favorable things about Appian's ability to deliver on this: "Instead [of lengthy traditional process modernization], we think the process mining approach that Appian offers is really powerful."

BackstoryMission-Driven Innovation: Inspections and Digital Services at the FDA

Other News at Appian World 2023

Also announced today was the Appian AI Skill Designer. It integrates generative AI into the Appian platform to reduce the need for specialized data science and Python coding skills. AI Skill Designer can create custom AI machine learning models on an enterprise's private data. This enables customers to create unique AI solutions that are tailored for their business without the need for hard-to-find data science skills. Three AI skills are available at launch, including e-mail classification, document classification, and document extraction.

OpenAI/ChatGPT Skill for Appian is Now Available

This provides the private AI capabilities that Matt talked about in this keynote. However, AI Skill Designer is also built on an extensible architecture, so others can extend it and provide new skills from the Appian App Marketplace. Appian also has created the first AI Skill extension, an OpenAI/ChatGPT Generative AI Skill available now available on the Appian AppMarket, which provides public AI capabilities in apps developed with the low-code platform. Appian is thus delivering on both sides of the public/private AI divide that Matt talked about so passionately in his keynote.

Separately, Appian also announced a new "Insight to Action” Process Mining Program. The new service provides process mining preparation, analysis, and service hours to implement process improvements, all for a one-time fixed fee. While process mining can find bottlenecks, inefficiencies, and insights into how to improve the business, the results are often famously difficult to interpret.  This service is intended to reduce the disconnect between process mining and the downstream automation technologies needed to implement the needed improvements.

Fixed free is a brave and in-demand model, though IT buyers have been wary of them in the past, since they often require more actual budget to reach completion. Appian is apparently aware of this and reading between the lines, it appears that not all process mining scenarios are candidates for the program. However, in an economically constrained environment in 2023, I predict there is appetite for access to fixed price services like this.

The official Appian Insight to Action service offering can be found here.

Live Blog at Appian World 2023: Day 3

9:07am: April DelFavero introduces Appian CTO Michael Beckley to talk about product vision.

9:11am: Michael Beckey starts off the product vision keynote, doing the usual disclaimers. Talks about a post on a "from Reddit from the Appian forum from just a few days ago, where Appian developers were expressing some angst and anxiety about whether or not AI was going to come into their jobs."

Appian CTO Mike Beckley

9:14am: Dismisses the concern however, noting that "this was true with Model Driven Development with drag and drop design. And yet the reality is when we make it possible for you to do so much more with less than the reality is you're more valuable, and you need more developers. So that's been true throughout Appian's history. There are now 10 times more developers than there were before we introduced visual design." I talked to Appian folks today, about the fact their platform unleashes the 24x more non-developers out there. Despite this, professional devs are still in high demand.

9:18am: Talks about the declarative language, SAIL (self-assembling interface language), that makes the Appian platform work so simply. Talks about that it must be possible to make it easy to teach AI. And that it works well with a consistent data fabric: "Similarly with the data fabric, it's a messy world out there you have hundreds or 1000s of API's. So they all have different syntax. They all have different functions. And it can be confusing to navigate, which is why we built the Data Fabric product common vocabulary to make it simple for you to interact with. And again, it's simple for users and developers. It should be simple to teach it to an AI."

9:22am: Now Mike explores how they are using large language models (LLMs) to make querying the data fabric far more contextual and effective. How Appian has "already been taking a large stride towards making it self service. Making it simple for people to build instant analytic solutions" using AI. Then presents a full demo of how they have gone from predictive suggestion of analytics to full free-form natural language queries, that then result in working analytics dashboards.

9:24am: "So we can interact with what the AI generated and get to the exact result you as a developer want or as a business user. Doesn't doesn't matter at this point. Now you're creating a really sophisticated report in seconds, not minutes, not hours and not even necessarily waiting for whatever that backlog is. What we see here is the potential for collaboration between AI and the developer community and the business users."

Video of Mike's powerful demo of using language language models to support querying data can be viewed here.

Appian CTO Mike Beckley Demonstrates Language Language Models Creating Contextual Dashboards

9:26am: "So to sum up what we've seen here so far, that's the question is AI really a threat to your jobs? Is it really a threat to human innovation, productivity, and every time AI makes that next leap?" Makes the analogy to chess, that computers can now play well, but notes the real world is messier and "is a lot more confusing and complicated than a test or a game."

9:28am: Mike sums up his thoughts on the impact of AI and its use in platforms like Appian to eliminate drudgery and unleash human potential: "The collaboration between AI and and that ability for you as creators to delegate the mundane and repetitive work documentation of your applications to an AI worker, is going to make it much more possible for you to be more human, and to be more creative." My analysis is that this is largely correct, and AI will likely have some task impact, but not large employment impact for now. Even longer term, it will only create more jobs as it creates new possibilities that create broader demand.

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Uber aims to press its 'data advantage' for AI, model training

Uber aims to press its 'data advantage' for AI, model training

Uber CEO Dara Khosrowshahi said the company's scale is providing a "significant data advantage" that will enable it to "develop cutting-edge AI that can be trained across larger data sets."

Khosrowshahi's comments, which were delivered in prepared remarks about Uber's first quarter earnings, highlight how the company is about mobility and delivery, but its competitive edge is really about data.

He said:

"On the consumer side, we are using an ultra-low latency deep neural network to predict highly accurate ETAs for rides and deliveries. On the earner side, we have developed advanced computer vision algorithms that have allowed us to more reliably and cost-efficiently process documents, resulting in faster onboarding times.

We are still in the early stages of using large data models to power improved user experiences and efficiencies across our platform, with much more to come."

This theme is increasingly common among companies that aren't necessarily known for technology. The upshot: Companies in every industry are producing a data flywheel that can be used for new innovation.

Uber reported better-than-expected first quarter earnings. Uber reported a first quarter net loss of $157 million on revenue of $8.8 billion, up 29% from a year ago. Uber's first quarter loss of 8 cents a share was a penny better than Wall Street estimates. Mobility revenue for the first quarter was $4.33 billion, up 72% from a year ago. Delivery revenue was $3.1 billion, up 23% from a year ago. Uber Freight revenue was $1.4 billion, down 23% from a year ago.

Related: Analytics and Business Intelligence Evolve for Cloud, Embedding, and Generative AI | Time for Software Architecture to Catch Up With the Event-Driven World

Khosrowshahi said Uber is using its platform to scale new businesses including Uber for Business and advertising. Data is also critical for Uber's efficiency efforts. Khosrowshahi said Uber expects to hold headcount flat to down with more "deliberate performance management."

Indeed, Khosrowshahi said that Uber plans to accelerate its path toward GAAP profitability "by optimizing every single line item across our entire P&L—including stock-based compensation, depreciation, amortization, and interest expenses."

This optimization includes process mining as well as automation. Uber has been a reference customer for Celonis and UIPath.

Speaking on a conference call with analysts, Khosrowshahi explained how Uber sees the progression of AI at the company. 

"As far as AI goes, we are looking full stack at AI. I think a lot of people obviously want to talk about the sexy, kind of new consumer applications. I would tell you that I think that the earliest and most significant effect that AI is going to have on our company is going to have to – is actually going to be as it relates to our developer productivity.

Some of the tools that we're seeing in terms of co-pilot are going to allow our devs to, kind of be super-devs and to be able to innovate more, build more faster having co-pilot along with them. And that will essentially leverage and accelerate innovation across the platform. 

I think on the cost side, you can see chatbots powering a lot more experiences as opposed to let's say live agents. I think the quality of those chatbot experiences is going to increase with AI with a voice that can be more human, interactions that can be more complex, etcetera.

And then we will look to surprise and delight. Pick me up at the airport. I'm arriving in American flight 260 on Tuesday. And we will know who you are, where your home is, what kind of cars you like, etcetera, and AI can power those kinds of experiences.

So, it's going to go from productivity to cost to delight." 

Among Uber's first quarter takeaways:

  • Uber's advertising base was up 70% with more than 345,000 businesses signed up.
  • The company will share more product updates at its GO-GET showcase on May 17.
  • Uber plans to hold its first sustainability and electrification event in London during the summer.
  • First quarter mobility active drivers were up 35% from a year ago.
  • Uber is looking to expand its delivery reach and expanding into grocery, convenience and alcohol.
  • The company's Uber Freight business is seeing headwinds amid macro-economic concerns.
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Constellation Research 2023 ESG 50 highlights rise of Chief Sustainability Officers

Constellation Research 2023 ESG 50 highlights rise of Chief Sustainability Officers

Constellation Research's 2023 ESG 50 list has a diversity of titles that highlight how companies are responding to environmental, social and governance (ESG) demands from different angles.

And those demands are increasing. Institutional investors have been pushing companies to detail carbon emissions and climate risks. In addition, a pair of sustainability regulations in Europe are impacting operations or soon will. The EU's Corporate Sustainability Reporting Directive (CSRD) is expected to be incorporated. The CSRD imposes sustainability reporting for all publicly listed companies and all corporations with more than 250 employees and more than €40 million in revenue or €20 million in total assets.

The CSRD will concern 50,000 EU companies, which will have to integrate sustainability reporting into management reports with a mandatory external audit.

Individual countries within the EU are also expected to have sustainability regulations such as the German Supply Chain Due Diligence Act (GSCA), which took effect Jan. 1, 2023, for companies that employ more than 3,000 people. Companies that employ more than 1,000 people will need to comply from Jan. 1, 2024.

Given that backdrop--along with companies signing up for the Science Based Targets Initiative (SBTi)--it's no surprise that more than half of the Constellation Research 2023 ESG 50 list is comprised of titles such as Chief Sustainability Officer (CSO) or EVP/SVP/Director and CSO.

Inflation and the need to become more efficient are also helping sustainability efforts. Companies that are more efficient save money and advance sustainability goals.

CSOs are often one member of a broader committee or team that oversees all of ESG at enterprises such as diversity, equity and inclusion. After CSO titles, corporate citizenship, corporate responsibility, and public/environmental affairs titles are the next most popular.

Doug Henschen, VP and Principal Analyst at Constellation Research, put the ESG 50 together. He said the rise of the CSO highlights the maturity of sustainability efforts.

"I'm particularly excited to see of smattering of titles such as ‘Chief Sustainability and Strategy Officer’ or “Chief Sustainability and Strategic Business Development Officer,’ as this hints at the mature perspective that sustainability is best achieved when it’s a strategic objective from the start rather than something you just report on after the fact. Organizations will increasingly recognize that sustainability, performance improvement and profitability can all go hand in hand when it’s a priority considered in all business decisions."

New C-Suite Innovation & Product-led Growth Future of Work sustainability Chief Sustainability Officer

Coursera: Generative AI will lead to reskilling, upskilling boom

Coursera: Generative AI will lead to reskilling, upskilling boom

Jeff Maggioncalda, CEO of Coursera, said generative AI such as OpenAI's ChatGPT-4 will have a much larger impact on education and employment than automation and other technology shifts.

Speaking on Coursera's first quarter earnings call last week, Maggioncalda said "we believe that AI represents the next major technological disruption that will dramatically change how we work and how we live."

Generative AI is a type of artificial intelligence that can produce content such as text, imagery, audio and data based on what it has learned from a massive training set of data. Generative AI has reached a tipping point as technologies such as OpenAI's ChatGPT and DALL-E have become popular. In addition, multiple technology vendors are racing to integrate generative AI into existing applications.

Generative AI guide: ChatGPT: Hype or the Future of Customer Experience

Indeed, as generative AI revamps the workplace it's also going to create a lot of talent reskilling, said Maggioncalda. He said:

"We believe that generative AI will be integrated into applications, software, and platforms that employees are already familiar with. This integration is happening quickly, and we believe it will increase the importance of digital transformation and talent reskilling and businesses.

And this brings us to our second trend which is skills development. For years, employers have been rapidly digitizing work processes and jobs that are repeatable and predictable. And generative AI has the potential to impact an entirely new class of knowledge workers unleashing a new wave of reskilling and upskilling imperatives."

For Coursera, this disruption can increase its total addressable market. We'll all be students either learning new skills or upskilling to avoid being replaced by an algorithm. Education will have to transform to become more personalized and relevant--perhaps via generative AI. It's worth noting that Chegg recently announced CheggMate, a new AI personalized learning service built with OpenAI's ChatGPT-4.

While ChatGPT may be a long-term tailwind for online education companies and reskilling, there will be short-term turbulence. 

For instance, Chegg reported solid first quarter results, but noted that ChatGPT is hampering new sign-ups. CEO Dan Rosensweig said:

"We believe that generative AI and large language models are going to affect society and business, both positively and negatively. At a faster pace than people are used to. Education is already being impacted. And over time, we believe that this will advantage Chegg.

In the first part of the year, we saw no noticeable impact from ChatGPT on our new account growth, and we were meeting expectations on new sign ups. However, since March, we saw a significant spike in student interest in ChatGPT. We now believe it's having an impact on our new customer growth. Fortunately, we continue to see very strong retention rates, suggesting that those students who already understand the value of Chegg continue to choose us and retain us at high rates. We are also expecting a positive recovery in enrollment trends, which historically would be good news for Chegg. Because it's too early to tell how this will play out. We believe that it's prudent to be more cautious with our forward outlook."

Constellation Research analyst Dion Hinchcliffe recently outlined in a research report how generative AI is already changing work. Governments and universities will have to adapt as well as enterprises.

Maggioncalda said:

"AI will amplify and accelerate the change being felt by individuals, pushing every one of us in every job to keep learning in order to stay relevant."

Coursera's bet is that its focus on universities as well as enterprises will transform learning. Coursera has launched Coursera Coach, a virtual learning partner powered by generative AI for personalized evaluations and feedback on submissions. Coursera is also using AI to generate course content, course structure, reading assignments and glossaries based on an author's input as well as recommend modules.

Internally, Maggioncalda said Coursera is leveraging generative AI to become more efficient in areas like research and development as marketing. He said:

"What I'm hearing from our engineers is that the productivity improvements that you can get as a software coder, especially at more entry-level software coding jobs is considerable. So, I think software coding productivity is going to go way up.

Most of our performance consumer marketing teams have been using (generative AI) to help write articles, write marketing messages, write emails, et cetera. Their productivity and quality has gone up quite a bit."

Generative AI will also be embedded into all the software tools being used by workers. "I would not be surprised that most CEOs are rethinking probably not work structure but certainly productivity expectations for how the company runs and what can be done by talented people in the company," he said.

DisrupTV Episode 200 - Featuring Leah Belsky from Constellation Research on Vimeo.

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Constellation Research Offers a Fresh Take on the Analytics and Business Intelligence Market

Constellation Research Offers a Fresh Take on the Analytics and Business Intelligence Market

Your top choice in analytics and BI should not come down to a one-size-fits-all assessment. Constellation’s 2023 Analytics and BI Market Overview report helps buyers identify a short list that fits their organization.

Analytics and business intelligence (BI) products have long helped companies become more data driven. The market is mature in many respects, but these days analytics and BI platforms are evolving in three important ways:

Cloud deployment. Supporting organizations as they move data and workloads into the cloud or multiple clouds to gain agility.

Embedding. Delivering insights where people work (not just in reports or dashboards) so more users can harness data and insights to drive better outcomes.

AI/ML augmentation. Getting more predictive and automated, taking advantage of ML, natural language (NL), and emerging forms of AI such as generative AI and large language models (LLMs). These features help organizations be more proactive, uncover insights, and eliminate manual tasks.

The Constellation Research 2023 Analytics and BI Market Overview Report, published on May 1, focuses on how well vendors are responding to these trends. Are they leading or lagging? The report also details what vendors offer in terms of more mature and expected functionality, including:

  • Data integration and preparation.
  • Data cataloging, data modeling and data management.
  • Security, access control, and governance
  • Dashboarding and reporting
  • Data storytelling

Unlike other reports that deliver a one-size-fits-all, top-right ranking, Constellation Research takes a different approach focused on helping customers to develop a short list of candidates that are right for their organization. As we note in the report, “no single offering is in the proverbial top-right corner for all customers. Just as there is a huge variety of vehicles—from sedans and SUVs to pickup trucks and convertible coups, all of which have four wheels and get you from point A to point B—there is great variety among the 17 analytics and BI offerings covered in the report. The coverage includes: Amazon QuickSight, Domo, Google Looker, IBM Cognos Analytics, Infor Birst, Microsoft Power BI, MicroStrategy, Oracle Analytics, Qlik Sense, Salesforce CRM Analytics, SAP Analytics Cloud, SAS Visual Analytics, Sisense, Tableau from Salesforce, ThoughtSpot, TIBCO Spotfire, and Zoho Analytics.

Which vendor should be on your short list? Consider that the product that most reports put in a top-right corner is available on only one cloud. Can an analytics and BI system realistically be your enterprise standard if it’s not available on the cloud where all or your data or a majority of your data resides? We think not.

There are other considerations that break one-size-fits-all, top-right analysis. For example, Constellation believes that organizations that are deeply invested in an enterprise applications portfolio from the likes of Infor, Microsoft, Oracle, Salesforce, SAP or Zoho should naturally have Infor Birst, Microsoft Power BI, Oracle Analytics Cloud, Salesforce CRM Analytics, SAP Analytics Cloud or Zoho Analytics, respectively, on their shortlist. The availability of pre-built analytic content, built-in data integration, and data model consistency tied to these applications makes these offerings very compelling.

Budget is another consideration, and organizations that want truly broad deployments will want to consider the low-cost deployment options available with Amazon Quicksight (assuming they’re running mostly on AWS), Microsoft Power BI (assuming they’re running mostly on Azure) or Zoho Analytics (assuming they’re using Zoho Cloud or are willing to self-manage the deployment on another cloud).

In addition to the points made above, the Constellation Research 2023 Analytics and BI Market Overview report is complemented by the publication of two related Constellation Research ShortListsTM based on the market overview research:

  • The Constellation Research 2023 “Multicloud Analytics and Business Intelligence Platforms” ShortList (which updates and replaces the 2022 ShortList on Cloud-Based BI). As the name change suggests, the bar has been raised. Now that every vendor included in the market overview offers a SaaS service on at least one cloud, the bar has been raised. Sure, many organizations still prefer to deploy and manage software themselves, yet every vendor tells us their vendor-managed services are their fastest-growing products. The updated ShortList recognizes eight vendors who offer their analytics and BI platforms on multiple clouds.
  • Our first-ever Constellation Research “Embedded Analytics” ShortList recognizes vendors that are going well beyond traditional embedding approaches aimed strictly at software and SaaS vendors. The nine leaders on this new list are also providing embedding options for third-party productivity and collaboration apps, enterprise apps, custom apps (including low-code/no-code development options), and event architecture/triggers/alerts and, in some cases, workflow capabilities for driving business processes based on analytics.

Figure 1. from the report details trends evolving embedded analytics and shaping our new Embedded Analytics ShortList. 

As for our third ShortList in this category, the most recent “Augmented Business Intelligence and Analytics” ShortList, published in August 2022, did not merit an interim update. Constellation is reviewing multiple generative AI features and functions that are in private preview or in limited public preview at this writing. Constellation’s regularly scheduled ShortList update, due in Q3 2023, will consider generative AI and other augmented advances that are generally available by that time.

To better understand which product might be the best fit for your organization, the market overview provides in-depth analyses of each of the 17 vendors listed, including their presence on our Cloud, Embedded and Augmented Shortlists. Ten of the 17 vendors are on two of these shortlists. One vendor, ThoughtSpot, an upstart innovator pursuing the latest trends, is on all three shortlists, but the report does not put any one vendor in a top-right corner.

As is the case for all 17 products covered in our market overview, customers should read the report and consider the fit with their organization, their deployment footprint, their use cases, and their long-term plans before placing any product on their short list for request-for-proposals and follow-up pilot testing.

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