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HOT TAKE: M&A Continues to Heat Up Around Growth Optimization Technologies

HOT TAKE: M&A Continues to Heat Up Around Growth Optimization Technologies

The pace of M&A seems to be increasing as we move through the summer. One area where we are seeing an interesting flurry of deals is around companies that help foster growth in one form or another. This might be around promotions optimization for B2C, or around further empowering the strategic power of RevOps teams.

RevOps teams are slowly becoming more and more strategic members of the team, in terms of leveraging the data in their operations wheelhouse to analyze and predict how revenue activities might play out for the quarter, year, etc. A slew of startups have formed to aid this transition - aiming to leverage Gen AI and other new technologies to provide low/no code tools for RevOps teams to build and manage sophisticated analytical and predictive models aimed at various system data sets (lead management, CRM, ERP, etc.). Similar in how we saw CMOs become more like CIOs in the past decade plus...RevOps leaders are now required to have even more technical acumen in the age of AI and the expectation of predictive analytics inside the RevOps tech stack.

So, it is not surprising that we are starting to see a wave of M&A to support this shift. We are seeing larger established players add this functionality via tuck-in acquisitions. And a few smaller firms are joining forces to build out a more data rich, predictive platform for RevOps to leverage every day.

The latest of such moves is by startup Forwrd.ai scooping up LoudnClear.ai. Forwrd has been building up its business as an analytics dashboard of sorts that guides RevOps leaders. The addition of LoudnClear brings more conversational AI, allowing users to harness and act on revenue opportunity signals that may stem from multi-channel, synchronous or asynchronous interactions from both inside and outside the usual real of sales interactions.

For example, RevOps teams can use the combined entity to model alerts and workflows based on upsell opportunity signals arising inside a contact center conversation. Since many customer support agents are either not trained nor permitted to act on potential revenue opportunities, uncovering these signals and routing them to the right sales or success team member can make the most of every potential revenue opportunity if configured properly. Again, this is important, as RevOps teams need to be more data-driven, predictive and strategic, but have not traditionally come from technical backgrounds, so the addition of low/no code tools and conversational intelligence is a big evolution for the market.

A few weeks prior we saw RevOps analytics provider Setsail get acquired by Zoominfo. SetSail provides a number of interesting analytics and dashboard tools for RevOps teams to better analyze sales interactions, pipeline and deal flow etc., leveraging data from various social media and other internal systems to better understand how deals track. With Zoominfo being a data provider for pre-sales teams, you can see how the strategic combination empowers both RevOps teams and sellers with more data, recommendations around prioritization of daily actions, outreach cadence and identification of buyer committee members, etc.

The recent M&A in the growth operations area isn't limited to a RevOps focus. Infor recently closed its acquisition of Acumen which fleshes out its growth optimization offerings for CPG companies. (More on Infor's recent industry-focused M&A activities here.) As CPG companies need to be closer to both their distributors, retail partners and downstream customers, Acumen helps gather and analyze the right sets of data to best optimize promotions and other offers to drive the highest profitability for all players in the CPG value chain. As Infor continues to flesh out its vertical offerings, Acumen will fit nicely inside the Infor CloudSuite Food & Beverage and CloudSuite Fashion offerings, providing more trade promotion and other capabilities inside those existing industry focused cloud products.

As the M&A activity heats up in this sector, we are slowly seeing the RevOps management, data enrichment providers and others come together to become what is looking like a true full journey growth operations platform. As more and more enterprises create offices of the Chief Growth officer, these platforms and tools will equip these new C-suite members with the right data, insights and workflows to better drive strategic growth plans and empower their downstream supporting cast.

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GenAI may be the new UI for enterprise software

GenAI may be the new UI for enterprise software

Enterprise software could be disrupted as category windows become collateral damage to generative AI. The scenario: Today's go-to systems are relegated to plumbing as generative AI becomes the de facto user interface that democratizes data, insights, analytics and automation.

Today, enterprise software is dominated by category winners such as Salesforce in CRM, Workday in HR, Oracle and SAP in ERP, Microsoft in productivity and ServiceNow in workflows starting with IT service management.

Generative AI may upend this situation as horizontal services connect systems and provide an interface that essentially composes applications on the fly. In other words, genAI is the new UI and without the surface layer enterprise systems are simply data stores.

In recent days, we've seen the following:

Runway launched its financial planning application that aims to make it easier for businesses to run their finances. Runway is more about an ambient experience than something trying to replace your existing systems. Directionally, what Runway is trying to accomplish makes sense.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

Deloitte launched an AI suite for CFOs and will likely expand into the rest of the C-suite. The general concept is generative AI that speaks finance and is trained on Deloitte, third party and enterprise proprietary data. This GPT for CFOs approach collects data from various source systems to provide a real-time view into enterprise financial health, insights and scenario modeling. The source systems for this GPT for CFO suite? Oracle and SAP (finance), Coupa (procurement), Salesforce (revenue), AWS Redshift, Snowflake and Databricks with more connections on deck.

AWS said its Q Apps are generally available. AWS' Q effort is a horizontal approach to generative AI that enables enterprises to automate workflows, create agents that act on your behalf, and automate tasks. Amazon Q is already boosting developer productivity, but its Q Business effort is likely to be a long-tail play for C-levels.

Anthropic is also on the way toward bespoke software. The company’s Artifacts effort is about melding collaboration with genAI.

The theme for these new efforts is simple: Combine data sources, find issues and deliver fixes that drive value. All that is missing from these various efforts is the process mining and automation component that could pair up with evolving genAI interfaces. ServiceNow is tying these disparate enterprise parts together as a platform, but AWS could be downright disruptive. ServiceNow CFO outlines method to Pro Plus SKU pricing

How could Q be disruptive to enterprise software? A few reasons:

AWS has the infrastructure layer and tooling to drive efficiency and potentially compress enterprise software margins. AWS also doesn't have an enterprise software business to protect. AWS did it to data center hardware, moves fast and is focused on customer needs.

CIOs are receptive to hearing more about AWS, Amazon Q and Q Apps because they are tired of software vendors that are looking to maintain margins over customer needs. Enterprise vendors are bifurcating between those that are trying to preserve margins and those that'll absorb some compression.

It’s been a year of disgruntled CXOs in our BT150 meetups.

Enterprises realize technology is at an inflection point, but generative AI is moving so fast they will want horizontal approaches so they can abstract the systems underneath (and avoid lock-in).

Generative AI has created a market that's split between customers that want to build vs. want to buy. An enterprise operating system that enables you to adapt regardless of the enterprise software underneath is compelling. That enterprise OS will have to be built and AWS is all about builders.

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Cloud-based GenAI, Industry and Sustainability Plays | Amazon Web Services' Summit NY 2024

Cloud-based GenAI, Industry and Sustainability Plays | Amazon Web Services' Summit NY 2024

Amazon Web Services' Summit New York 2024 is complete and here's a look at a few takeaways and between the lines items you need to know. Overall, there's a #genAI turn to the practical as projects aim to go from pilots to production. Constellation Insights Editor-in-Chief Larry Dignan talks with Constellation VP & analyst Andy Thurai about #cloud-based genAI, industry plays and AWS' sustainability play.

Watch the full interview!

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Infor acquires Albanero, Acumen to build out its industry data efforts

Infor acquires Albanero, Acumen to build out its industry data efforts

Infor said it has acquired two companies to bolster its data migration and industry cloud efforts.

The company said it acquired Albanero, a data migration and management company, and Acumen, a revenue growth management and consulting firm. Infor's plan is to use Albanero and Acumen to serve up industry-specific data within ERP systems.

Albanero has been an Infor data migration and management partner since 2022. Albanero has a data platform that combines information from multiple systems and migrates it to Infor's CloudSuite applications. With Albanero, Infor said it will be able to consolidate enterprise data within its industry applications for midmarket companies.

Infor said Albanero’s data migration services and API connector are available for its CloudSuite for Food & Beverage, Fashion and Distribution industries. Infor said it will add other industries.

Acumen is focused on consumer-packaged goods (CPG) companies and offers consulting and analytics for pricing, trade terms, promotions and processes. Infor said that Acumen will bring Trade Promotion Management to its stack and will be integrated into Infor CloudSuite Food & Beverage and CloudSuite Fashion.

The two purchases closed July 1. Terms of the deal weren't disclosed. Albanero founders, Bruce Douglas and Manish Sharma, Acumen founders Nick Ryan and Matt Wills, and employees of both organizations will join Infor.

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AT&T data for 'nearly all' customers breached

AT&T data for 'nearly all' customers breached

AT&T said that customer data covering "nearly all" of its customers from May 1, 2022, to October 31, 2022, and Jan. 2, 2023 was downloaded from a third-party cloud platform.

According to TechCrunch's Zack Whittaker, that third-party platform was Snowflake. As detailed by Google Cloud's Mandiant unit cybercriminals have been targeting Snowflake customer instances for data theft and extortion.

The breached AT&T data includes metadata such as cell site ID numbers and interactions as well as phone numbers. Personally identifiable information such as Social Security numbers and dates of birth were not breached, according to AT&T.

In a regulatory filing, AT&T said it learned of the incident April 19 with the attack happening between April 14 and April 25. AT&T said:

"While the data does not include customer names, there are often ways, using publicly available online tools, to find the name associated with a specific telephone number."

AT&T's breach is just the latest in a long line of high-profile attacks that are now required to be disclosed. These attacks mean enterprises need to map out response and resilience over prevention.

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Classiq CEO Minerbi on the intersection of quantum computing, HPC and use cases

Classiq CEO Minerbi on the intersection of quantum computing, HPC and use cases

Quantum computing software provider Classiq has been busy forging partnerships with everyone from Nvidia to BMW to Citi as it aims to expand enterprise use cases with a software layer that abstracts the underlying hardware.

A few recent developments from Classiq include:

We caught up with Classiq CEO Nir Minerbi to talk about quantum computing and where it's headed.

Classiq's role and state of quantum computing. Minerbi said the goal of Classiq is to build out the software layer for quantum computing. "We developed the operating system and compiler for any quantum computer," he said. "We established this company four years ago because no one could really program on quantum computers. We knew software is essential to developing this new field."

He added that Classiq's role is to work across multiple quantum computing providers since there are many players and approaches. Minerbi said:

"The quantum computing revolution has turned from a question of when to one of relatively soon. In order to realize the potential, we need software. Many good companies are focused on developing hardware and it's too early to bet on a winner. We see amazing progress in the industry."

An agnostic approach. Minerbi said Classiq's approach is to be able to integrate with multiple quantum platforms. "We want to be focused on what you want to achieve on the application or algorithm," he said. "Our complier is taking a high-level functional model that optimizes for the specific machine and the software stack automates the rest of the processes." In other words, Classiq is focused on use cases and abstraction layers instead of hampering developers with details about the quantum circuit, error correction and other hardware issues.

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Supercomputing and quantum computing. Minerbi said most quantum algorithms are hybrid with some parts for quantum computing and other parts classical. "Today it's definitely a hybrid workflow," said Minerbi. "We see many HPC providers trying to embed quantum computers within the HPC stack. We'll see more HPC centers buying quantum computers and integrating them. From the software perspective, this hybrid approach is something we're very much involved in."

Minerbi noted that HPE is a close partner as is Nvidia, which has its CUDA platform for quantum computing. "Quantum and supercomputing will be integrated so software integration will be very important," he said.

Nvidia's role in quantum. Minerbi said Nvidia is a key partner since many of the quantum simulations were running on GPUs. "These simulations are essential for development of quantum applications and algorithms," said Minerbi. "It's a natural partnership between Classiq and Nvidia."

Minerbi said QPUs, GPUs and CPUs will all be a part of the data center mix and work together.

Meeting developers where they are. Minerbi said Classiq is looking to enable developers to create quantum applications without being experts in quantum computing. "The level of abstraction is tight. You don't need to specify gate level operations and can use Python," he said.

Enterprise use cases. Classiq recently announced partnership with BMW, which established a quantum team a few years ago. Minerbi said BMW understood that to truly leverage quantum you have to create a team to develop applications, algorithms and the IT to be proficient before that hardware is ready. BMW is optimizing cooling systems with quantum algorithms and other use cases. A Classiq partnership with Citibank is different since the bank is just establishing its quantum efforts. Minerbi aid those two examples represent the extremes in quantum, but Classiq aims to be the software stack that appeals to all levels.

The roadmap ahead. Minerbi said Classiq's roadmap for the past few years has been to "take the best practices and technologies from the classical stack and bring them to quantum."

"We eventually want the quantum stack to be almost the equivalent in abstract development and high-performance results," said Minerbi. "All of the pillars are already in the platform, but there's so much work to be done. You'll be able to model a quantum program in a visual way with blocks with python embedded and more integrations. We need to progress the hardware to extract more value with less quantum resources."

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AWS Summit New York 2024: Q Apps, Bedrock fine-tuning, customization, Guardrails aimed at production genAI

AWS Summit New York 2024: Q Apps, Bedrock fine-tuning, customization, Guardrails aimed at production genAI

Amazon Web Services is focused on enabling generative AI throughout its platform in a bid to move projects from pilots to production. The strategy is to meet enterprises where they are with practical features including fine-tuning of large language models (LLMs), guardrails and additons to Amazon Bedrock.

At AWS Summit in New York, the company set out to layer genAI throughout its tech stack along with services that will make it more part of the everyday workflows. To make genAI move from proof of concept to production, AWS is rolling out a series of services. Bayer is rolling out Amazon Q across its organization and Amazon's One Medical is doing the same. AWS, which has launched 326 genAI features since 2023, also touted use cases at Ferrari to ingest data from vehicles and create new designs and models.

GenAI’s prioritization phase: Enterprises wading through thousands of use cases | AWS annual revenue run rate hits $100 billion as growth accelerates | AWS names Garman CEO effective June 3

Add it up and AWS' big goal was to highlight its genAI stack n a way that equates to a series of productivity gains and business wins. Matt Wood, VP of AI Products at AWS, said: "It's better to focus on what's not going to change." 

In other words, genAI will have to deliver business value to move from pilot to production. Wood said genAI is early in its development with leaps in business value appearing in the next 12 to 18 months. "It's becoming apparent that genAI will be woven throughout all of our applications," said Wood.

Here's a look at what AWS announced:

Q Apps goes to GA

Q Apps will be generally available. AWS said Q Apps will enable customers to build apps that can execute tasks via a natural language interface. Q Apps can then be shared with others if useful. AWS pitch for Q Apps focused more on optimizing processes. For instance, Q Apps can distill CRM data to generate insights that go into new RFPs saving hours spent digging through documents and content repositories.

AWS launches Amazon Q, makes its case to be your generative AI stack

The shift with Q Apps is that AWS is moving from finding an answer to a question to figuring out what the problem is. What remains to be seen is whether a horizontal interface such as Q effectively becomes the engagement layer to other enterprise applications.

Q Apps can turn Amazon Q conversations into apps, leverage enterprise data and inherit security and governance controls.

AWS is enabling developers to generate custom code inside Amazon Q Developer since Q can become familiar with an enterprise's coding practices, terminology and comments. "Picture a world where developers are running multiple agents in parallel," said Wood.

Within Amazon SageMaker Q will be used to move more quickly through different steps for fine tuning models or preparing data. AWS is adding the ability to build ML models in natural language, add recommendations in SageMaker Studio notebooks and generate code and resolve bugs with a notebook.

The upshot is that customers are starting to use Amazon Q as a vehicle to build assistive systems. Wood said Q can be used for large applications as well as ones for small teams. Smartsheet uses Amazon Q chatbots to connect teams, write account plans and complete other tasks.

Bedrock improvements

Bedrock will add a new image model from Stability AI to complement the addition of Anthropic's Claude 3.5 Sonnet. Wood said that 41% of large enterprises were using 3 or more large language models.

Claude 3 Haiku will also be fine-tunable via Bedrock. The ability to fine tune models in Bedrock will be a win for enterprises since fine tuning a smaller model is more cost effective and useful. Fine-tuning for other Claude models will follow. Wood said a few quick steps will enable enterprises to tune with internal data, control privacy with encryption keys and customize with hyperparameters.

Bedrock will also get more retrieval augmented generation (RAG) capabilities within Bedrock including more connectors to Salesforce, Confluent and Microsoft SharePoint for more enterprise data and knowledge bases. Those connectors, wrapped under Knowledge Bases for Amazon Bedrock, are designed to enhance foundational models as well as speed up vector searches and Amazon memoryDB integrations.

"Customers are using RAG for a wide range of use cases," said Wood. "We're expanding the type of data you can connect beyond S3 into additional data connectors and Web data from user provided public URLs."

AWS said it is also adding more capabilities to Agents for Amazon Bedrock including the ability to retain memory over multiple interactions. This ability is being integrated into customer facing use cases at Delta, United and Booking.com.

"You can carry over the context from different agent workflows," said Wood.

Wood added that Agents for Amazon Bedrock will be able to generate code, analyze data and generate graphs.

Guardrails

The company is also launching guardrails beyond Bedrock that are independent of models, Guardrails can ground based on context, search for a subset of acceptable answers via knowledge base connectors and increase response rates. AWS is betting that the ability to add a guardrail layer that's LLM and application agnostic will be a win.

AWS launched Contextual Grounding Check in Guardrails to ground answers via knowledge bases and corporate data. The grounding check asks if the RAG result is found in source material and whether it's related to the query.

AWS is also adding a new interface for prompt flows and prompt management. These prompt tools will be specific to model families but will enable faster upgrades.

Amazon is matching 100% of the electricity consumed by global operations with renewable energy. The play here for AWS is offsetting sustainability concerns due to genAI workloads.

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AMD acquires Silo AI for $665M as it builds out AI ecosystem, genAI stack

AMD acquires Silo AI for $665M as it builds out AI ecosystem, genAI stack

AMD said it will acquire Silo AI for $665 million in cash in a deal that advances the company's efforts to broaden its generative AI ecosystem.

Silo AI, the largest private AI lab in Europe, will give AMD a team of AI scientists and engineers, tailored AI models and the ability to build out an enterprise ecosystem. While Nvidia has cashed in on generative AI due to its GPUs, the company also has developed an extensive platform and ecosystem.

AMD has invested $125 million across a dozen AI companies in the last 12 months and acquired Mipsology and Nod.ai. The spending spree is designed to bolster AMD as a full-stack AI player.

Peter Sarlin, CEO of Silo AI, will continue to lead the company, which will become a part of AMD Artificial Intelligence Group. The deal is expected to close in the second half of 2024. Vamsi Boppana, senior vice president of the Artificial Intelligence Group at AMD, said Silo AI will accelerate AMD's AI strategy.

Key facts about Silo AI include:

  • The company is based in Helsinki, Finland.
  • Customers include Allianz, Philips, Rolls-Royce and Unilever.
  • Silo AI has open-source multilingual LLMs including Poro and Viking.
  • The company is known for its SiloGen model platform and Silo OS.
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GenAI’s prioritization phase: Enterprises wading through thousands of use cases

GenAI’s prioritization phase: Enterprises wading through thousands of use cases

Enterprises have thousands of use cases for generative AI and are now working through prioritizing them and ultimately moving to production.

At AWS Summit 2024 in New York, a panel of partners and integrators talked about Amazon Q early adoption and genAI use cases with a focus on pilots to production.

The big takeaway is that the experimentation phase is over, but companies have thousands of use cases to prioritize. General themes are horizontal functions, industry focus, IT, code and anything focused on productivity. Integrators are now working toward vertical adoption of generative AI.

Indeed, those use cases--especially industry-focused efforts-- are behind a multi-year partnership between AWS and Deloitte create a genAI innovation lab. The two companies are designed to move generative AI pilots to production. The effort is aligned to Deloitte’s IndustryAdvantage initiative, a strategic $2 billion investment to co-innovate with eligible clients and alliances to develop industry-focused solutions. ”For instance, Deloitte's lab is developing an AI suite designed for CFOs. Deloitte and AWS will combine various services to focus on industry use cases leveraging Amazon SageMaker, Bedrock, Q and Braket. 

TCS Krishna Mohan, VP and Deputy Head of TCS' AI and Cloud unit, said the focus is on prioritizing use cases to deploy. Enterprises went to line of business to collect use cases and came back with thousands.

"A lot of customers have 3,000 to 4,000 use cases. They went to line of business heads and asked for use cases. Every line of business leader had a budget and the central organization also had budget," said Mohan. "Now the focus is on prioritizing them and how to deploy into production."

Mohan said use cases that deliver on productivity metrics win out. Use cases that are horizontal across functions, industry focused and make it easier on compliance are winning out, he added. Key use cases include IT productivity, sales, marketing, finance, HR, customer segmentation in retail, automation in manufacturing, and customer experience in airlines.

Stephanie Pace, Global AI/ML GTM leader at Quantiphi, said the genAI market is segmenting between enterprises builders and buyers. Companies looking for faster time to value, said Stephanie Pace, Global AI/ML GTM leader at Quantiphi. Pace said Quantiphi was an Amazon Q pilot customer and has found it helpful to deliver faster time to value. 

According to Pace, genAI budgets and decision making are more aligned with the line of business. The genAI buying table now includes IT, compliance and line of business. "We saw pilots not move into production because compliance wasn't in the room," said Pace. "Projects are now focused less on the art of the possible and more on the art of the profitable."

The biggest challenge for moving genAI to production is enterprise cloud and data maturity. Not all companies are ready for genAI due to data strategies being immature, said AllCloud CEO Eran Gil.

Dr. Ryan Ries, Chief Data Strategist at Mission Cloud, said genAI requires the correct data schema to use natural language processing. "You have to go through and clean up these systems so the data being captured is accurate and available for use," said Ries. "Companies moved toward having the best data lake ever without doing the groundwork."

Generative AI's looming crap in, crap out data problem

Constellation Research CEO Ray Wang said:

"Many projects will remain in POC phase as clients grapple with how much data they need to deliver on a precision that their customers will trust, where will they partner for data, and who do they sue when something goes wrong? That being said, the customers that know when and where to insert a human into the process are the customers that will win in the Age of AI."

Other takeaways include:

  • Mohan said manufacturing is adopting genAI quickly as are regulated industries.
  • Implementations of genAI can be challenging due to integration, people and processes. The big lesson is that genAI by itself doesn't cure anything by itself.
  • Sales productivity has been a big use case by democratizing information and surfacing the data that improves efficiency. Pace said customers have used Amazon Q to surface insights, in-stock inventory and order information. "We have explored sales productivity and driving revenue with customers," said Pace.
  • Recruiting was another key use cases, said Ries. GenAI is being used to comb through candidates, create questions and prevent bias.
  • There's a lot of plumbing work that is required to move genAI into production.
  • Data sources and IP issues typically require enterprises to build more genAI integrations and customize. "Everybody is worried about IP and their data going out," said Mohan.
  • Mohan said enterprises need to focus on business use cases and value over what technology is being used. Mohan said he expected the next two to three quarters will sort out the business value of genAI and more best practices.
  • Enterprises are seeing cost improvements with genAI due to model improvements as well as compute efficiency. New tooling is also helping improve costs.

More on genAI dynamics:

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Runway aims to make business finance more intuitive, collaborative

Runway aims to make business finance more intuitive, collaborative

Runway, a startup looking to break into the financial planning for businesses market, said its AI-driven platform for enterprise finance is generally available.

The company, not to be confused with the AI video content startup with the same name, is looking to break into a crowded market. Runway is aiming to make financial modeling, headcount planning, department budgeting and finance workflows more user friendly and intuitive.

As previously reported, enterprise software buyers are increasingly disillusioned with giants that keep raising prices and a lack of new entrants offeringcompetition.

Siqi Chen, CEO of Runway, said CFOs need to be freed from admin work and reports that largely look backward. Runway features "Ambient Intelligence" that enables CFOs and teams to better collaborate and plan. "What teams need is a clear, intuitive understanding of how all the functions of a business work together, from sales and marketing to product and engineering. Clear context creates alignment, enables true collaboration, and accelerates execution," said Chen.

Runway connects with more than 650 applications including the most common accounting, HR, CRM and data warehouse systems. Runway automatically updates forecasts with new actuals and simplifies processes with a drag-and-drop approach.

Constellation Research CEO Ray Wang said:

"The future is ambient experiences, where systems move from persuasive (making you spend more time for their benefit) to consensual (where you choose where you spend time), to mindful (where the system makes nudges and suggestions in your interest).  The crux of the design is to make life easier, which is very rare in legacy solutions. Add a layer of human collaboration, this system has the ability to not only save time and money, but also help you find exponential improvements." 
 

Here's what Runway's Ambient Intelligence includes:

  • Clear explanations of financial drivers such as cash burn and gross margin for more democratized modeling.
  • Budget vs. actual variances that are automatically adjusted.
  • Scenario comparisons for modeling and making future plans.

While Runway is in a crowded market the company has landed customers such as Superhuman, AngelList and 818 Tequila. According to TechCrunch, Runway has raised $33.5 million in venture funding so far.

 

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