We both find these agents great. They collect all the necessary information from visitors like questions about the product, use cases, pricing, competitors and so on. While the AI agent is fielding my inquiries and collecting information for the vendor, it’s providing me with answers back while training itself to do these tasks better. Some agents then initiate actions: Do I want to see a demo, get pricing, or speak to a live person? And some queue all that up within the chatbot or interface I’m in.

I love this!

As a potential buyer, I really don’t want to go through the onerous process of contacting three vendors to get a demo, which starts with filling out a form and then waiting for an SDR to contact me. That initiates the old/current process in which I’m 1) placed into a nurture campaign, 2) forced into an inquisition by the SDR, and then 3) passed to a sales rep. But it doesn’t end there: 4) The sales rep then cross-examines me, asking many of the same questions that the SDR did previously, then 5) introduces a sales engineer to the mix, and 6) finally schedules a demo.

This experience stinks. I don’t want to go through the “buyer’s journey” that the vendor decided was right for me. I want to go through my journey and get the information I want, when I am seeking it. AI agents are the gateway to enabling such customization, so bring it on. I’m a fan.

And yet, there’s still something missing here. Companies and vendors are creating agents or personas that represent the company’s side of things. But what about me? What about the other side of the equation, the individual buyer?

INDIVIDUAL AGENTS
This was the discussion that Harish and I had. He posited that the next logical step in this wave of AI agents was individual AI agents—the Harish agent, the Bill agent.

Visualize this: I want to learn about three different vendors’ software. Instead of starting the old, previously described process (times three!!), how about I train MyBill agent? Or as Harish nailed it, “Mini Me”.

In this situation, I feed Mini Me with my needs. It could be a requirements document, an RFP, my specific challenges, my workflow documents . . . you name it. Now that my agent is trained, it goes to a group of vendors and initiates a knowledge transfer. Included in my AI agent could be a whole bunch of demographic information that I elect to share: my role, my contact information, my background, my contact preferences, my calendar and availability. I could even prime it with my current buying stage: I am still evaluating, I am ready to buy, I have budget or don’t have budget, I am the decision maker or I’m an influencer to the decision. I can share whether my company is starting a new project or looking to replace a current provider. I can allow my individual agent access to publicly available information about my past buying behavior (i.e. I worked at Pendo* and we used Salesforce on the sales team).

In digital marketing, we talk about first-party vs. third-party cookies. We talk about intent. We talk authentic, personalized outreach. I’d submit that aside from talking with me live directly, an individualized agent is the next most personalized form of engaging me. As I lamented above, talking with me personally is getting harder to do. First, like many buyers I no longer have a work phone, so just ringing me is getting harder. Plus, with branded caller ID, I can see who you are and ignore your calls. Email is a fine alternative to phone, but we all know how noisy inboxes can get. And then alternative channels like LinkedIn are spotty depending on which tools I utilize. Most importantly, as a buyer I neither want to engage nor have time to spend with three vendors and go through that difficult “journey” outlined above.

Let’s return to my hypothetical scenario above. I’ve dispatched my individualized agent to do the heavy lifting for me. Why does this concept appeal to me so much?

As a buyer, I want to get as well educated on a potential purchase as possible. I want to scour the vendor’s data and product. I want to know what their customers are saying. Likewise, I want them to understand me and my project as best they can—really zero in on my business problem and prove how they solve it.

Once I’ve dispatched my agent, I can apply the human touch. I can review the details my agent gathered and evaluate what it’s found for me.

In this scenario, I’ve provided a bunch of data about me, my company, my project and my preferences to potential vendors. I gave them this information, instead of having them purchase data and tools and commission sales teams to stalk me. Meanwhile, the vendor receives this detailed data in exchange for giving me what I want: knowledge. That knowledge can be in the form of videos, proposals, data sheets, demos, etc. These are all items that I can roll up into a business case. I can achieve a fairly informed position, all without needing multiple humans to educate me (and waste precious human time on both sides).

PERSISTENT AGENTS
Before we close, let’s take this imagined scenario one step further. Once my agent has visited the vendor site, maybe it keeps a connection to that site so my data stays updated and keeps information flowing. The vendor releases a new version. Or a new product. The company shares its new roadmap, so I can now incorporate that into my knowledge base. The positioning changes for how the company sells its product. They run a promotion on pricing. These are all valuable inputs for me as a buyer and worth keeping a consistent connection. Data could similarly flow from me back to the vendors in play. What if there is a budget freeze? What about an economics change for the buyer? Valuable inputs to share yet again.

THE BIG QUESTION
I get asked a lot: Do I think AI is the gateway to sales reps’ extinction? No, I don’t believe so. But I do think the current sales model is quickly ceding to a new agentic-fueled model.

We’ve already seen in transactional business arrangements that technology and software can eliminate the person. I used to visit a store and deal with a person at the checkout to buy laundry detergent; Amazon changed that forever. But in a considered and substantial purchase with a long-term relationship at stake, I still believe humans need to be involved in the sale. What if I’m buying a software that requires implementation, integration or setup? What if this is a multi-year project or purchase? Sales reps will still do what they do best: show their product, prove its value and close the deal. Agentic sales are accelerating this process while increasing its efficiency, and they’re only getting started.

Today, we know sales reps waste a tremendous amount of their time doing non-sales work. The rise of AI agents in the go-to-market function will help eliminate some of that work, which allows sellers to spend more quality time with buyers, handling the stuff the agents cannot.

Look, I’m somewhat self-aware. Is the individualized-agent scenario I just imagined above easy to implement? Will it work the way I played it out? These and many other questions are still unknown. But I do see large changes to the selling model we’ve used for 30 years ahead.

The information contained here is based solely on the opinion of Bill Binch, and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as legal, tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity. The views expressed here are solely those of the author.

*Denotes a Battery portfolio company. For a full list of all investments and exits, click here.

The information above may contain projections or other forward-looking statements regarding future events or expectations. Predictions, opinions and other information discussed in this publication are subject to change continually and without notice of any kind and may no longer be true after the date indicated. Battery Ventures assumes no duty to and does not undertake to update forward-looking statements.

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Our practice is diverse and focuses on critical enabling technologies for research, quality control, automation and scientific workflows, spanning instrumentation and sensor technologies, consumables (the picks and shovels enabling life-science research, analytical testing workflows and R&D) — even training and service providers.

Much like our software-investing colleagues, we prioritize what we call high-quality revenue: predictable and recurring revenue from consumables and services, over one-time product sales.

For our companies, which often have a number of revenue streams, from product sales and service contracts to consumable sales and software/data subscriptions, it can be more challenging to define value, especially on a comparative basis as the revenue mix varies across companies. But we are seeing more and more technology companies implement software-enabled products and services, as artificial intelligence becomes more accessible and as investors and management teams place a greater emphasis on revenue stability, especially in the wake of the pandemic.

Within our portfolio, we’ve always prioritized improving revenue mix as a key value creation lever, in addition to other key factors such as scale, organic growth rate, profitability and customer/market diversification to name a few.

So, we sought to quantify what we knew intuitively: that for non-software technology businesses, revenue quality mix is a meaningful indicator of value, and moreover, should serve as a key lever to help companies increase in value over time.

Based on a quantitative analysis of publicly-traded ITLST companies and our own portfolio, we created a new framework — the Revenue-Quality Podium — to help management teams, investors and industry stakeholders track value creation as companies transition to a higher share of high-quality revenue.

Download the Revenue-Quality Podium Whitepaper here to see our full analysis. 

The post Introducing the Revenue-Quality Podium: How Quality of Revenue Drives Value for Industrial Tech and Life-Science Tools Companies appeared first on Battery Ventures.

Proposal and business-development teams are key to discovering and unlocking revenue opportunities for government contractors. Bid discovery alone proves cumbersome, as finding qualified opportunities across a myriad of government data sources is not enough; teams need to prioritize bids with the greatest chance of winning (pWin), while also optimizing for the largest revenue opportunities.

From there, teams spend weeks or months parsing request for proposal (RFP) requirements, piecing together past proposal content, conducting fresh market research, manually writing long proposals and meticulously checking compliance boxes to avoid self-disqualification. This convoluted process puts small businesses at a significant disadvantage and hampers efficiency and growth for even the largest contractors.

Procurement Sciences is changing the paradigm. By combining deep workflow expertise with the power of large language models (LLMs), Christian and his team have built an end-to-end platform that is revolutionizing the multi-trillion-dollar government contracting industry. The company’s AI-powered suite of tools automates the entire government-contracting lifecycle, enabling businesses to discover relevant opportunities, generate tailored proposal content, analyze the competitive landscape, manage their pipeline and verify compliance – up to 10x faster than was possible through legacy, manual methods.

The value is clear. In just over a year since launching, Procurement Sciences has empowered over 80 customers, from Fortune 500 aerospace and defense companies to small businesses across sectors, to work smarter and win more.

And from customers’ perspectives, the results speak for themselves:

“We doubled proposal volume while keeping the team size the same. 70-80% of words were generated by AI in those submissions.”

“A task that normally takes two weeks is now taking just four hours.”

“Our pWin historically has been 35%. With PSci, it’s 50%.”

“This is equally valuable for everyone. State, federal, local. FARs, DFARs. Everyone.”

We could not be more excited to partner with Christian and the Procurement Sciences team on their journey to bring the power of AI to the world of government contracting. Through their work, they are not only boosting efficiency and growth for the largest U.S. government contractors but also helping to level the playing field for small businesses seeking to serve our government and communities across the country. We are honored to be a part of that journey.

The post Procurement Sciences Makes Government RFPs a “Really Fast Process” appeared first on Battery Ventures.

But what if the new, more-challenging macroeconomic environment, coupled with powerful new technologies like AI, means we should rethink the metrics we’ve all gotten used to – and encourage companies to reevaluate the right set of benchmarks to survive and thrive?

That’s one of the core takeaways from our 2023 OpenCloud report, which we’re releasing today as the enterprise-tech industry continues its journey out of the woods and, hopefully, spawns stronger, fitter, leaner companies.

Indeed, in the technology markets, the dust is finally beginning to settle. Growth rates for the big cloud providers are not what they were two years ago, and the IPO market has not yet rebounded. But we see the technology landscape quietly pivoting toward a horizon brimming with promise as more enterprise-tech companies revert to fundamentals, seeking to balance growth and profitability and capture new efficiencies with AI.

In many ways, this is a watershed moment for the industry, one that will change how software and technology companies are created. The implementation of AI, both in terms of companies being able to offer more AI products and leverage the technology to drive more-efficient internal operations, will allow cloud companies to generate new sources of revenue while meaningfully reducing internal costs.

As we wrote in this year’s report, “as growth returns, highly profitable, high-growth companies will return,” ushering in an era of mega-B2B software companies that will gear up for IPOs when they hit $1 billion in revenue, not just $100 million (the old standard). And as these companies evolve and scale over time, the very definition of a healthy software company will change.

Our full 2023 State of the OpenCloud report is available for download here and at the bottom of this post, where we’ll break down some of our key takeaways.

Cloud Providers Invest Ahead of the AI Curve

Pain in the market has affected every software company, public and private. The world’s largest cloud providers — AWS, Microsoft Azure and Google Cloud, which generate a collective $200B in annual revenue — are no different. These companies have seen growth slow down significantly in the past year and have been forced to cut headcount and other costs.

Importantly, however, these companies are leaning heavily into capital expenditures related to new businesses: major, long-term investments into the future of software, particularly in AI. We interpret this as a leading indicator for building customer demand, conveying a sense of optimism for the market recovery and a high-growth, high-profitability future enabled and necessitated by automation.

Redefining Healthy Software Company Metrics

As research-driven, enterprise-technology investors, we have a deep appreciation for metrics and ratios but the assumptions that guided us in years past may not hold for the next cloud generation. While valuations have come back closer to long-term historical averages, the profile of public companies today is far from average – in many ways, they are stronger than ever.

It’s also important to distinguish valuations from business fundamentals. A company generating $1 billion in revenue today might not be valued as highly as it once was, but reaching such a revenue milestone is still a monumental achievement. And we think these companies will be well positioned as we enter an era of more profitability, more efficiency, and ultimately, higher valuations over the long term.

Starting from the top line, we recommend that early-stage and growth-stage software companies focus closely on logo velocity. We know that as companies mature, they rely on expansion revenue to drive growth. However, logo velocity helps to ensure that there is a large enough customer base to fuel future growth, but also aligns sales to balance new ARR and expansion ARR from day one.

The Path Ahead for Private Unicorns

Navigating the public markets is always challenging, but the current climate is particularly unforgiving. In our 2022 State of the OpenCloud report, we predicted there was pain ahead for private companies who faced a very high bar to IPO. Cut to today, we have seen only one software IPO in the past year — Klaviyo.

While we’re not out of the woods yet, the software ecosystem at large can support a path to IPO for many private software unicorns. And, paradoxically, the tougher macro environment has created a new class of pre-IPO companies that are stronger and more resilient than those in the past.

Conclusion

While we are still in the early innings for OpenCloud, our team is more optimistic than ever about the potential for cloud and open-source companies, with AI as a new growth lever. There is a significant opportunity at hand for software leaders to recalibrate and improve their companies’ operational efficiency through automation. The implementation of AI across customer offerings and internal processes promises not only cost savings but also enhanced capabilities, paving the way for resilient and future-proofed enterprises.

For more insights, please see our full 2023 State of the OpenCloud report below.

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