Results

Palantir US commercial revenue jumps 121% in Q3, ups outlook

Palantir US commercial revenue jumps 121% in Q3, ups outlook

Palantir continued to land commercial accounts as its third quarter results handily topped expectations. The company’s US commercial revenue was up 121% from a year ago.

The company reported third quarter earnings of $476 million, or 18 cents a share. Non-GAAP earnings were 21 cents a share on revenue of $1.18 billion, up 63% from a year ago.

Wall Street was expecting Palantir to report earnings of 11 cents a share (17 cents a share non-GAAP) on revenue of $1.09 billion.

During the quarter, Palantir announced partnerships with Hadean, Lear, Lumen Technologies and SOMPO. Palantir has increasingly been competing with more traditional enterprise software vendors and winning accounts via its data ontology and ability to deploy AI.

Based on the Rule of 40, Palantir blew that away with a score of 114%. CEO Alex Karp said the company’s US business growth was 77%. Most of Palantir’s revenue is focused on the US as well as western allies.

By the numbers:

  • US revenue was $883 million in the third quarter.
  • US commercial revenue was $397 million with US government revenue of $486 million, up 52% from a year ago.
  • The company said it closed $2.76 billion of total contract value in the third quarter, up 151% from a year ago.
  • Customer count was up 45% from a year ago.
  • Palantir had $6.4 billion in cash and equivalents.
  • In the third quarter, Palantir closed 204 deals worth at least $1 million, 91 worth at least $5 million and 53 worth at least $10 million.

As for the outlook, Palantir raised its outlook and projected 2025 revenue between $4.396 billion and $4.4 billion. Commercial revenue will top $1.43 billion. Fourth quarter revenue will be between $1.327 billion and $1.331 billion.

In a shareholder letter, CEO Alex Karp delivered his usual complement of strong quotes. He said:

  • “This remains the beginning, the first moment of a first chapter.”
  • “It is worth remembering that the business is now producing more profit in a single quarter than it did in revenue not long ago. This ascent has confounded most financial analysts and the chattering class, whose frames of reference did not quite anticipate a company of this size and scale growing at such a ferocious and unrelenting rate. Some of our detractors have been left in a kind of deranged and self-destructive befuddlement.”
  • “Our partners in the United States—the earliest and most voracious adopters of the novel language models that are presently reordering human life and of the Ontology that allows them to effectively operate—understand how significantly the terrain beneath us all has shifted.”

 

 

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AI in the Boardroom: Redefining Consulting, Talent & Trust

AI in the Boardroom: Redefining Consulting, Talent & Trust

"All business is personal" — Jay Laabs, CEO & Founder of Spaulding Ridge, shares how deep client relationships, trust, and strategic upskilling are driving AI-powered transformation in consulting. From boardroom pressures to leadership imperatives like ‘freedom within a framework,’ discover how AI is changing not just business models, but company culture itself. This is for anyone navigating the evolving landscape of AI, data, and innovation. Watch this live interview with Liz Miller during Dreamforce 2025!

Future of Work Innovation & Product-led Growth Next-Generation Customer Experience Tech Optimization On ConstellationTV <iframe width="560" height="315" src="https://www.youtube.com/embed/9yNEUaB9g6E?si=VosCa9ijM6TSrXHi" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

MongoDB names CJ Desai CEO

MongoDB names CJ Desai CEO

MongoDB said it has appointed CJ Desai, an alum of Cloudflare and ServiceNow, as CEO effective Nov. 10.

In a statement, the company said current CEO Dev Ittycheria will retire from a full-time role but will stay on MongoDB's board.

It was evident that Desai was going to land a CEO gig when Cloudflare reported its earnings. Cloudflare said Desai was resigning as president of product and engineering to take a CEO role at a visible publicly traded company.

Desai has held technology leadership roles at Cloudflare, ServiceNow and held roles at EMC and Symantec over the last 25 years. Desai's highest profile role was at ServiceNow where the company's annual revenue went from $1.5 billion to $10 billion. Desai resigned from ServiceNow in 2024 after violating company policy.

In a statement, Desai said:

"MongoDB has long been the partner of choice for building applications that transform businesses, and now it is uniquely positioned to power the next wave of AI-driven applications. My directive is clear: by staying relentlessly close to customers, delivering category-defining products, and executing at scale, we can seize the enormous opportunities ahead."

Desai takes over for Ittycheria as MongoDB is performing well. MongoDB reported a strong second quarter and raised its outlook.

MongoDB said its third quarter results will be at the high end of its earnings and revenue guidance. MongoDB said it will report earnings on Dec. 1.

Scale and strategy

Wall Street analysts asked Desai about strategy, go-to-market scale and the architecture behind AI workloads. 

Desai said MongoDB can further scale. A few key takeaways include:

  • "MongoDB has long been the partner of choice for applications that transform businesses, and I believe the company is exceptionally positioned to power the next wave of AI applications."
  • Desai said his experience in scaling companies will be useful to MongoDB. Desai said MongoDB’s market motion will be to expand from developers to the C-suite.
  • "MongoDB participates in a very large growing market, and it has also a very clear architectural advantage to become part of the architecture for modern workloads. There are so many workloads being changed to leverage AI. We have reached this inflection moment where Mongo can truly become the heart of this next phase of architecture. MongoDB architecture was not force fitted for AI workloads. It existed for AI workloads."
  • Desai said that AI workloads are rewiring database decisions. “I think the rearchitecture is happening right now, and it is our job as MongoDB to ensure that customers understand the why and how we are ideally suited for those applications,” he said.

Desai, who started his career at Oracle, was asked about MongoDB's positioning given he oversaw the ServiceNow acquisition of RaptorDB. He was also asked about the limitations of Postgres. He said:

"When I truly look at the technology of MongoDB, and I spend some time truly evaluating the recent innovations from MongoDB on 8.0 and post 8.0 releases, what I would say is that this is the right database to build or modernize an application for. Relational databases tend to be very rigid and do not handle the unstructured data really well. When you think about AI applications of the future, it will still be both structured and unstructured data. And as the business changes, MongoDB would still be on the right side of that equation. Many of our customers want to scale that application for AI workloads."

Regarding Postgres, Desai said ServiceNow wanted to maintain the SQL functionality and structured data approach. "Our job with the team is to continue to make sure that MongoDB is top of mind, understand the advantages of MongoDB and to be flexible for scale out," he said.

Desai was also asked about competition from cloud providers. He said MongoDB's launch of Atlas nailed the cloud transition and ability to be agnostic. He said that MongoDB's multicloud approach is a moat. "Multicloud is here to stay," said Desai, who noted recent cloud outages. "MongoDB with a cloud agnostic architecture and how seamlessly it can work in a multi cloud environment is a competitive moat for MongoDB."

He added that MongoDB has tailwinds in database migrations to the cloud as well as AI workloads. Desai said international, notably India, also remains a growth area for the company. Desai will talk more about MongoDB strategy on the December 1 earnings call.

Constellation Research analyst Holger Mueller said:

"This brings Desai's career to a full circle -- 30 years ago he and his team built databases at Oracle and now he is at the helm at one of the few Oracle competitors left standing. This is good news for MongoDB customers and the overall database market that has atrophied. Data remains critical in the AI era and the most coveted data is in documents."

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OpenAI inks $38 billion deal with AWS, starts renting GPUs immediately

OpenAI inks $38 billion deal with AWS, starts renting GPUs immediately

OpenAI said it has signed a $38 billion agreement to use AWS and its Amazon EC2 UltraServers immediately. Under the deal, OpenAI will access hundreds of thousands of NVIDIA GPUs and likely CPUs for AI agents.

For AWS, the OpenAI deal is another big win. AWS reported 20% revenue growth in the third quarter and launched its Project Rainer cluster for Anthropic. AWS will announce Trainium3 at re:Invent 2025.

The big takeaways from this OpenAI-AWS deal may be the following:

  • OpenAI and Anthropic are diversifying compute with the latter more likely to leverage custom silicon from hyperscalers. In the AWS case, Anthropic is using Trainium2. For Google Cloud, the Anthropic deal is about the TPUs. OpenAI is diversified across Microsoft Azure, Google Cloud, Oracle Cloud Infrastructure and now AWS. See: OpenAI, Anthropic increasingly diverge as strategies evolve
  • OpenAI is more of a Nvidia shop, but it'll be interesting to see whether it winds up diversifying into AWS custom silicon--especially for CPUs.
  • The AWS-OpenAI deal is another data point in the AI industry's circular economy. OpenAI has more than $1 trillion in future commitments. However, OpenAI appears to be paying AWS immediately.

In the statement, AWS and OpenAI said:

"OpenAI will immediately start utilizing AWS compute as part of this partnership, with all capacity targeted to be deployed before the end of 2026, and the ability to expand further into 2027 and beyond."

Other key points:

  • The deal is focused on Nvidia GPUs but can expand to "tens of millions of CPUs" for agentic workloads. Inference will be a huge market as AI agents scale.
  • The OpenAI infrastructure on AWS will include clusters of Nvidia's GB200s and GB300s via Amazon EC2 UltraServers.
  • AWS said the OpenAI deal includes inference for ChatGPT as well as training new models.
  • OpenAI and AWS started to work together when the AI company put its open weight foundation models on Amazon Bedrock.

More:

 

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TD SYNNEX CIO Kristie Grinnell on adopting AI, tech debt and change management

TD SYNNEX CIO Kristie Grinnell on adopting AI, tech debt and change management

TD SYNNEX CIO Kristie Grinnell outlined the tech distributor’s AI plans and how change management is key to driving revenue growth and productivity.

Here’s a look at the takeaways from my conversation with Grinnell, a Constellation Research BT150 member.

TD SYNNEX’s vision is to become an “AI-fluid company.” Grinnell described how the distributor’s AI strategy is built around empowering every employee to use AI tools intelligently rather than isolating them to specialists:

“We want to grow our business with the use of AI so it will augment our workforce. We envision a hybrid workforce, but we also envision that the future of distribution will become more digital as well. Now we don't think that the human will ever completely be out of the loop, but it’s imperative that we become what we call an AI fluid company where everybody can speak AI, know when to use an agent, know when to use Gen AI, know when to use machine learning and know when to even use a bot versus that toolkit.”

AI is central to both revenue growth and operational efficiency. Grinnell emphasized that AI doesn’t just save money—it’s equally a revenue driver:

“We feel it has to work for us to grow and our customers want the same experience. They want TD SYNNEX employees to be able to walk them through where they are on that journey digitally. So, there's a revenue piece to that. But we absolutely see that we're going to have to find the efficiencies in our own company so that as we grow that revenue, we're growing our profit as well.”

The “AI factory” concept will tackle tech debt and enable agility. Grinnell framed TD SYNNEX’s modernization around an “AI factory” that turns agents into the new layer of applications while gradually eliminating legacy complexity:

“We want to create what everybody's calling the AI factory. That factory is going to have to be able to talk to the data as well as the applications as inputs and create essentially new agents that become the new applications, overriding some of that tech debt that we have. Our goal would be that one by one, those agents will help us start to take a little bit of the tech debt away.”

Data discipline begins with focused domains, not “boiling the ocean.” She explained that effective AI depends on pragmatic data strategies—starting small, cleaning up legacy complexity, and proving value domain by domain:

“The approach we're using is that you just have to pick a domain as if this is where we're going to start and then we can go forward. We have so much data. It can be messy. We just have to really focus on one area first.”

The greatest challenge is change management and workforce education. Grinnell said success with AI depends more on people and learning than on technology alone:

“Every employee has an opportunity now that they didn't before. It's really not about the technology at this point. It's about the education, the knowledge and the know-how of your employee base. For us, this is more the biggest change management exercise we've ever led, rather than the biggest IT project we've ever led.”

More from CCE 2025:

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AGI may be far away, but 'jagged AI' will still take jobs

AGI may be far away, but 'jagged AI' will still take jobs

The enterprise AI market appears to be embracing a little nuance and CxOs would be wise to avoid banter about artificial general intelligence and think about systems that drive returns in the real world. Those returns will likely be generated with fewer workers.

Microsoft CEO Satya Nadella had an interesting exchange with a Wall Street analyst who asked about AGI, Microsoft's deal with OpenAI and the never-ending rise in capital expenditures. The exchange followed Microsoft’s strong first quarter results.

Nadella said: "How are these AI systems going to truly be deployed in the real world, make a real difference and make a return for both the customers who are deploying them and then obviously, the providers of these systems? Even as the intelligence capability increases, the problem is it's always going to still be jagged. You may even have a capability that's fantastic at a particular task, but it may not uniformly grow. So, what is required is in fact, these systems, whether it is GitHub Agent HQ or the M365 Copilot system. Don't think of this as a product. Think of it as a system that in some sense smooths out those jagged edges and really helps the capability."

As for AGI, Nadella said it's not coming anytime soon. "I think we will be in this jagged intelligence phase for a long time," said Nadella. "We feel very good about building these systems as organizing layers for agents to help customers. I feel pretty good about the progress in AI, but I don't think AGI as defined by us in our (OpenAI) contract is every going to be achieved anytime soon. I do believe we can drive value for customers with advances in AI models by building these systems."

What about returns for Microsoft? Don't sweat that one. Microsoft generated first quarter free cash flow of $25.7 billion, up 33% from a year ago, even as it stepped up capital expenditures to $34.9 billion with half of that sum going to GPUs and CPUs. CFO Amy Hood said Microsoft is capacity constrained through the fiscal year but is seeing strong demand signals for AI and returns ahead.

In other words, Microsoft doesn't have to chase AGI. Neither does Alphabet, which said it will spend $91 billion to $93 billion in 2025 capital expenditures. Alphabet is seeing strong demand for its TPU instances as well as its Google Cloud AI services and Nvidia-based offerings.

Alphabet CEO Sundar Pichai said the company is seeing extensive productivity gains from AI and monetizing advanced in multiple ways--advertising, YouTube and Google Cloud. The pace of LLM big bang developments be more spaced out, but returns are there.

In addition, enterprises are taking a more systems-based approach to AI and focusing on multiple models and agents. "Our packaged enterprise agents in Gemini Enterprise are optimized for a variety of domains, are highly differentiated and offer significant out-of-box value to customers. We have already crossed 2 million subscribers across 700 companies," said Pichai.

At the Google Public Sector Summit, a few panels noted that "2026 will be the year of AI ROI."

This systems-based approach to AI, agent layers and governance is why ServiceNow CEO Bill McDermott could hardly contain himself on the company's third quarter earnings call.

One product that's resonating with customers is ServiceNow's AI Control Tower. That interest highlights how agentic AI, various models and governance is more the story than AGI or whatever OpenAI's Sam Altman is cooking up this week.

ServiceNow's Amit Zavery, Chief Product and Operating Officer, said: "What we have done is we have integrated all the different systems out there to give you full visibility and control. It resonates instantly."

All of these comments sound very pragmatic. But there are already signs that this productivity is going to lead to fewer jobs. The job market may not be fire as much as it is no hire.

You don't need AGI for a white collar recession

First, let's start with the obvious. This week was where a lot of folks seemed to realize at once that we're in a white collar recession. To be realistic, the white collar job losses have been mounting for two years (you only need to ask someone who lost a gig and can't find one). And now the recent data points are adding up and it appears AI is taking jobs.

UPS cut 14,000 corporate jobs and 35,000 total. The disclosure, which was made on UPS's third quarter earnings call, boils down to AI and automation and the need to align headcount with volume declines.

Brian Dykes, CFO of UPS, said "we finished down nearly 34,000 positions year-over-year, which includes a reduction from our driver voluntary separation program. Nearly 1/3 of the reductions occurred in September."

UPS CEO Carol Tome noted that UPS is automating everything it can.

Amazon did its part to ding white collar jobs when it announced that it was cutting 14,000 corporate jobs. The rationale? AI requires less layers of management. Yes, most companies talk about augmenting human workers, but the reality is you just don't need as many people. Also see: AWS fires up Project Rainier, Trainium2 cluster for Anthropic

Other companies are either cutting white collar jobs (Target, GM) or holding the line on hiring (JPMorgan Chase). Alphabet said it is holding the line on headcount.

Alphabet CFO Anat Ashkenazi said generative AI is enabling the company to become more efficient across multiple fronts including headcount and infrastructure. "This is not a onetime effort but an ongoing way in which we operate the business," he said.

And this isn't just corporate America. At the Google Public Sector Summit in Washington DC, it was a common belief that AI agents were going to take on more work.

Ed Van Buren, an applied AI strategic growth offering leader at Deloitte, said upskilling will be critical. "Most federal agencies are smaller than they were last year. But still government has critical work that has to get done. It's going to be important for industry to help out a smaller government workforce. The Trump Administration is saying very directly that AI and emerging technologies are going to augment the existing remaining Federal workforce," said Van Buren.

A few thoughts:

  • Coming out of Constellation Research's Connected Enterprise agentic AI discussions, I was more optimistic about humans and their ability to find work. This week, I'm back to thinking we're going to need a lot less people to get work done. The point? The AI vs. human employment reality is going to ebb and flow as will your emotions. See: CCE 2025: AI agents: Dreams, reality and what's next

  • No government has an answer or even rough plan for these job losses. Something that stuck with me from both Constellation Research's AI Forum in Washington DC and Google Public Sector is that more will be done with less due to AI. Aside from vague talk of upskilling, retooling work and retraining humans there's no plan for dealing with the labor losses. AI is one reason why the economy is being revamped with a manufacturing spin, but once the construction on AI factories is done where's the work? Should manufacturing come back to the US, there will still be fewer people and more robots.
  • Ultimately, we get to a place where AI-enabled entrepreneurship will be rewarded. However, it's unclear whether everyone is suited to be an entrepreneur.
  • With any luck AI will be like previous technology shifts where humans adapt and new roles are created. The disruption in between will take years to play out.
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The Ghosts That Hold Us Back: How Leaders Can Get Out of Their Own Way | DisrupTV Ep. 416

The Ghosts That Hold Us Back: How Leaders Can Get Out of Their Own Way | DisrupTV Ep. 416

The Ghosts That Hold Us Back: How Leaders Can Get Out of Their Own Way | DisrupTV Ep. 416

In this episode of DisrupTV, co-hosts R “Ray” Wang, CEO of Constellation Research and best-selling author of Disrupting Digital Business, and Vala Afshar, Chief Digital Evangelist at Salesforce, sit down with two powerful voices reshaping the conversation around leadership and innovation:

  • Scott D. Anthony, author of Epic Disruptions: 11 Innovations That Shaped Our Modern World, and
  • Muriel Maignan Wilkins, author of Leadership Unblocked: Break Through the Beliefs that Limit Your Potential.

Together, they explore how innovation and leadership intersect—and why the key to unlocking the future often lies in looking inward.

From Florence Nightingale to AI: The Patterns of True Disruption

Scott D. Anthony’s Epic Disruptions traces the arc of innovation across history, from the printing press and gunpowder to AI and clean technology. One of his favorite examples? Florence Nightingale, whose pioneering work in data visualization, public health reform, and education redefined modern healthcare.

By using data to tell a story, Nightingale proved that ideas gain power when they connect emotionally and intellectually. Her ability to pair insight with communication sparked system-wide change—an essential lesson for today’s innovators.

  • “Florence Nightingale didn’t just collect data—she told a story that moved the world.”

Anthony argues that true disruptors act at a systemic level, combining vision with storytelling to drive enduring transformation. He also notes that the dynamics of disruption—whether in healthcare, energy, or AI—are remarkably consistent: bold ideas collide with entrenched systems, and progress depends on leaders who can manage that tension.

When “Disruption” Gets Misunderstood

Anthony also revisits the legacy of Clay Christensen, who first introduced the concept of disruptive innovation. He points out that many modern organizations misuse the term, labeling any change or new product as “disruptive.” This dilution of meaning matters because it leads to confusion—and often, misguided strategy.

Christensen himself might have seen technologies like generative AI as “new capabilities with new downsides,” a reminder that innovation is never purely positive. The leaders who succeed are those who stay grounded in reality—balancing vision with humility.

The Ghosts That Haunt Innovation

Transitioning from the external to the internal, Muriel Maignan Wilkins offers a complementary perspective: innovation doesn’t fail because of bad ideas—it fails because of blocked leaders.

In Leadership Unblocked, she identifies seven common beliefs that quietly sabotage potential, including:

  • “I need to be involved.”
  • “I can’t say no.”
  • “I know I’m right.”
  • “I can’t make mistakes.”

These beliefs often come from early experiences, past successes, or a leader’s “origin story.” Left unexamined, they become ghosts—the internal voices that keep people from evolving.

  • “Every organization has ghosts. The trick is learning which ones still serve you—and which ones are holding you back.”

The Power of Self-Awareness and Coaching

Wilkins emphasizes that the first step to unblocking leadership is self-awareness—recognizing your role in the obstacles you face. Leaders often blame external forces (the market, their teams, or technology) when the real issue lies in their own habits or fears.

Her process of recognize ? reframe ? rebuild helps leaders replace limiting beliefs with empowering ones. This mindset shift not only transforms individuals but also inspires the teams they lead.

Coaching, she adds, is a critical tool for this process. Great leaders, like legendary basketball coach John Wooden, lead through questions, curiosity, and compassion—not control. Effective leaders don’t just manage—they coach their people to see and solve problems differently.

Why Mindset Drives Innovation More Than Strategy

Both Anthony and Wilkins agree: the biggest disruptions don’t start with technology—they start with a mindset shift. From Florence Nightingale to the rise of AI, innovation requires courage, empathy, and adaptability.
And from legacy corporations to startups, leadership success now depends on self-reflection as much as on strategic skill.

  • “The hardest part of innovation isn’t the technology—it’s the mindset.”

As DisrupTV co-host R "Ray" Wang notes, today’s most transformative leaders are those who know when to pause, question assumptions, and unlearn outdated beliefs.

Key Takeaways

  • Disruption is systemic. The same forces that shaped the printing press and the compass are reshaping AI and clean tech today.
  • Self-awareness is innovation fuel. You can’t disrupt your industry if you’re stuck in your own beliefs.
  • Storytelling is power. As Florence Nightingale proved, data alone doesn’t change the world—stories do.
  • Leadership starts with unblocking. Before you can transform your organization, you must first transform yourself.

Final Thoughts: Innovation Starts Within

Leading through disruption isn’t about reacting to the latest trend or technology—it’s about developing the self-awareness and courage to evolve from within.

When leaders confront their hidden blockers and shift their mindset, they unlock the potential not only to adapt—but to define the next era of innovation.

Watch the full episode of DisrupTV to hear Scott D. Anthony and Muriel Maignan Wilkins discuss how to break through hidden blockers, master your mindset, and lead with purpose in an age of constant change.

Related Episodes

If you found Episode 416 valuable, here are a few others that align in theme or extend similar conversations:

 

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Google Public Sector Summit 2025: Takeaways on data, AI, ROI from 7 technology leaders

Google Public Sector Summit 2025: Takeaways on data, AI, ROI from 7 technology leaders

Public sector technology executives laid out a series of takeaways and best practices at the Google Public Sector Summit in Washington, D.C., in October. The takeaways ranged from focusing on your data foundation to use cases for artificial intelligence (AI) agents and the importance of training and human-in-the-loop processes.

Here’s a tour of what seven public sector technology leaders had to say (PDF).

More from Google Public Sector Summit 2025:

It's all about data first

Dr. Chrysoula Malogianni, Associate Vice President of Innovation at ODU: Your data is everything

Old Dominion University worked with Google Public Sector to launch MonarchSphere, a platform that weaves AI throughout ODU's student experience, research and operations.

Dr. Chrysoula Malogianni, Associate Vice President of Innovation at ODU

Malogianni said your AI success largely depends on your data. “Have a data plan. AI is not a catastrophe or a panacea. AI can’t do anything. You need robust data. You need infrastructure and a data foundation so you can validate AI. You need to also start preparing your target population for AI adoption. If we don't understand the AI, you won’t have a plan.”

She added that ODU put a lot of work into the data foundation along with Google Public Sector. Among the key assets:

  • 20 years of recorded courses and data that can be combined with real-time data from interactions
  • Notebooks for mind maps and course outlines to create assistants with the help of instructional designers
  • Data types from transcripts, advisers, and student interests
  • Combined course data and public data to enable students to create personalized journeys

And don’t forget the leadership. “It’s important to have visionary leadership, because transformation doesn’t start from technology. It starts from visionary leadership, appropriate partnership, and having a good plan,” said Malogianni.

See: Old Dominion, Google Public Sector Create AI Incubator

Matthew Gunkel, CIO, University of California, Riverside: Data Governance Matters

Matthew Gunkel, CIO at the University of California, Riverside, said on a panel at the Google Public Sector Summit that he has been working with Google Workspace and Gemini to speed up code transformations, surface unstructured data, and pull together different data sources.

“On the student success side, we really had an opportunity to look at where we can do advanced forecasting and modeling and then work to really use data governance. We’ve been putting data governance in place over the last 12 to 18 months to accelerate our ability to manage a resource-constrained environment. We have a lot of classroom challenges where we don’t have seats and we have too many students. How can we forecast and plan effectively and efficiently but also communicate that back to the degree programs?”

Gunkel said the data governance strategy has enabled the university to start to deploy Gemini agents on top of broad institutional data in Google Cloud BigQuery.

Data governance has been critical, because AI requires an organization to “manage information much more closely and frequently with purpose,” he added.

“AI really is worthless without the data behind it,” said Gunkel, who noted that the university is working closely with Google on Gemini use cases but needs the data strategy aligned.

Key items for UC Riverside include:

  • Improving enrollment support leveraging data and AI to inform students what degree programs are available
  • Semantic data mining to leverage unstructured information from transcripts
  • Automating transcript verification and using AI to deliver high-quality plans of action

Use Cases Abound

Ted Ross, CIO of the City of Los Angeles: Use Cases and Training Matter

The City of Los Angeles and Google Public Sector announced a partnership that layers Google Workspace with Gemini throughout the city’s transformation.

Speaking on a panel, Ross laid out some tips and best practices:

  • Use cases are not hard to find. Ross said that in government, use cases abound for areas where AI can improve efficiencies. Information dissemination and analysis are big ones. “In emergency management, AI has the ability to synthesize real-time information from utilities, cities, counties, states, and multiple jurisdictions,” said Ross. “This information also has to be multilingual.”
  • Ross added that it helps to think through AI use cases in terms of personas. “Think from the perspective of personas like the broad workforce, managers, front lines,” said Ross.
  • Don’t scrimp on training. “I’m a huge fan of training and giving employees an intro to AI,” said Ross, who added that the training and use of AI are critical to employee engagement. “AI is a once-in-a-generation shift of how people are computing, and you have to train the workforce so you can launch them into the future and build AI fluency. Make the investment in training now.”

From left to right: Ted Ross, CIO, City of Los Angeles; Kenneth Zellers, Commissioner, State of Missouri; Richard Smyth, Associate Vice President of Innovation and IT Services, Georgetown University; and Tony Orlando, Managing Director, Partner, and Specialty Sales, Google Public Sector.

Richard Smyth, Associate Vice President of Innovation and IT Services, Georgetown University: Student Lifecycle Management

Speaking on a panel, Smyth said Georgetown is leveraging AI and Gemini to improve the lifecycle of students through different phases, all the way to becoming alumni.

“What we want to do is make sure we can create an experience that allows students to succeed, successfully deliver on their program, but also to become strong alumni and to give back to the university as well as to the community,” Smyth said. “We think about the touchpoints that happen all the way through that journey. Historically, those systems and processes don’t interconnect, so we’re thinking of ways that we can use Gemini and Google Workspace to connect those processes and systems to create the ultimate experience.”

Key points from Smyth about Georgetown’s approach to AI:

  • “We’re focused on customer-centricity but also value.”
  • Georgetown needed to interact with a wide variety of departments and teams that engage the student community.
  • Training to use AI correctly was a necessity.
  • Solving pain points for individuals in various departments meant that those individuals bought into the AI plan.
  • Time was a big value driver. “We were going through this journey with the pilot with 60 to 80 people, and one of the things that came out was time savings. Over the course of the year, we could save $650,000 to $700,000, and that was just with the folks in the pilot program. Imagine if you scaled that across the university,” said Smyth.

Ultimately, Smyth said, the goal is to use that time savings to shift more of the focus to the student experience.

Kenneth J. Zellers, Commissioner of the State of Missouri: AI Enables Digital Services

The State of Missouri is focused on digital citizen services at the speed of business, said Zellers.

To become digital, Zellers said, AI has to be able to collapse silos. “We have 17 different departments, and our 6.2 million customers can go online and access any of them,” he said. “It’s not a facade. We’re using AI to move people to various portals. When the AI is good, it’s efficient.”

Zellers said there are multiple use cases across the state government. Here are a few:

  • Bill reviews where AI can speed up the compare-and-contrast process with previous versions: “You should still go back through it, but AI saves what used to take hours and hours and compresses them to a few minutes,” said Zellers.
  • Department of Revenue Answers (Dora), a chatbot that gives answers to taxpayers: There have been more than six million interactions.
  • Wrangling facility management, design, and construction projects: Zellers said the goal is to use AI and data from multiple assets and construction projects and become more predictive to generate savings.
  • Like other CxOs in the public sector, Zellers emphasized training and finding influencers who can lead others to use AI tools. He said the state focused on introductory AI training that included workers on every level.

“We invited people from senior level,” he said. “We invited people from frontline and administrative staff. But a lot of people forget that the administrative staff is in the middle. Sometimes they get left behind. Those are the true influencers. So, we had the initial training, and then we started getting calls because people go back to the department and talk about what they saw.”

Returns on Investment

Mansour Sharha, Innovation and Technology Director for the City of Dearborn: Build Trust, Drive ROI

Sharha said the City of Dearborn is one of the more diverse cities in the U.S., with multiple Middle Eastern countries and dialects represented.

The big use case for Dearborn was using Gemini to translate documents and assist residents. Instead of using a human agent to translate, Gemini was able to solve problems via a chatbot.

“When we started the model, we provided 10 to 15 questions that residents asked, and two years later, we’re actually addressing more than 75 questions,” said Sharha. “That provided a huge value for us in terms of staffing and efficiencies.”

Sharha said Dearborn can now use its call center for more complicated queries without adding more staff.

To deploy AI, Sharha said, he had to earn the trust of each department. “We started from the bottom up and really listened to the people who will be using the technology,” he said. “A lot of people are afraid of AI. It’s really about learning AI so you can be more efficient. We built that trust and focused as a team on providing [added value] to each department.”

Use cases include:

  • Using Gemini to translate documents into different languages for citizens.
  • Request-for-proposal (RFP) responses via AI. Sharha said Dearborn created a checklist of what an RFP should include, and now the process is automated at the front end. Humans do the evaluation once RFPs are culled from 30 to 50 down to a handful.
  • Planning and zoning commission requests are also sped up with AI, and the approval process has gone from four to five weeks to three to five days.

Looking ahead, Sharha said, Dearborn has plans to leverage AI for police and fire data and analytics.

Marcie Kahbody, Deputy Secretary of Technology and Agency Information Officer, California State Transportation Agency: Human in Loop

The California State Transportation Agency (Caltrans) has terabytes of siloed data that’s hard to get to but nevertheless a big asset. Caltrans collects data from 39,000 ground detectors, transportation management systems, and 3,000 cameras. The plan is to use that data to support the Caltrans vulnerable roadway users (VRU) plan, designed to end road fatalities and serious injuries on California roadways by 2050.

The agency started a pilot with Google Public Sector focused on wrangling the data and using it to improve safety. “We started with a sandbox so we don’t put any personal identifiable information (PII), and then we started sharing it with Gemini,” Kahbody said.

Caltrans conducted risk assessments, ran proofs of concept (POCs), and laid out needs with cybersecurity professionals, she added. Today Caltrans engineers and analysts can look at California Highway Patrol collision data to remediate high-collision areas in minutes, compared with a few days before.

"With VRU, it’s now just a click of a button to analyze,” she said.

However, you need humans in the loop in processes. “We always have a human in the loop. The engineer looks at the data closely to make sure that it’s valid and there’s no hallucination,” said Kahbody. “It’s saving us a lot of time and provides more time for our engineers to do the tasks that are more valuable.”

The project will move to production in January, she said. Reports will include recommendations to make roads safer, including better traffic signals, pedestrian lines, and areas that could be remediated. The returns will be measured in decreases in road fatalities and injuries.

Going forward, Caltrans will be launching a series of POCs and moving them into production. “We serve 23,000 employees,” said Kahbody. “Communication and change management is huge. We had a lot of communication with the unions, labor relations, and legal folks to understand that AI is not replacing anybody. Our strategy is providing tools to help engineers do a better job so they have more time to be strategic.”

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