DXC Technology has secured a major public-sector win, landing a 7+1+1-year contract to modernize the Metropolitan Police Service’s core business systems and resource deployment capabilities. Following a competitive tender, the Met selected DXC as its Master Vendor for BPO services, as well as ERP and Resource Management (RM) system replacement—marking one of the UK’s most significant policing transformation initiatives in recent years.

The project aims to overhaul how the Met manages people, processes, and technology across its HR, Commercial, and Finance functions. With more than 40,000 officers and staff serving London, the Met’s operational complexity demands real-time data, streamlined workflows, and scalable digital infrastructure. DXC’s mandate covers all three.

Real-Time Resource Planning for a Modern Police Force

By replacing legacy ERP and RM systems, DXC will help the Met better plan and allocate its resources using real-time insights. This shift is expected to improve responsiveness to local priorities, reduce administrative drag, and unlock meaningful cost savings by eliminating redundant processes and technology.

The initiative aligns with the Met’s “New Met for London” strategy, an ongoing program focused on restoring public trust, modernizing operational capabilities, and enabling officers to focus more time on frontline policing. The transformation also aims to strengthen cross-department collaboration and increase transparency in how budgets, assets, and personnel are managed.

Marie Heracleous, Chief Officer of Business Services at the Met, said DXC submitted the strongest bid: “DXC will help us better plan and manage our resources, modernise our technology, reduce cost and enable our officers to focus more on frontline policing.”

Backed by MOPAC and Designed for Long-Term Impact

The contract was signed between DXC and the Mayor’s Office for Policing and Crime (MOPAC), signalling a broader commitment to smarter, more efficient digital services across London’s policing ecosystem. MOPAC’s endorsement reinforces the project’s role in improving public outcomes, not just internal workflows.

For DXC, the deal reflects its growing footprint in public-sector modernization. “We are proud to partner with the Metropolitan Police Service on this mission-critical transformation,” said Derek Allison, UKI Managing Director at DXC. He highlighted the integration of Oracle Fusion SaaS, AI capabilities, and Strategic Workforce Management tools designed specifically for operational policing.

DXC says the project will deliver lasting benefits for both the Met and London communities, enabling a business service model built for transparency, efficiency, and long-term sustainability.

DXC’s Broader Public-Sector Track Record

The initiative builds on DXC’s experience supporting digital transformation across the UK’s public sector. The company recently earned recognition from IDC MarketScape as a Leader in Worldwide AI Services for National Civilian Government—an endorsement of its responsible AI approach, innovation depth, and sovereign public-sector capabilities.

For the Met, the partnership promises not just a technical upgrade but a foundational shift in how London’s largest public service organization operates. For DXC, it marks another strategic win in a sector where modernization, accountability, and operational efficiency are more critical than ever.

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PitchBook is bringing generative AI directly into the heart of private capital research. The company today announced PitchBook Navigator, a natural-language, AI-powered feature that lets users surface private market insights instantly through simple prompts inside the PitchBook Platform. Navigator will be available to subscribers in late November.

PitchBook also revealed an upcoming Model Context Protocol (MCP) integration with OpenAI, enabling subscribers to securely access PitchBook’s proprietary datasets directly within ChatGPT. Together, these launches push private market analytics into a new phase—one defined by trusted AI, faster research, and seamless cross-platform intelligence.

AI Needs Quality Data—PitchBook Claims It Has Both

“AI is only as powerful as the data and research behind it,” said Paul Jaeschke, Chief Product Officer at PitchBook. With Navigator and a growing network of LLM partnerships, the company aims to merge the speed of generative AI with the rigor of its proprietary data—long considered a gold standard in private markets.

Navigator uses natural-language queries to deliver insights across companies, deals, and market themes. It’s powered by PitchBook’s AI + HI (Artificial Intelligence + Human Insights) methodology, combining automated intelligence with human validation. The goal: responses that are fast, consistent, and anchored in verified data.

At launch, Navigator supports deal sourcing, due diligence, and market trend analysis. Over time, it will expand to cover PitchBook’s full dataset, research library, and IP portfolio. Early beta testers report faster workflows and clearer research summaries, especially for trends, summaries, and cross-market comparisons.

AI-Driven Insights, Verified at the Source

One of Navigator’s standout features is traceability. Users can review source links and underlying data references inside every response, a capability that beta testers say improves trust and simplifies verification—critical for investment teams operating under compliance constraints.

Testers also highlighted Navigator’s ability to break down queries by region, columns, or data type, offering structured, contextually intelligent outputs well beyond traditional search functions.

Bringing PitchBook Data Directly Into ChatGPT

PitchBook is also extending its enterprise AI strategy by integrating with OpenAI via MCP. Subscribers will soon be able to query PitchBook’s private market data securely inside ChatGPT, without switching tools or manually reconciling results.

The integration reflects a shared ambition: making high-quality, vetted data easily accessible in conversational AI environments. For financial professionals who increasingly rely on AI assistants for research, this could eliminate an entire layer of friction from daily workflows.

A Clear Signal of Where Private Market Intelligence Is Heading

Thomas Van Buskirk, EVP of Technology and Engineering at PitchBook, says the company’s two-decade investment in data integrity positions it well for an industry now racing toward AI adoption. PitchBook’s roadmap focuses on:

• AI-driven data collection to scale coverage with faster ingestion engines

• In-platform AI experiences including Navigator, summaries, predictive analytics, and workflow accelerators

• Strategic LLM partnerships ensuring trusted data surfaces wherever professionals work

In a market defined by speed, accuracy, and pressure to synthesize massive amounts of information, PitchBook’s moves suggest a clear direction: private market research will increasingly be conversational, integrated, and powered by verified AI.

Navigator’s launch and the OpenAI integration mark a significant step toward that future—one where data-driven decision-making moves from hours to seconds, and where trusted intelligence follows users across platforms.

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CallMiner just secured a notable position in the refreshed CMP Research Prism for Conversational IVR/Voicebot, earning recognition as a core performing provider. The designation reinforces CallMiner’s momentum in the fast-expanding market for AI-driven self-service, conversational voice automation, and customer experience (CX) intelligence.

The timing is strategic. The voice channel, long dependent on rigid touch-tone IVRs, is undergoing a major reboot. CMP Research notes that conversational AI, generative models, and emerging agentic capabilities are driving leaders to modernize their automated voice systems. And CallMiner sits squarely at the center of that shift.

Self-Service Demand Is Driving AI Investment

CallMiner’s own 2025 CX Landscape Report highlights the trend: 40% of senior CX and contact center leaders say AI’s biggest CX benefit is enabling customers to resolve issues independently. That aligns with rising expectations for fast, low-friction self-service—especially during periods of workforce strain and tight customer experience budgets.

CMP Research’s latest evaluation examines 20 voicebot vendors and positions CallMiner among those helping enterprises advance automation strategies while improving customer satisfaction. In a crowded field, this placement signals that CallMiner is delivering measurable value in real-world deployments.

OmniAgent Brings Voice-Optimized Automation to the Forefront

A major contributor to CallMiner’s performance is CallMiner OmniAgent, the company’s virtual agent solution built specifically for voice. The platform uses AI to automate omnichannel interactions with natural, human-like delivery. According to CallMiner, organizations using OmniAgent can reduce operational costs while improving the quality and consistency of customer engagements.

The real differentiator is its integration with CallMiner’s broader conversation intelligence platform. This pairing gives enterprises an end-to-end loop: identify which conversations to automate, deploy optimized flows, and continuously monitor automated interactions to refine accuracy and improve outcomes. It turns automation from a static deployment into a living system that learns.

CEO and founder Jeff Gallino says the companies that stand out in the automation wave will balance efficiency with customer experience. He argues CallMiner is already there, delivering “seamless, personalized automation” powered by insights extracted from real customer interactions.

A Framework Designed for CX Decision-Makers

CMP Research’s Prism is one of the few evaluation models built exclusively for customer contact and CX leaders. Updated twice a year, it reflects the latest market performance and technology advancements. For buyers navigating a crowded landscape, the Prism’s positioning helps distinguish vendors based on measurable capabilities, not marketing claims.

Nicole Kyle, Managing Director of CMP Research, says the framework exists to give decision-makers clear guidance during high-stakes technology evaluations. With AI voicebots accelerating in maturity and adoption, these assessments are becoming essential for risk-averse CX leaders planning long-term automation strategies.

The Bigger Market Signal

Recognition in the Prism suggests CallMiner is well-positioned as enterprises shift toward digital-first voice automation. Demand for conversational IVR and voicebots is rising quickly, driven by the need for efficiency, reduced wait times, and personalized self-service experiences. As AI models power more natural, accurate voice interactions, platforms like OmniAgent are becoming critical infrastructure for modern contact centers.

With AI reshaping expectations across CX, CallMiner’s growing influence indicates a broader industry pivot—one where conversation intelligence and automation aren’t just add-ons, but core pillars of customer engagement.

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Crest Data just landed a distinction held by only 0.1% of AWS Partners. The company has earned the AWS Cloud Operations Competency in Monitoring and Observability, a certification that validates its deep technical expertise in helping enterprises optimize performance across AWS environments. The announcement arrives alongside the launch of Crest Data’s Migration Acceleration Service for Amazon CloudWatch, now available in the AWS Marketplace.

This combination—elite certification plus a dedicated migration engine—marks a serious push to reshape enterprise observability strategies. It also positions Crest Data as a preferred partner for organizations looking to ditch legacy monitoring tools and consolidate operations on Amazon CloudWatch.

A Rare Certification That Signals Serious Expertise

Only a fraction of AWS Partners meet the performance bar for Cloud Operations Competency. Crest Data joins that small tier with a focus on monitoring and observability, two areas that have become essential for cloud-native performance.

The certification highlights years of collaboration with AWS, backed by significant hands-on migration experience. CEO Malhar Shah says the competency is a milestone that strengthens the company’s long-running partnership with AWS. For enterprises, it signals a more reliable path to managing modern, distributed applications on AWS without adding operational burden.

Solving the Pain Points of Observability Migration

Observability platform migrations rarely go smoothly. They’re slow, expensive, and intricately tied to compliance, workflows, and existing engineering practices. Platforms overlap for months during transitions, doubling cost and complexity. Crest Data’s new service attempts to cut through this by automating most of the heavy lifting.

The company claims its migration engine automates up to 90% of dashboard and alert conversions, reducing project timelines by 60%. With over 100 migrations completed, Crest Data’s consulting teams handle the remaining nuance—tag structures, field mappings, SLO alignment, and architectural refactoring—without derailing operations. Combined, the automation and expertise make migrations at least 60% more cost-effective than traditional approaches.

Early customers appear to back the claims. AML Partners reports that Crest Data helped achieve full observability coverage across customer application stacks through Amazon CloudWatch, strengthening reliability and SLO performance.

Unlocking Advanced CloudWatch Capabilities

Organizations moving to Amazon CloudWatch through Crest Data’s service can tap into a broader suite of modern features, including:

• Advanced metrics and alarms

• Cross-account and multi-region observability

• AI-driven anomaly detection

• Enhanced database observability

These capabilities matter as enterprises scale distributed systems and build AI-ready operations. Legacy observability tools often struggle with high cardinality, multi-region complexity, and cloud-native signal volume. CloudWatch’s newer feature set, paired with Crest Data’s automation, helps close those gaps at a more palatable cost.

The Bigger Impact on Cloud Observability

The combination of AWS competency and a new migration engine positions Crest Data as a strategic player in the cloud observability market. As more organizations face budget pressure and tool sprawl, consolidating onto CloudWatch becomes increasingly attractive. Crest Data’s offering is timed for that shift, promising faster migrations with less disruption and a clearer path to unified cloud monitoring.

Enterprises betting on AWS as their primary platform now have a partner capable of delivering observability modernization without the usual pain, cost, or technical drag. And in a market where platform sprawl slows innovation, that advantage is not small.

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OpenText just expanded its enterprise footprint—and its AI ambitions—by securing official certification for SAP S/4HANA Cloud Public Edition. With this move, OpenText becomes an SAP Solution Extensions partner offering a cloud-ready document management platform built to support SAP’s flagship Cloud ERP. For customers, that means deeper control, cleaner compliance, and faster digital workflows across increasingly complex environments.

It’s a strategic shift with real weight. Enterprises migrating to SAP Cloud ERP are demanding ways to connect their structured SAP data with the massive volume of unstructured content scattered across the business. Without that connection, AI initiatives stall, processes break, and decision-making suffers. OpenText wants to solve that — and do it natively inside the SAP ecosystem.

Why This Certification Matters

SAP Cloud ERP has become the operating backbone for organizations pursuing large-scale modernization. But process performance depends heavily on the ability to unify content and data. OpenText Core Content Management plugs directly into this need, offering governed, AI-ready content controls that extend SAP’s structured workflows.

The logic is simple: AI is only as good as the information feeding it. The more unified the content, the better the outcomes. With this certification, SAP customers gain a cloud-first layer of automation, transparency, and compliance designed for large, distributed enterprises.

SAP’s Darryl Gray underscored the point, calling the partnership “a catalyst for high-performance in the cloud ERP era.” His message is clear—real modernization requires content and process to move in lockstep, and the OpenText–SAP integration attempts to deliver that alignment at scale.

AI-Ready Information Starts With Unified Content

The companies argue that AI value collapses without deep access to reliable unstructured content. Emails, contracts, customer communications, recorded interactions—these assets shape context but rarely live in accessible, governed environments. OpenText wants to fix that by creating what its CMO Sandy Ono describes as a “unified view of all enterprise knowledge.”

In practice, that means surfacing content within SAP Cloud ERP to support planning, procurement, finance, supply chain, and every operational layer depending on consistent information. By removing silos, enterprises should gain cleaner insight pathways, stronger compliance controls, and fewer blind spots when deploying AI across mission-critical workflows.

What Customers Can Expect

With native integration comes several tangible upgrades:

• Automation at scale across document-heavy processes

• AI-ready content pipelines that unify structured and unstructured data

• Embedded compliance aligned with SAP Cloud ERP governance models

• Cloud-first agility that reduces integration work and operational overhead

For enterprises wrestling with fragmented content management, the offering provides something rare: a single, native path to govern information globally while preparing it for AI use cases.

The Bigger Picture

The certification signals where enterprise software is heading. ERP platforms may remain the system of record, but content platforms are quickly becoming the system of insight. As AI adoption accelerates, the pressure to unify data and content will rise, making partnerships like SAP and OpenText far more consequential than a typical product extension.

For now, OpenText’s certification gives SAP Cloud ERP customers a clearer route toward intelligent, compliant, and AI-enabled operations — without stitching together yet another integration layer.

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Enterprise marketers love talking about AI, automation, and cutting-edge martech. Yet a new survey suggests most of that hype falls apart at the foundation: the data feeding those tools. According to fresh research from Intermedia Global (IMG), only 2% of UK marketing leaders rate their data quality as strong and flowing cleanly through their martech stack. In other words, 98% are operating with data that slows them down—or worse, derails their ambitions.

The study, which surveyed 250 C-suite executives running marketing technology budgets within mid-sized UK enterprises (£100m–£500m in revenue), exposes a deep operational gap. Despite a decade of martech expansion, data remains the weakest link.

Data Inefficiencies Are Draining Budgets—and Patience

Marketers often blame slow performance on tools, teams, or budgets. Yet IMG’s findings point to something far simpler: poor data flow. And the consequences show up everywhere.

Nearly half of respondents waste time manually pulling reports. Forty-four percent say weak data slows learning cycles and triggers repeat mistakes. Even more concerning, 42% admit they lose budget because existing tech is underused or misused. Another 40% struggle with broken targeting and wasted media spend—a costly issue in a market where every click is scrutinised.

These pain points reflect a deeper structural problem. Martech stacks have grown rapidly, but integration rarely keeps pace. When the pipes are clogged, nothing downstream works as promised.

AI’s Value Hinges on Data Readiness

IMG’s data planning lead, Emily Crisp, points out that the problem isn’t a lack of awareness. In fact, 91% of CMOs say data quality directly affects campaign performance. What’s missing is action—and the discipline required to fix foundational issues before adding new technology.

Crisp also highlights a growing disconnect: brands are pouring money into AI tools while ignoring the data requirements those tools depend on. MIT’s recent findings show that 95% of companies have yet to see ROI from generative AI pilots. The issue isn’t AI—it’s the poor-quality data feeding it.

Tools powered by machine learning amplify whatever they ingest. If the inputs are messy, the outputs will be worse. In short, AI cannot rescue bad data. It only exposes it.

The Real Path Forward: Fix the Data, Not the Stack

The martech industry has long been obsessed with adding new platforms, integrations, and “next-gen” capabilities. IMG’s research is a blunt reminder that innovation without operational discipline rarely delivers value.

Crisp puts it plainly: improving data flow is the first step toward better performance. Ignoring it creates friction at every stage of the marketing lifecycle. Before CMOs chase new AI promises, they must address the fundamentals—governance, hygiene, enrichment, and cross-platform consistency.

It may not be glamorous, but it is transformative. Strong data turns existing martech into high-performing assets. Weak data turns even the most advanced tools into expensive clutter.

For marketing teams under pressure to prove ROI, this is the wake-up call. AI will not fix the martech ecosystem. But clean, efficient data just might.

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Landbase, the agentic AI startup reshaping how businesses identify, target, and engage customers, has been named to Gartner’s 2025 Cool Vendors in AI for Marketing list. The recognition highlights emerging companies delivering breakthrough applications of AI, especially those redefining how marketing and GTM teams operate.

The announcement marks a major milestone for Landbase, which has rapidly positioned itself as a new category leader in AI-driven audience intelligence and go-to-market execution.

A New Model: Turning Natural Language Into GTM Precision

Landbase built the first agentic AI platform for go-to-market, enabling teams to define ideal customers or markets using plain language. Instead of stitching together datasets, tools, and spreadsheets, users simply describe their intent. Landbase then instantly generates precise, high-fit audiences.

After creating these audiences, the platform’s AI agents enrich, validate, and activate them across channels—email, phone, and social—so campaigns begin with relevance instead of guesswork. The approach compresses what used to take days into minutes, giving marketing and sales teams a shared intelligence layer that keeps GTM motions aligned.

Leading B2B enterprises and fast-growing startups rely on Landbase to uncover new markets, surface hidden demand, and connect siloed workflows around a single view of audience fit.

“Agentic AI Is the Next Wave of Business Growth”

Landbase CEO and co-founder Daniel Saks called Gartner’s recognition a validation of the company’s core thesis: agentic AI and domain-specific models will define the future of business growth.

Enterprises may be swimming in data, Saks noted, but converting fragmented information into actionable insights still slows GTM teams. Landbase addresses that by pairing its proprietary GTM Omni models with agentic data and search workflows. The result is instant, high-accuracy predictions of who companies should target next.

“It’s about turning complex and fragmented data into simple, actionable intelligence that drives real outcomes,” Saks said.

A Platform Built for Speed, Accuracy, and Alignment

Landbase’s traction comes at a time when precision targeting has become essential. Budgets are under pressure. Conversion cycles are longer. GTM teams need more signal and less noise.

The GTM Omni model that powers the platform continuously learns from millions of real-world interactions. This improves prediction accuracy and audience quality over time, helping companies focus effort where conversion likelihood is highest.

Recent platform enhancements include:

• More powerful natural-language querying for audience exploration

• Audience validation tools to refine high-fit segments

• Collaborative AI agents that simulate GTM roles and streamline execution across targeting, qualification, and outreach

These capabilities reduce wasted spend and give teams a clearer path to measurable pipeline impact. As companies look beyond static databases and manual research, Landbase offers an adaptable system built for speed and intelligence—not spreadsheets and guesswork.

Landbase Emerges as a New Standard for AI-Driven GTM

The Gartner Cool Vendor nod reflects a broader shift in the market. Go-to-market teams now compete on accuracy, agility, and integrated intelligence. Landbase is positioning itself as the platform that brings all three together through agentic AI.

With its natural-language interface, predictive models, and cross-channel activation engine, the company is creating a new blueprint for modern audience strategy—one where GTM teams move faster, collaborate seamlessly, and engage only the buyers that matter.

As organizations push for smarter growth with fewer resources, Landbase is quickly becoming a foundational tool for AI-powered audience definition and execution.

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ArcadianAI is stepping into the center of the security world with a blunt message: the traditional monitoring model is breaking. Security operations across the U.S. and Canada pour millions into control rooms, staff, and infrastructure, yet human operators still miss incidents and burn out. Most camera feeds show nothing for hours, while the workload keeps rising. ArcadianAI says it has the answer—AI employees that work like trained guards but at machine scale.

Ranger: An AI Guard Built for Real-World Security Demands

The company’s flagship platform, Ranger, connects directly to existing CCTV and NVR systems, acting as a fully autonomous digital security guard. Ranger does more than detect motion. It thinks like a human operator, reads context, and understands which events matter.

It recognizes weapons, break-ins, theft, fights, fires, accidents, vandalism, loitering, and suspicious vehicles. Each incident is analyzed, scored, and sent to operators with location context and recommended actions. Instead of endless noise, monitoring teams receive filtered, high-value decisions.

Ben Kavousi, Vice President of Operations at Virtual Security Concierge, put it plainly: “One AI guard can monitor more than ten thousand cameras for less than one human operator watching sixteen.”

Scaling Security Without Scaling Labor

Human monitoring hits the same wall every time: more cameras require more people. That equation doesn’t scale. Ranger flips the model. It never loses focus, gets tired, or misses a detail. It watches thousands of feeds across multiple sites in real time. No hiring waves, no training cycles, no new control rooms. Just cameras and AI.

This shift is especially relevant as monitoring centers face tighter margins and rising contract demands. Ranger functions as a workforce multiplier—an AI teammate filling the gap between growing workloads and limited personnel.

Cutting Costs While Improving Coverage

Labor remains the largest cost driver in video monitoring. It’s also where most missed incidents originate. Ranger automates up to 95 percent of manual monitoring work, reducing false alarms and expanding coverage. It delivers continuous situational awareness across every camera feed, something no human team can match.

This moves monitoring companies from labor-heavy operations to lean, intelligent systems capable of scaling without burnout or quality loss. Ranger isn’t positioned as a software tool. It’s marketed as a true AI teammate.

Built for the Modern Monitoring Center

ArcadianAI designed Ranger as an open, flexible platform compatible with the tools monitoring centers already use.

Key integrations and capabilities include:

• Support for RTSP, ONVIF, SIP, H264, and H265

• Compatibility with 3,000+ camera models, including Hanwha, Axis, Hikvision, Avigilon, and Pelco

• Integrations with Immix, Sureview, DW Spectrum, and Brivo

• Cloud or hybrid edge deployment with no proprietary hardware

• REST API, Webhooks, and MQTT for VMS and PSIM workflows

• Built-in encryption, audit logs, and GDPR, CCPA, and SOC 2 compliance

• Modular AI policies for residential, commercial, industrial, and education sites

This makes Ranger a drop-in AI layer for existing infrastructure rather than a full-system replacement—one of the fastest paths to modernization for centers that can’t afford downtime.

AI as a Teammate, Not a Replacement

ArcadianAI frames its technology as collaborative, not disruptive. Founder Marie Roohi captured that sentiment: “AI is not the end of security guards. It is their strongest teammate.”

For an industry grappling with labor shortages, rising costs, and expanding camera networks, Ranger marks a clear shift toward an AI-first model—one that addresses the realities of today’s monitoring landscape without discarding human expertise.

ArcadianAI believes the future of security isn’t fewer guards. It’s smarter systems, stronger teams, and AI support that scales where people can’t.

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