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94% of Developers Report AI Productivity Gains, but Governance Maturity Lags Behind Adoption, Finds New Study From Info-Tech Research Group

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Research findings published in Info-Tech Research Group’s newly released AI Adoption and Impact Study indicate that AI is becoming embedded across the software development lifecycle, with 94% of developers reporting productivity gains and 83% reporting meaningful defect reduction. However, the firm’s study also shows that AI-generated code requires more testing, that security concerns remain a leading barrier to adoption, and that legacy code continues to limit AI’s effectiveness in development workflows. 

ARLINGTON, Va., July 20, 2026 /PRNewswire/ — New research data from Info-Tech Research Group shows that while AI is changing how software is planned, built, tested, and delivered, many organizations are still working to align governance, security, and review practices with the rapid pace of adoption. The global IT research and advisory firm’s recently published report, AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights, examines how applications, engineering, and product leaders are using AI across the software development lifecycle and where adoption is delivering measurable value or creating new execution risks.

Based on 578 completed survey responses from leaders in Applications, Engineering, and Product who are actively adopting AI across the software development lifecycle (SDLC), Info-Tech’s report finds that 84% of respondents use AI in the Build phase, applying it to tasks such as analysis, design, development, and testing. At the same time, 67% of developers agree that AI-generated code requires more testing than human-generated code, which highlights a central tension for software leaders: AI is improving productivity and quality outcomes, but the practices needed to review, govern, and secure AI-assisted development are not yet keeping pace.

“AI is no longer sitting at the edge of software development. It is becoming part of how teams build and deliver work every day,” says Brian Jackson, principal research director at Info-Tech Research Group. “The challenge for leaders is that faster code creation does not reduce the need for disciplined delivery. It increases the need for clear review standards, security guardrails, and stronger measurement so organizations can turn AI adoption into sustainable value rather than unmanaged risk.”

Info-Tech’s report shows that productivity gains are already widespread. According to the firm’s survey data, 94% of respondents report meaningful productivity improvements from AI, and organizations with higher AI maturity at the Build phase are more likely to report greater developer productivity. The findings also indicate that AI is helping improve quality, with 83% of respondents reporting meaningful defect reduction in shipped code, including 54% reporting a 2x reduction, 21% reporting a 5x reduction, and 7% reporting a 10x or greater reduction.

However, the report insights also reveal that adoption is advancing faster than process maturity. Among developers using AI at the Build stage, Info-Tech reports that only 37.4% describe their AI maturity level as formal or better. These findings suggest that without standard operating procedures, production-readiness criteria, and clear review requirements for AI-generated code, organizations risk inconsistent quality outcomes as AI adoption scales across teams.

Key Findings From Info-Tech’s AI in Software Development Study
Info-Tech’s AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights report identifies several findings that applications, engineering, and product leaders should consider as they expand AI use across the SDLC:

AI is widely used in the Build phase, but review requirements are increasing. The report finds that 84% of respondents use AI in the Build phase, while 67% agree that AI-generated code requires more testing than human-generated code. The findings indicate that AI may shift developer time from writing code to reviewing and validating code, even as overall efficiency improves.AI maturity remains behind adoption. The three most popular AI tools used in the Build stage are integrated development environment (IDE) plug-ins, AI chatbots, and agentic tools. Yet only 37.4% of developers using AI at the Build stage report formal procedures or better, suggesting that many teams are using AI in an ad hoc way without mature governance or review practices.Security and quality concerns continue to limit adoption. Across all organizations surveyed, security and IP concerns are the top AI adoption challenge at 48%, followed by output quality concerns at 42%, skills gaps or training at 34%, data readiness and quality at 26%, and poor tool integration at 24%. Security concerns are especially prominent in medium and large organizations.Legacy code remains the biggest workflow roadblock. The report finds that 51% of respondents say AI breaks on complex or legacy code, making it the most common personal roadblock to AI use in development workflows. AI code not passing quality gates follows at 46%, while inconsistent skill levels across teams are cited by 36.7% of respondents.Experience shapes how teams use and govern AI. Practitioners with more than 15 years of experience are nearly twice as likely to use AI security tools as those with 3 to 7 years of experience. They are also more likely to raise security or codebase context concerns, suggesting that experienced practitioners play an important role in applying AI more carefully.Developer roles are beginning to shift. Less experienced developers are more likely to report speed and efficiency gains from AI, while more veteran developers are more likely to report shifting their time toward reviewing and validating AI-generated code. Info-Tech’s findings suggest that leaders should ensure review processes include appropriately experienced staff before AI-generated outputs move into production.

The report also highlights a perception gap between product, engineering, and applications teams. When asked whether vibe coding with AI guarantees faster releases with zero errors, product respondents were the most likely to agree, while applications practitioners were the most skeptical. Info-Tech notes that this gap creates risk when delivery commitments are made before the teams responsible for quality have assessed the work.

“AI-assisted development can produce real gains, but the closer teams are to the code, the more they recognize the need for scrutiny,” explains Jackson. “Organizations should not treat AI as a shortcut around engineering discipline. They should use it as a reason to make security review, production-readiness standards, and quality measurement more explicit across the SDLC.”

Based on the data findings, Info-Tech recommends that software leaders focus on building practical governance around the highest-volume AI use cases first. This includes defining criteria for when AI-generated code is production-ready, establishing review expectations for security and quality, involving engineering and applications teams in AI adoption decisions, and tracking defects by category to understand where AI is improving outcomes and where human oversight remains critical.

The AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights report is part of Info-Tech’s ongoing AI Adoption and Impact Study. The research provides evidence-based data and expert analysis to help IT leaders understand how AI is affecting software development, where it is creating measurable benefits, and where barriers to integration and value delivery remain.

For exclusive and timely commentary from Info-Tech’s experts, including Brian Jackson, and access to the complete AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights report, please contact pr@infotech.com.

About Info-Tech Research Group
Info-Tech Research Group is the “get things done” partner for over 30,000 IT, HR, and marketing leaders worldwide. The fastest growing research and advisory firm, Info-Tech enables leaders to make well-informed decisions and transform their organizations through AI, strategic foresight, step-by-step methodologies, practical tools, industry-leading advisory, and training programs. For nearly 30 years, tens of thousands of private and public organizations have trusted Info-Tech to lead their most important initiatives through periods of change and deliver outcomes that truly matter.

To learn more about Info-Tech’s HR research and advisory services, visit McLean & Company, and for data-driven software buying insights and vendor evaluations, visit the firm’s SoftwareReviews platform.

Media professionals can register for unrestricted access to research across IT, HR, and software, and hundreds of industry analysts through the firm’s Media Insiders program. To gain access, contact pr@infotech.com.

For information about Info-Tech Research Group or to access the latest research, visit infotech.com and connect via LinkedIn and X.

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SOURCE Info-Tech Research Group

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Integrate Brings Governed Marketing Data Into AI Workflows B2B Teams Already Use

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Integrate MCP lets marketers ask business questions about their marketing campaigns, leads, spend, pipeline and ABM data, and act on the answers with the same permissions and approvals they trust in Integrate

BOULDER, Colo., Oct. 5, 2026 /PRNewswire-PRWeb/ — Integrate, a universal lead management and data governance platform for B2B marketing teams, today announced the availability of Integrate MCP, a connection that brings the Integrate platform directly into the AI workflows marketing teams already use. Built on the Model Context Protocol (MCP), the open standard for connecting AI tools to business systems, Integrate MCP is available today in most AI tools that can connect through MCP. Integrate MCP is part of the company’s broader headless Integrate initiative, which is designed to make the platform accessible and operable beyond the traditional user interface.

“Marketers don’t have a data problem, they have a distance problem,” said Mehul Nagrani, CEO of Integrate.

Go-to-market teams usually know the question they want answered. Getting the answer is the hard part: finding the right dashboard, building a saved report, remembering field names and filters, then going back into the platform to act on what they learned. Integrate MCP removes those steps. Users ask a question in business language and get an answer grounded in their governed Integrate data, with a link back into the platform where one applies.

Rather than introducing a separate AI assistant inside the Integrate platform, Integrate MCP connects the AI tools customers already use directly to Integrate. This approach gives leaders, sales and operations teammates a consistent way to access governed data and take action from their existing AI environment. Integrate remains the system of record and enforces the same permissions, data controls and approval requirements already in place. Because it is built on an open standard, Integrate MCP can reach more AI tools as customers adopt them.

“Where work gets done is going through the biggest shift since the web browser,” said Mehul Nagrani, CEO of Integrate. “Marketers don’t have a data problem, they have a distance problem. The answer is already in the platform, but getting to it means finding the right report, building the right filter, then logging back in to act on it. We didn’t want to add one more AI chatbot to the pile, with its own token tollgate. Your team already has one. Integrate MCP connects it to governed data, with the same permissions they have today.”

Answers Across the Platform, in Plain English

With Integrate MCP, customers can:

Quantify lead governance. See accepted and rejected leads, ranked rejection reasons and the savings each one produced.Diagnose delivery issues. Check integration success and failure rates and see exactly why a specific lead failed to post to a CRM or marketing automation system.Track spend and pipeline. See how spend, savings, governed leads and marketing-sourced pipeline by week, month or quarter.Compare channels and publishers. Review performance across content syndication, social, webinar and custom channels, get scored publisher recommendations for a campaign, and compare publishers head to head.Follow the ABM funnel. Track target accounts from Target to Closed/Won, spot buying-group and persona gaps, and see tied Salesforce opportunities.Measure conversion. Pull MQL and SQL conversion by partner, channel, quarter or contract list.Skip the saved report. Query report data directly, or open saved reports and download the full file.

Example questions to query the MCP include: “Why were leads rejected last quarter, and what did that save us?” “Which target accounts engaged recently but have buying-group gaps?” and “Why did leads fail to post to Salesforce yesterday?”

From Answer to Action

Integrate MCP also lets users make changes from the conversation, starting with sources. A marketer can ask the assistant to find the right sources and update them, such as pushing an end date. The assistant previews every proposed change, naming each source with its current and new values, and saves nothing until the user approves.

As AI assistants and agents increasingly interact with business systems on behalf of users, Integrate is building toward a model where customers can access governed data, make decisions and take action through the AI tools and workflows they already use, while Integrate remains the underlying system of record.

The Same Governance Customers Already Trust

Integrate MCP was designed so AI access doesn’t create a new governance problem:

Same permissions as the platform. Every request runs as the signed-in user, through their existing Integrate login. The assistant sees only what the user can see and does only what the user can do.Financial and personal data stay gated. Spend, savings and conversion data require financial access, and contact-level personal information is redacted for users without PII access.Account-scoped by design. Every question runs against a single named account, and the assistant never switches accounts without the user asking.Confirm before commit. Changes are previewed and require explicit approval before anything is saved.Admin control. Administrators can turn off MCP access entirely, or keep read access on while turning off the ability to make changes.

Availability

Integrate MCP is available now to Integrate customers in most AI tools that can connect through MCP. Integrate plans to extend support to additional MCP-compatible AI tools and to expand the actions users can take from the conversation over time. To learn more, visit https://www.integrate.com/demo/ or contact your Integrate account team.

About Integrate

Integrate is the pipeline integrity platform for B2B marketing and revenue teams. It sits between every external demand source and your MAP/CRM as a single, governed intake and orchestration layer that automates and standardizes lead flow to your revenue engine. Across content syndication, events, paid digital, partners, social, webinars, list uploads and forms, Integrate validates, enriches, de-duplicates, and applies your consent, privacy and quality rules before any record reaches downstream systems. The result is clean data, faster action and pipeline that converts.

Media Contact

Keith Wiley, Integrate, 1 4152034255, keith@metzpr.com, www.integrate.com

View original content:https://www.prweb.com/releases/integrate-brings-governed-marketing-data-into-ai-workflows-b2b-teams-already-use-302898629.html

SOURCE Integrate

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MN ETF Seeks to Give Investors Access to OpenAI and Anthropic

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SAN FRANCISCO, Oct. 5, 2026 /PRNewswire/ — Corgi Invest today announced the launch of the MN (Corgi MANGOS ETF), an actively managed exchange-traded fund seeking to provide investors exposure to OpenAI and Anthropic, two private companies in artificial intelligence, alongside Meta Platforms, NVIDIA, Alphabet (Google), and SpaceX. MANGOS is an acronym formed from the first letter of each of the six companies’ names.

“For years, exposure to OpenAI and Anthropic has been reserved for venture investors and insiders,” said Jeff Weniger, Chief Investment Strategist, Corgi Invest. “MN puts that exposure inside a wrapper investors already know how to use a standard, exchange-traded fund, bought and sold like any other ETF, with no lockups and no accreditation requirement.” Shares may trade at a premium or discount to NAV and may have limited liquidity.

Targeted Access to OpenAI and Anthropic

OpenAI and Anthropic are companies in AI, yet neither is publicly traded, ordinarily putting them out of reach for everyday investors. MN is designed to close that gap: within a standard, exchange-traded structure, with no accreditation requirement and no private-fund lockup, the Fund seeks exposure to both companies through cash-settled total return swaps1, rather than direct share purchases.

These private-company swaps2 provide one-for-one exposure, with no leveraged return multiplier3, and are initially priced with reference to perpetual futures contracts4 linked to the respective companies. Combined exposure to OpenAI and Anthropic is limited to 15% of the Fund’s net assets at the time of investment, consistent with the Fund’s liquidity risk
management program.

Fund Overview

Beyond its OpenAI and Anthropic exposure, the Fund seeks capital appreciation by investing, under normal market conditions, at least 80% of its net assets in equity securities of all six MANGOS companies and in financial instruments including total return swaps that provide economic exposure to those companies’ equity value.

The Fund offers this exposure through a standard exchange-traded structure, investors can buy and sell shares through a brokerage account like any other ETF. MN carries a total annual operating expense ratio of 0.20%.

About the MN Companies

The Fund seeks to give exposure to six companies: Anthropic, a privately held AI safety company and developer of the Claude family of AI models; OpenAI, a privately held developer of ChatGPT and frontier AI research; Meta Platforms (social media and virtual/augmented reality); NVIDIA (GPUs and AI computing infrastructure); Alphabet/Google (search, cloud, and AI); and SpaceX (launch services, Starlink, and, through its ownership of xAI and X, AI and social media). Portfolio weightings are determined through active management rather than index replication.

Availability

MN began trading on Cboe BZX Exchange on October 2, 2026, and is available through brokerage accounts nationwide.

About Corgi Invest

Corgi Strategies, LLC is an SEC-registered investment adviser founded in 2025. As of June 30, 2026, the firm managed approximately $821 million in assets. Corgi Invest builds actively managed exchange-traded funds designed to give everyday investors access to concentrated, high-conviction themes.

Media Contact
Corgi Invest
operations@founderledfunds.com

Important Disclosures 

Investors should consider the investment objectives, risks, charges, and expenses of the Fund carefully before investing. This and other information is contained in the Fund’s prospectus, which should be read carefully before investing. Shares are bought and sold at market price, not NAV, and are not individually redeemable from the Fund.

Investing involves risk, including possible loss of principal. Anthropic and OpenAI are privately held companies for which substantially less public information is available; the Fund’s exposure to these companies through swaps involves counterparty, valuation, liquidity, and pricing risks, including reliance on perpetual futures reference pricing that may differ materially from the companies’ actual value, and is limited to 15% of net assets. The Fund is non-diversified and concentrates its investments in six companies and the related industries in which they operate, which may make the Fund more volatile than a more broadly diversified fund. The Fund is newly organized and has no operating history. See the prospectus for a complete description of principal risks.

This press release is not an offer to sell or a solicitation of an offer to buy shares of the Fund, and is not a prospectus. The Fund’s registration statement, including its prospectus, has been filed with the SEC:
https://www.sec.gov/Archives/edgar/data/2078265/000207826526000415/cik0002078265-2026 0929.htm. Shares are not FDIC-insured, may lose value, and have no bank guarantee.

This release contains forward-looking statements regarding the Fund and the companies to which it has exposure. Actual results may differ materially from those expressed or implied.

Corgi ETF Trust I. Distributed by Paralel Distributors LLC, member FINRA.

Definitions

1. Total return swaps: Contracts under which the Fund receives an investment’s gains and income, pays its losses and typically pays financing costs, without owning the investment directly.

2. Private-company swaps: Total return swaps linked to companies whose shares are not publicly traded.

3. One-for-one exposure, with no leveraged return multiplier: The swap is designed to reflect the referenced investment’s gains or losses at a 1:1 rate, before fees and costs, without magnifying them. The Fund’s overall return may differ.

4. Perpetual futures contracts: Contracts with no fixed expiration date that use periodic payments between traders to help keep their prices aligned with a referenced asset. Their prices may differ from its actual value.

View original content to download multimedia:https://www.prnewswire.com/news-releases/mn-etf-seeks-to-give-investors-access-to-openai-and-anthropic-302898634.html

SOURCE Corgi Strategies, LLC

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Work4Flow and LogicMonitor Deepen Edwin AI Investigations in ServiceNow with New Agent-to-Agent Capabilities

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Work4Flow’s ServiceNow and agentic AI engineering expertise supports new Agent-to-Agent (A2A) capabilities connecting LogicMonitor observability intelligence with the ServiceNow AI Platform

CUPERTINO, Calif., Oct. 5, 2026 /PRNewswire/ — Work4Flow, solving the Last-Mile Challenges of Agentic AI Adoption, today announced its collaboration with LogicMonitor, the AI-first platform for Autonomous IT, to develop new Agent-to-Agent (A2A) capabilities that extend LogicMonitor’s Edwin AI intelligence into ServiceNow.

The initiative combines LogicMonitor’s observability intelligence and Edwin AI capabilities with Work4Flow’s expertise in ServiceNow architecture, integrations and agentic AI. The resulting A2A experience brings Edwin AI’s event correlation and AI-powered investigation into ServiceNow, enriching incidents with operational intelligence as they unfold.

Connecting Intelligence with the Customer Experience
IT operations teams increasingly rely on multiple enterprise platforms to investigate and resolve technology issues. LogicMonitor provides the operational intelligence enterprises need as they move toward Autonomous IT, and many LogicMonitor customers use ServiceNow to manage incidents and operational processes. The opportunity is to bring that intelligence directly into the environment where teams are managing the response.

Edwin AI provides operational intelligence by correlating signals across the IT environment and using LogicMonitor’s ITOps Context Graph to help teams understand relationships, impact, root cause and recommended next steps. LogicMonitor sought to make that intelligence more directly accessible , allowing customers to use relevant operational context as part of an investigation without requiring that intelligence to be recreated within ServiceNow.

Engineering the A2A Connection
Working alongside LogicMonitor’s product and engineering teams, Work4Flow brought specialized expertise across ServiceNow architecture, agentic AI, integrations and implementation to bring the A2A experience to market, enabling ServiceNow AI agentsto request specific Edwin AI capabilities as needed.

At the center of the experience are two core use cases: event correlation and AI-powered investigation. Edwin AI correlates signals across the IT environment to understand the broader operational issue, then supports deeper investigation through operational metrics, root cause analysis and diagnostic guidance. Through A2A, that intelligence can feed directly into and enrich the ServiceNow incident as it unfolds, giving responders and AI-driven workflows better context for determining what comes next.

Underpinning those use cases are six Edwin AI capabilities: Alert Metrics, Insight Metrics, Root Cause Analysis, Alert Summary, Insight Summary, and Diagnosis Guidance. Rather than receiving a fixed package of information, ServiceNow can call on these capabilities as an investigation progresses, drawing on correlated event context early in an incident, supporting metrics as more evidence is needed, and root cause analysis or diagnostic guidance as the investigation deepens.

LogicMonitor remains the source of the underlying observability intelligence and product experience, while A2A provides a structured way to bring that specialized intelligence into ServiceNow workflows, creating a tighter connection between investigation and action.

Building the A2A Experience Together
Work4Flow brought specialized expertise across ServiceNow architecture, agentic AI and implementation, working alongside LogicMonitor’s product and engineering teams to bring the A2A experience to market. The collaboration builds on an existing relationship supporting LogicMonitor customers operating ServiceNow environments.

“The real value of AI agents comes from giving them access to the right intelligence at the right moment,” said Karthik Sj, Chief AI Officer, LogicMonitor. “With A2A, Edwin AI can bring LogicMonitor’s observability context, from metrics to root cause analysis, directly into ServiceNow as an investigation unfolds. Work4Flow brought the ServiceNow and agentic AI expertise that helped us turn that vision into a production-ready experience for customers.”

Why LogicMonitor Was the Right Project for Work4Flow
For Work4Flow, the engagement reflects the company’s broader focus on helping technology providers make their existing products and intelligence accessible to enterprise AI.

LogicMonitor presented a strong use case: an established technology platform with advanced observability intelligence, a clear customer requirement, and an opportunity to extend that intelligence into ServiceNow through agent-to-agent interaction.

“LogicMonitor trusted Work4Flow’s growing ServiceNow and agentic AI capabilities to bring Edwin AI into the ServiceNow ecosystem,” said Sanjay K. Gupta, Founder and CEO of Work4Flow. “Our team combined deep ServiceNow expertise with agentic AI engineering to build the A2A integration connecting ServiceNow AI agents with Edwin AI. As enterprise AI moves toward multi-agent orchestration, secure and governed AI, and production-scale adoption, this collaboration demonstrates how A2A can connect specialized AI capabilities across platforms, extending the value of Edwin AI and delivering scalable, measurable business outcomes and greater ROI for joint customers.”

Extending the Value of Edwin AI for ServiceNow Customers
For organizations using both LogicMonitor and ServiceNow, incidents can remain within the ServiceNow workflow while Edwin AI supplies the specialized operational intelligence needed to understand what is happening, why it is happening and what to investigate next. As agent-driven workflows become more capable, access to specialized domain intelligence like Edwin AI will become increasingly important to what those agents can accomplish.

“At Bell Techlogix, we’re building toward an operating model where AI takes on more of the repeatable work and our people can focus on improving the processes around it,” said Tim Wheeler, Chief AI Officer, Bell Techlogix. “Bringing Edwin AI’s operational intelligence into ServiceNow is a natural extension of that strategy. As agent-to-agent capabilities mature, we see an opportunity to give teams better context at the point of action, automate more of the Tier 1 workload, and scale more intelligent IT operations without scaling resources at the same rate.”

About Work4Flow
Work4Flow is a ServiceNow-focused agentic AI implementation and engineering company helping technology providers and enterprises design, build, and deploy AI solutions across the ServiceNow ecosystem. Work4Flow specializes in agentic AI development, custom integrations, applications, Skills, accelerators, and enterprise AI enablement.

Learn more: www.work4flow.com

About LogicMonitor
LogicMonitor® is the AI-first platform for Autonomous IT, enabling enterprises to operate complex digital systems with greater resilience, efficiency, and confidence. By unifying visibility from user to code across infrastructure, cloud, Internet, and digital experience, LogicMonitor delivers the intelligence required to anticipate issues, eliminate blind spots, and take action automatically. Powered by Edwin AI, LogicMonitor helps IT and business leaders reduce operational toil, protect revenue, and accelerate innovation.

For more information, visit www.logicmonitor.com and our blog, or follow us on LinkedIn, X, Facebook, and YouTube.

Media Contacts
Work4Flow
Sanjay K. Gupta
408-839-2810
contact@work4flow.com

LogicMonitor
Paige Thornton
press@logicmonitor.com

ServiceNow, the ServiceNow logo, and other ServiceNow marks are trademarks and/or registered trademarks of ServiceNow, Inc. in the United States and/or other countries.

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SOURCE Work4Flow Inc

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