
Most software executives plan custom AI agents to drive change
The report, titled Navigating Agentic AI & Generative AI in Software Development: Human-Agent Collaboration is Here , was commissioned by OutSystems in partnership with CIO Dive and KPMG. Surveying 550 global software executives, it explores how artificial intelligence is affecting the software development lifecycle (SDLC) and the workplace at large.
AI changing software development
With technology budgets scrutinised and outcomes under pressure, IT leaders are increasingly turning to agentic AI to address operational hurdles such as fragmented toolsets and siloed data. Businesses are reporting that agentic AI enables them to automate key workflows, offer more personalised digital services, and innovate rapidly - all while maintaining compliance, security, and governance standards.
Woodson Martin, Chief Executive Officer of OutSystems, commented, "The software development lifecycle is undergoing a significant transformation as organizations increase AI investments to maintain their competitive edge. Blending AI with development tools enables IT leaders to manage this shift effectively and securely. In a near future, AI agents acting as highly specialized teams will continuously monitor business needs, identify opportunities, and proactively refine software solutions, allowing developers and business leaders to play a more creative role and focus on strategic priorities. This report underscores how AI advancements are reshaping traditional roles and unlocking opportunities for innovation and collaboration between humans and technology."
Survey respondents highlighted concrete results from AI adoption: more than two thirds reported increased developer productivity and higher-quality software with fewer bugs. Additionally, 62% noted improved scalability in development, while 60% cited greater efficiency in testing and quality assurance (QA).
Impacts on the workforce
The report projects that experimentation with agentic AI and its uptake over the next 24 months will drive organisational change. According to the survey, 69% of software executives expect AI to introduce new, more specialised roles - including oversight, governance, prompt engineering, agent architecture, and agent orchestration - to adapt to AI's evolving function within companies.
Furthermore, 63% of respondents said AI will require considerable upskilling or reskilling of existing teams to meet the skills needed in this new landscape.
Where AI is being used
Almost half (46%) of executives report their organisations already integrate agentic AI into workflows, with a further 28% in the piloting stage. The most anticipated area for AI agent deployment is customer support, with 49% planning to use AI agents to handle customer inquiries and support functions autonomously.
The focus on customer service exceeds other domains such as product development (38%), sales and marketing (32%), supply chain management (28%), human resources (24%), and finance and accounting (23%).
Drivers and risks associated with AI
The primary goals for AI adoption, as expressed by over half the respondents, include improving customer experience (56%), automating repetitive tasks (55%), accelerating software development (54%), and advancing broader digital transformation objectives (53%).
However, the report also identifies significant challenges. 64% of software executives cited risks around governance, security, and compliance with widespread AI adoption. An equal proportion expressed concerns regarding transparency and reliability of AI decisions.
The proliferation of disparate AI tools has led to new issues with oversight and increasing technical debt, with 44% identifying AI sprawl as a growing risk. Addressing these burdens will be critical in ensuring AI's long-term value for business.
Building confidence in AI tools
Michael Harper, Managing Director at KPMG LLP, noted, "A lot of organizations started with pilots a year ago or even prior to that, but now they're starting to see real efficiency gains in areas like code generation and application testing. Those activities are giving organizations more confidence in using these tools and helping them to move forward."
The survey covered executives from a range of industries and geographies, including IT consultancy, manufacturing, banking, financial services, and insurance, with data collected across the United States, United Kingdom, Japan, France, Canada, Australia, India, and Germany.

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Otago Daily Times
6 hours ago
- Otago Daily Times
Air NZ sees AI use as 'force for good'
Air New Zealand is working with the organisation behind ChatGPT to expand the use of artificial intelligence to help the airline avoid flight delays. The national carrier was part of a select group around the world given the opportunity to partner with OpenAI, in a first of its kind collaboration in New Zealand. Air New Zealand chief digital officer Nikhil Ravishankar told RNZ's Morning Report programme today the partnership enabled Air New Zealand to roll companion AI out to its corporate workers at pace. It also allowed the airline to "co-create" solutions, Ravishankar said. "So we already have about 1500, what we call, custom GPTs in the organisation. Think of them as sort of rudimentary agents and what Open AI partnership allows us to do is work with their engineering teams and product teams to develop these solutions to solve airline problems, not just for Air New Zealand. "We're hoping that the solutions are also applicable around the world." It also allowed Air New Zealand to become a "test bed" for some of Open AI's more cutting-edge solutions, he said. "So we get first access, early access to some of these tools as they emerge and some of these tools are turning up on almost a weekly basis." The aim was to make Air New Zealand a better airline. Ravishankar said the airline expected to see improvements in on-time performance, integrated planning and how the airline scheduled the network it flies, and service experience for customers including product design in-flight and on the ground. "So almost every aspect of the customer's experience with the airline will be impacted by AI and this partnership going forward." Asked about pricing, Ravishankar said Air New Zealand was already using AI to deal with the cost of flying, which he said was complex. "The hope really is we want it to be a force for good so we are looking at utilising AI to drive more, fairer value-centric outcomes as much as anything else." Asked what this meant, Ravishankar said AI allowed the airline to take into account "a lot more things as we think about how we price an airline seat". "For our regional network for example where we are a lifeline service, we could think of pricing approaches that fulfil that role that we play, versus what we might be doing in say the US market where we're trying to attract premium leisure tourists into the country."


Techday NZ
8 hours ago
- Techday NZ
AI data centre market to reach USD $933.76 billion by 2030
The global artificial intelligence data centre market is projected to experience significant growth driven by the increasing demand for AI workloads, data explosion, and big data analytics. Research from MarketsandMarkets estimates that the worldwide AI data centre market will rise from USD $236.44 billion in 2025 to USD $933.76 billion by 2030, registering a compound annual growth rate (CAGR) of 31.6% throughout the forecast period. Market drivers This growth is primarily attributed to the surging demand for AI workloads across multiple industries, including healthcare, finance, and manufacturing. These sectors increasingly rely on high-performance computing infrastructures to manage complex algorithms and process large-scale data sets in real time. In response, companies are making investments in data centres optimised for artificial intelligence, which incorporate advanced hardware such as GPUs, TPUs, and storage systems capable of supporting intense computational loads. Such investments aim to address both the performance needs of AI tasks and the growing quantities of data generated by digital transformation initiatives. While demand is growing, the establishment of AI-centric data centres involves considerable costs. These include the need for specialised hardware, advanced cooling mechanisms to manage the substantial heat generated by high-performance servers, and a workforce with expertise in AI operations and maintenance. This cost barrier may prove significant, particularly for small and mid-sized enterprises looking to adopt AI infrastructure. An emerging trend within the segment is the adoption of environmentally sustainable practices. Organisations are implementing green AI data centres that focus on energy efficiency and reduced carbon footprints as part of efforts to address both cost and regulatory pressures related to sustainability. Compute servers lead The compute server segment is anticipated to command the highest market share in the AI data centre market during the forecast window. Compute servers play a crucial role in deploying AI workloads, especially in domains such as deep learning, natural language processing, and computer vision. These applications require substantial computational power for both training and inference, typically delivered by servers outfitted with GPUs, TPUs, or custom AI accelerators. These advanced servers enable parallel processing and real-time analysis of data, making them an integral component for enterprises deploying AI at scale. As the demand for automation, advanced analytics, and new digital services grows, the market for compute servers tailored for AI purposes continues to expand. Another important factor is the increasing use of AI in hyperscale data centres, which process large volumes of data on a continuous basis. To satisfy these computational requirements, compute servers are increasingly considered the backbone of modern AI data centre architectures. Their reliability and performance are central to the operation of scalable, high-capacity AI systems. Enterprise adoption increases The enterprise segment is projected to register the highest CAGR in the AI data centre market over the forecast period. This is attributed to the widespread digital transformation across industries and a growing emphasis on intelligent, data-driven decision-making for business processes. Enterprises are increasingly deploying AI workloads for a variety of applications, such as predictive maintenance, customer analytics, fraud detection, and automation of routine business tasks. To support these initiatives, many are investing in data centres that are specifically optimised for AI, offering the required computational power and scalability. While hyperscale technology providers have already established robust AI infrastructure, a transition is seen in enterprises actively developing or collaborating to build AI-focused data centre capacity in pursuit of maintaining competitiveness. Industry-specific examples include financial institutions leveraging AI data centres for real-time risk assessment, while healthcare organisations use them for expediting diagnostics and drug discovery. The AI data centre market is witnessing strong growth due to the rising demand for AI workloads across healthcare, finance, and manufacturing sectors. These workloads require high-performance computing infrastructure to process complex algorithms and large-scale data sets in real-time. Companies are investing in AI-optimized data centres that integrate GPUs, TPUs, and advanced storage systems to meet this demand. However, one main constraint is the high implementation cost associated with setting up AI-centric data centres, including investment in specialised hardware, advanced cooling systems, and skilled personnel. This can be a barrier, particularly for small and mid-sized enterprises. An emerging opportunity lies in the increasing adoption of green AI data centres as organisations focus on sustainability and energy efficiency. The growth of AI-as-a-Service (AIaaS) platforms is also granting mid-sized and smaller enterprises access to advanced AI technologies without requiring hefty upfront infrastructure investments. Additionally, government incentives targeted at promoting digital innovation and AI utilisation in sectors such as manufacturing and transportation are further encouraging enterprise spending on AI-capable data centres. With enterprises increasingly transitioning to hybrid cloud and edge computing models, the requirement for data centre infrastructure that is robust, secure, and energy-efficient is accelerating. These trends are expected to underpin the strong market growth expected in the coming years.


Techday NZ
8 hours ago
- Techday NZ
Motorola unveils 'AI nutrition labels' for safety technologies
Motorola Solutions has launched 'AI nutrition labels' aimed at providing greater transparency around the use of artificial intelligence in its safety and security products. The new labelling system will outline how artificial intelligence is deployed in each product, detailing the type of AI used, the ownership of the data processed, available human controls, and the intended purpose behind the technology's application. The company stated that this approach marks the first time such labels have been made available for public safety and enterprise security technologies, allowing users to better understand the 'ingredients' of each AI-enabled solution in a manner that draws comparison to food nutrition labelling. Mahesh Saptharishi, Executive Vice President and Chief Technology Officer at Motorola Solutions, emphasised the importance of clarity and transparency in the deployment of AI technologies in the context of safety and security. "It is our unwavering conviction that technology - including AI - is the bedrock for safety and security, and it must be deployed with purpose and transparency to fulfil its promise as a force for good," said Saptharishi. "Nutrition labels help describe AI's use in protecting neighbourhoods and nations, and we are proud to take a lead role in bringing greater transparency to AI innovation." The AI nutrition labels are designed to provide a summary of four core attributes relevant to users of these technologies: the particular type of AI employed, the party responsible for data ownership, the available mechanisms for human oversight, and the specific reason for integrating AI within a given product. AI capabilities have become increasingly integral to Motorola Solutions' safety and security portfolio. The company describes its AI as designed to assist users by providing accurate and timely information, supporting personnel in understanding unfolding events and prioritising their responses when confronted by complex safety threats. Saptharishi elaborated on the challenges faced by individuals charged with public safety, and how AI can augment their efforts, particularly in high-pressure scenarios. "Safety threats often unfold at a scale, speed and sophistication that can outstrip any one person's capacity to make sense of the situation," said Saptharishi. "AI can ingest, learn and cross-reference data to provide contextual understanding. At Motorola Solutions, we design our AI-enabled technologies to augment human focus, effort and performance when seconds matter most. Our AI nutrition labels will bring added clarity to the important role AI is playing in helping to protect people, property and places." Motorola Solutions confirmed the labels have been developed under the guidance of its Technology Advisory Committee, a cross-functional group tasked with providing ethical oversight and guidance regarding the design and implementation of new product technologies. The company positions the initiative as part of its ongoing commitment to support safer communities, educational institutions, and workplaces, by ensuring users and stakeholders can access straightforward information about the underpinning AI technologies and the safeguards in place. The company's announcement comes as the deployment of AI in public safety settings draws increased scrutiny from stakeholders, policy-makers, and the broader public, particularly regarding issues of privacy, human oversight and ethical boundaries. Motorola Solutions said the AI nutrition labels will now feature across its ecosystem of safety and security technologies, reflecting a shift towards increased transparency and engagement with the ethical aspects of AI deployment in critical environments.