CIO CIO

Overview In this episode, we’re joined by Kenneth Lindegaard, the visionary CIO of Space42. Kenneth brings a unique blend of strategic insight and hands-on expertise to the table.As CIO of Space42, Kenneth is at the forefront of integrating cutting-edge technologies such as AI, satellite communications, and geospatial insights. His mission is to optimize these technologies for both operational efficiency and customer satisfaction, driving digital transformation that enhances business outcomes. Join us as we delve into Kenneth’s approach to balancing innovation with operational demands, and explore how he is shaping the future of IT in the Middle East. Register Now source

Deployment: Automating the LLM operations lifecycle Keep in mind that everything surrounding artificial intelligence and agentic AI is still evolving. We are seeing models being released faster, which introduces model management activities that we didn’t have to manage previously. Tooling is evolving and new frameworks are being released that make processes easier and more streamlined and that can reduce technical debt. You need to ensure your AI solution evolves as well. You will need to iterate your solutions more frequently than you would have with your traditional non-AI solutions. You also need to ensure that you have a versioning strategy to keep up with modifications and new features.  If you aren’t planning updates, with a versioning strategy, as well as updating the iterative tests, your AI system will become obsolete. This can cause unreliability and it becomes a technical debt that you will struggle to maintain.  The benefits of fully automating the LLM operations lifecycle to enhance efficiency, consistency and reliability, while also supporting continuous improvement, cost-effectiveness and compliance far outweigh the cost.  Agentic AI solutions have immense potential for businesses seeking to automate tasks, enhance efficiency and incorporate the benefits of agentic AI. But if you aren’t deploying, testing, monitoring and automating the process it doesn’t matter how good your solution is or what the potential could have been.  In this article, we have covered the processes around agentic AI DevOps but I want you to take away five things that you should ensure are your foundational baseline required as the basis for every successful implementation:  Automate, automate, automate: Automate tasks, create automation pipelines, automate testing, automate evaluations, automate the deployment of monitoring.  Deploy to containers and virtual environments: Run solutions in Docker containers to isolate the agents and constrain their access.  Restrict access: Limit the agents’ access to resources, and to the internet, as well as data repositories to prevent unauthorized access or data oversharing.  Monitor: Monitor output logs, performance logs and custom metrics during and after execution to identify issues that require human review. Create and compare against the baseline to identify and easily identify unintended behavior.  Human oversight: Run tests with humans in the loop to supervise the agents and ensure that you have included all scenarios that will require human intervention.  Fully automating the LLM operations lifecycle will enhance efficiency, consistency and reliability, while also supporting continuous improvement, cost-effectiveness and compliance.  Stephen Kaufman serves as a chief architect in the Microsoft Customer Success Unit Office of the CTO focusing on AI and cloud computing. He brings more than 30 years of experience across some of the largest enterprise customers, helping them understand and utilize AI ranging from initial concepts to specific application architectures, design, development and delivery.  This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects. source

source

00:00 Maryfran Johnson 0:00Hello. Good afternoon and welcome to CIO Leadership Live. I’m your host, Maryfran Johnson, the CEO of Maryfran Johnson media and the former editor in chief of CIO magazine. Since November 2017 this video and audio podcast has been produced by the editors of CIO.com and the digital media division of Foundry, which is an IDG company, our growing library of past interviews, all of them openly available on both cio.com and CIOs. YouTube channel includes more than 150 chief information technology and digital officers from mid sized to large companies across every industry joining that esteemed lineup of CIOs today is a long time friend of the family who has been interviewed in CIO magazine and on ci.com a number of times over the years. Richard Wiedenbeck, he’s the chief AI officer at Ameritas, based in Lincoln, Nebraska. Ameritas is a mutual based financial services company with annual revenues of 3.4 billion. It serves some 6 million customers, many of them in the small to mid sized business space, and it serves them with a broad array of life annuities, retirement, disability, dental and vision insurance plans. Rich has worked in business and Senior Technology roles for more than three decades across multiple industries, including defense, manufacturing, consulting and software. He joined Ameritas in 2010 as the vice president of it, moving up into the CIOs chair in 2013 in 2020, he was inducted into our CIO Hall of Fame, which every year honors an elite group of outstanding business technology leaders. Then last year, in January of 2024, rich joined yet another elite group of leaders who hold the newly minted and still relatively rare title of Chief AI officer. According to our 2025 state of the CIO survey, only 14% of mid sized to large companies have caios, and another 21% of companies are out there actively looking to hire one the responsibilities of this emerging CI C suite role which are being covered by a lot of our cio.com reporters these days. Those responsibilities range from setting a company’s overall ai ai strategy and overseeing how and where the AI tech is being used to developing an AI skilled workforce and to establishing a new enterprise governance that integrates with existing corporate cultures. It is no small task, as you’re going to hear about during this conversation with rich, and there’s some really great expectations around this role. So we have a lot to talk about here. Welcome rich. Thanks for joining me today.Richard Wiedenbeck 3:05Thank you. Maryfran, always a pleasure to be chatting with you. Totally.Maryfran Johnson 3:09Alright, let’s start out with let’s talk first about a broader picture of how the broader business picture about how Ameritas has been doing during these last few challenging years, and the role that it has been playing in the business success you have been having,Richard Wiedenbeck 3:28yeah, absolutely. So, I mean, Ameritas, I always like to say, if you look at our kind of growth, right, our growth has been relative, has been really good, relative to the industry, right? We’re classified, even with that broad range of diversified products, we still get classified in the life and annuity space, or the life insurance space. If you look at that industry, or sub part of the insurance industry, it’s been growing at about one to 3% a year, and we’ve been growing at about seven to nine so we’re clearly outgrowing our industry, which is a good sign. But by the same token, if you look at our expense structure, our expense structure seems to be holding pace with our top line, right? So top line growing, bottom line going, or top expense structure growing at the same rate, right? So, so the challenge to do that cost curve. And then around 2020, we took a look at that and said, Hey, we really, you know, we really need to modernize, you know, I mean, a lot of the standard stories. We need to modernize our systems. We need to really look at how we’re getting things done. We need to look at the interactions and the digital advancements we’re having, and then we need, we need to look at this kind of cost curve bending thing. And we took on a transformation project, an enterprise wide transformation project, we call Pepi, everybody. Everybody gives it a name. Everybody gets an acronym. You know, you always,Maryfran Johnson 4:55everybody loves a good title on a program, right? Yes,Richard Wiedenbeck 4:58always, um. And so we started that journey and and we’re, you know, we’re obviously, you know, four years into it, we’ve, you know, like any transformation journey, you’re going to say, these things went well. These things didn’t go as planned. These things. We wish we could go back and do a little differently, but I think all in all, we’ve made meaningful progress on that, on that journey, and then we started to see the AI frame come in and and we didn’t want to lose sight of that, and we didn’t think it was something to wrap into that. It was something to really start to pay attention to a little bit differently. But I think a lot of firms are on that broad, transformative journey, whether you call it Age of the Customer digital, you know, all of those are pieces of the puzzle and Ameritas certainly, just like other firms in our industry, and even our industries, have been actively pushing to make progress on that, not just kind of doing it as a Hey, here’s our portfolio, let’s prioritize. We actually chose to drive our investment levels up for a period of time to really try to make meaningful progress on it. And now we’re kind of coming on the tail end of that saying now let’s get into that standard. Still continue to make investments, but But where are we pushing those priorities, and how do we bring this

Hangzhou developed DeepSeek despite US export controls on high-performance chips commonly used to design and test AI models, thus proving how quickly advanced AI models can emerge despite roadblocks, adds Adnan Masood, chief AI architect at digital transformation company UST. With a lower cost of entry, it’s now easier for organizations to create powerful AIs with cultural and political biases built in. “On the ground, it means entire populations can unknowingly consume narratives shaped by a foreign policy machine,” Masood says. “By the time policy executives realize it, the narratives may already be embedded in the public psyche.” Technology as propaganda While few people have talked about AI models as tools for propaganda, it shouldn’t come as a big surprise, Moogimane adds. After all, many technologies, including television, the Internet, and social media, became avenues for pushing political and cultural agendas as they reached the mass market. source

Business leaders know it’s crucial to use identity-driven customer data to make smart decisions. But often, they get stuck because they don’t have a unified view of their customers and prospects. That’s a recipe for poor business outcomes. The consequences of getting identity wrong are substantial: Poor data quality = missed insights, operational inefficiencies, and wasted marketing spend. Vendor lock-in and cost overruns = higher expenses with limited flexibility. Slow digital adoption = inability to activate customer data reliably at scale. Customers are engaging through multiple channels, yet 2024 Forrester Research reported that consumer perceptions of Customer Experience had dropped in three consecutive years to its lowest point ever.[i] CIOs face mounting pressure to optimize their data strategy, manage vendors effectively, and accelerate digital transformation. Identity resolution is central to all three, yet many organizations struggle with fragmented data, vendor management, and scalable identity solutions.  We share three common mistakes that hinder data strategies and how they can be fixed. 1. Underestimating the complexity of a customer data strategy Data siloed across platforms prevents unified customer profiles. Companies collect on average 100+ data points per consumer, with at least 22% becoming obsolete each year.[ii] Inaccurate data impacts AI models, personalization efforts, and decision-making. How CIOs can reduce complexity: Adopt a first-party identity graph that continuously resolves and updates customer data for accuracy. Bring customer consent into the graph building process to align with regulatory requirements.  The impact: Companies that effectively organize and manage the customer experience can realize a 20% improvement in customer satisfaction, a 15% increase in sales conversion, and a 30% lower cost-to-serve.[iii] 2. Relying on a single vendor Many identity solutions are bound within a single ecosystem, limiting expansion flexibility. Vendor lock-in risk has only increased with the migration to cloud, and it complicates AI adoption.[iv] How CIOs can fix vendor lock-in issues: Build a vendor- and data-agnostic identity framework that supports cloud architectures that allows for interoperability, flexibility, and user control. The impact: A flexible identity framework reduces vendor costs, aligns with regulatory requirements, minimizes data movement, and accelerates business outcomes. 3. Treating identity solely as an IT problem When organizations lack a unified identity resolution, they struggle to activate customer data efficiently. Data silos hinder digital transformation, according to 81% of IT leaders, and 95% say data integration issues are impeding AI adoption.[v] How CIOs can fix identity issues: Treat identity resolution as a core enabler of digital transformation to create value across all functions. Implement enterprise identity solutions that empower business users to activate and collaborate on customer data without delays. The impact: Organizations that integrate identity resolution into digital transformation see faster time-to-market and improved AI-driven insights. Identity strategy as a CIO growth lever Unless they can overcome siloed data problems, CIOs will struggle to unlock the value of their data, enable AI-powered insights, and truly understand their customers. Solutions such as LiveRamp’s Enterprise Identity framework unify customer data into a singular, actionable profile that enables precise targeting and personalization. The company utilizes advanced ML algorithms to construct an accurate view of the customer.  Build an actionable and measurable view of customers. Learn more here. i Jacques, Pete, “Customer Experience Quality In The US Falls To An All-Time Low,” June 17, 2024. Forrester Research. ii Maurici, Vinny, “Understanding the Phenomenon Also Known as Data Decay,” April 13, 2023, Dun & Bradstreet. iii Erlich, et al, “How the operating model can unlock the full power of customer experience,” June 28, 2022. McKinsey & Co. iv Rooney, Paula. “CIOs weigh the new economics and risks of cloud lock-in,” Dec. 14, 2023. CIO. v “85% of IT Leaders See AI Boosting Productivity, but Data Integration and Overwhelmed Teams Hinder Success,” January 23, 2024. Salesforce. source

PM Ramdas explains that when executives understand the security implications of AI initiatives, they become strong advocates for balanced, secure implementation strategies. This proactive approach helps build a corporate culture where cybersecurity is viewed as an enabler of AI innovation rather than a hindrance. Trust in the age of Deepfake AI ‘Seeing is believing’ was an adage to live by, but in the era of undetectable, sophisticated deepfake fraud courtesy AI, this doesn’t quite ring true. Apart from public misinformation, organizations suffer massive impact to business, reputation and trust deficit if the evil side of AI runs unchecked. Rohit Singh speaks of their ‘AI vs AI’ mechanisms to stay ahead of scammers. “We counter AI-driven fraud with AI-powered detection tools that analyse micro-expressions, vocal tonality, and inconsistencies in digital communications to identify deepfake attempts in real time. We also employ adaptive authentication, such as liveness detection, contextual MFA, and real-time identity challenges, to thwart impersonation attempts.” source

At Hannover Messe 2025, Accenture and Siemens announced an extension of their strategic partnership in the form of the Accenture Siemens Business Group, an Accenture-based joint business unit that will employ 7,000 professionals with manufacturing and IT expertise worldwide. The group will develop software-defined products and factories, combining industrial technology with AI-supported engineering and manufacturing expertise. To achieve this, the group will leverage the Siemens Xcelerator portfolio for automation, industrial AI, and software, as well as Accenture’s data and AI expertise, to help companies redesign their engineering and manufacturing processes. Digitalization of manufacturing Roland Busch, president and CEO of Siemens AG, extolled the advantages of the partnership: “Two market leaders bring together their unique capabilities: technology, data access, and strong expertise in software, automation, and industrial AI — and Accenture’s strength, data, and AI in engineering and manufacturing.” source

As the world becomes increasingly globalized, businesses need to adapt – which is why language translation has never been more important. Whether translating content to reach global audiences, adapting product manuals for user safety and compliance, or facilitating seamless communication through real-time speech translation in customer support or business meetings, the ability to translate with accuracy, context, and speed is vital. But the question becomes, which translation tools do you need? While free translation tools may suffice for consumers, when it comes to business, “good enough” isn’t enough and only precise, nuanced, context-rich and secure solutions will do. Those results are achievable with DeepL’s Language AI platform, says Sebastian Enderlein, CTO at DeepL. “There has been commoditization of regular day-to-day translations. But we offer high quality, secure language and communication tools that over 200,000 businesses around the world trust to remove language barriers, whether internally between colleagues, or externally with clients,” says Enderlein. “Our platform, which offers written and spoken translation tools, as well as advanced writing functionality, going beyond word-to-word translation to offer contextualization and personalization with specialized AI.” The CTO offers this example: A Korean car manufacturer and a Japanese parts supplier need precise communication about components in an upcoming shipment. Accurate translation requires contextualizing automotive industry terminology, as even small errors could result in incompatible parts and disrupt production. This is where DeepL’s Language AI excels, capable of translating highly complex, custom terminology directly between two languages, ensuring full industry context. To achieve that level of precision, DeepL’s platform is powered by specialized LLMs trained on high-quality linguistic data and the expertise of thousands of language experts. “Human model tutoring is critical to the quality and accuracy we’re known for,” says Enderlein. The quality DeepL achieves drives significant efficiencies, saving customers time and money and enabling businesses to scale faster, producing fast, fully accurate translations that require less re-work. “Blind tests have shown that translations powered by our next-gen LLM require two to three times fewer edits than our competitors,” he says. The DeepL API As part of its platform, DeepL offers an API solution that enables customers to integrate DeepL’s Language AI technology into their own systems. Earlier this year, DeepL expanded its API capabilities to include more advanced translation capabilities powered by its next-generation language models, as well as the DeepL API for Write, which provides access to its advanced writing tools, providing AI-powered suggestions on word choice, phrasing, style, tone and more. Common use cases for the DeepL API include website translation and multilingual company communications via integrations with collaboration tools, CRMs and helpdesk platforms. Additionally, many leading CAT tool providers have integrated DeepL’s technology, allowing translators to leverage DeepL’s high-quality neural translations directly within their preferred software. Businesses are catching on. DeepL’s fast-growing customer network includes industry leaders like Softbank, Mazda, Harvard Business Publishing, The Ifo Institute, and Panasonic Connect. As companies expand and deepen their global initiatives, language and translation are critical to ensuring effective communication across international markets. Learn more about DeepL, including its powerful API solution, here. source