This is a VB Lab Insights article presented by Capital One. Enterprises are now deeply invested in how they build and continually evolve world-class enterprise platforms that enable AI use cases to be built, deployed, scaled, and evolve over time. Many companies have historically taken a federated approach to platforms as they built capabilities and features to support the bespoke needs of individual areas of their business. Today, however, advances like generative AI introduce new challenges that require an evolved approach to building and scaling enterprise platforms. This includes factoring in the specialized talent and Graphics Processing Unit (GPU) resource needs for training and hosting large language models, access to huge volumes of high-quality data, close collaboration across many teams to deploy agentic workflows, and a high level of maturity for internal application programming interfaces (APIs) and tooling that multi-agentic workflows require, to name a few. Disparate systems and a lack of standardization hinder companies’ ability to embrace the full potential of AI. At Capital One, we’ve learned that large enterprises should be guided by a common set of best practices and platform standards to effectively deploy AI at scale. While the details will vary, there are four common principles that help companies to successfully deploy AI at scale to unlock value for their business: 1. Everything starts with the user The goal for any enterprise platform is to empower users — therefore you must start with those users’ needs. You should seek to understand how your users are engaging with your platforms, what problems they’re trying to solve and any friction they’re coming up against. At Capital One for instance, a key tenet guiding our AI/ML platform teams is that we obsess over all aspects of the customer experience, even those we don’t directly oversee. For example, we undertook a number of initiatives in recent years to solve the data and access management pain points for our users, even though we rely on other enterprise platforms for these. As you earn the trust and engagement of your users, you can innovate and reimagine the art of what’s possible with new ideas and by going “further up the stack.” This customer obsession is the foundation for building long-lasting and sustainable platforms. 2. Establishing a multi-tenant platform control plane Multi-tenancy is essential for any enterprise platform, allowing multiple business lines and distributed teams to use the core platform capabilities such as compute, storage, inference services, workflow orchestration, etc. in a shared but well-managed environment. It allows you to solve core data access pain points, allows abstraction, enables multiple compute patterns, and it simplifies the provisioning and management of compute instances for core services — for example, the large fleet of GPUs and Central Processing Units (CPUs) that AI/ML workloads require. With the right design of a multi-tenant platform control plane, you can integrate both best-in-class open-source and commercial software components, and scale flexibly as the platform evolves over time. At Capital One, we have developed a robust platform control plane with Kubernetes as the foundation, which scales to our large fleet of compute clusters on AWS, that are used by thousands of active AI/ML users across the company. We routinely experiment with and adopt best-in-class open-source and commercial software components as plug-ins, and develop our own proprietary capabilities where they give us a competitive edge. For the end-user, this enables access to the latest technologies and greater self-service capabilities, empowering teams to build and deploy on our platforms without having to call on our engineering teams for support.  3. Embedding automation and governance As you build a new platform, it’s critical to have the right mechanisms in place to collect logs and insights on models and features along the end-to-end lifecycle, as they are built, tested and deployed. Enterprises can automate core tasks such as lineage tracking, adherence to enterprise controls, observability, monitoring and detection across various layers of their platforms. By standardizing and automating these tasks, it is possible to cut weeks and in some cases, months of time from developing and deploying new mission-critical models and AI use cases. At Capital One, we’ve taken this a step further by building a marketplace of reusable components and software development kits (SDKs) that have built-in observability and governance standards. These empower our associates to find the reusable libraries, workflows and user-contributed code they need to develop AI models and apps with confidence knowing that the artifacts they are building on enterprise platforms are well-managed under the hood. In fact, at this point in our journey, we consider this level of automation and standardization as a competitive advantage. 4. Investing in talent and effective business routines Building state-of-the-art AI platforms requires a world-class, cross-functional team. An effective AI platform team must be multidisciplinary and diverse, inclusive of data scientists, engineers, designers,  product managers, cyber and model risk experts and more. Each of these team members brings with them unique skills and experiences and has a key role to play in building and iterating on an AI platform that works for all users and can be extensible over time.  At Capital One, we have made it our mission to partner cross-functionally across the company as we build and deploy our AI platform capabilities. As we’ve sought to evolve our organization and build up our AI workforce, we established the Machine Learning Engineer role in 2021 and more recently, the AI Engineer role, to recruit and retain the technical talent that will help us continue to stay at the frontier of AI and solve the most challenging problems in financial services. Along the way, establishing and communicating well-defined roadmaps and change controls for the platform users, and incorporating feedback loops into your planning and software delivery processes is critical to ensuring your users stay informed, can contribute to what’s coming, and understand the benefits of the platform strategy you’re putting in place. Future-proofing your foundations for AI Building or transforming enterprise platforms for the AI era is no small task, but it will set your business

Traditional managed services have long been caught in a fundamental dilemma: achieving high-quality service delivery while maintaining cost-effectiveness. After offshore and cloud, managed services are being reshaped by AI at a fundamental level, introducing a new services paradigm that blends performance-based models, automation, and human-centric refinement. This shift is happening across industries, from HR and supply chain to help desk and manufacturing operations. AI-led services represent the next wave, potentially replacing or significantly augmenting human capital. Contact Centers As The Tip Of The Spear Consider the contact center, a historically manual, low-margin cost center. Enter the AI-powered model: An AI platform handles the lion’s share of interactions and continuously learns from every engagement. AI-first providers such as Crescendo are delivering managed services as software that flip the economics and the value proposition of traditional business process outsourcing. Crescendo’s platform promises to leverage advanced large language models and proprietary IP to handle 50–70% of interactions seamlessly. The rest — complex, high-touch cases — go to top-tier human experts. Knowledge engineers use customer interactions to constantly refine and improve the AI models, ensuring that the system gets smarter and more effective over time. Complexity doesn’t vanish overnight, but the reliance on large manual operations decreases as the AI becomes better at understanding context, maintaining accuracy, and adhering to brand voice. The Economics Of AI-Powered Services Perhaps the most revolutionary aspect of AI-powered managed services is their economic model. Rather than charging for labor hours or headcount, these services are increasingly moving toward outcome-based pricing. This approach aligns provider incentives with customer success metrics, fundamentally changing the dynamics of service delivery. Key advantages of this model include: Predictable costs tied directly to successful outcomes. Reduction of traditional staffing and training overheads. Reduced operational complexity. Scalability without proportional cost increases. The Learning Organization What sets advanced AI-powered managed services apart is their ability to learn and improve continuously. Unlike traditional services where knowledge often remains siloed within individual agents, AI systems can systematically capture and apply insights from every interaction. Knowledge engineers play a crucial role in this ecosystem. This knowledge loop creates a virtuous cycle: Each interaction provides data for model improvement. Enhanced models deliver better customer experiences. Improved experiences generate more positive interaction data. The system becomes increasingly effective over time. The Road Ahead: Fully Managed, Always Improving While the market is in its early stages, venture capital and investment firms are betting heavily on these AI-powered services. They anticipate adoption rates that could surpass the SaaS revolution, driven by clear ROI and immediate operational benefits. Contact centers are proving to be the perfect testing ground, but this model will expand across IT services, HR, supply chain, and other domains of operation where service quality and cost efficiency matter. This is what the future looks like: managed services that aren’t merely offshored or outsourced but are continuously optimized, AI-infused, and laser-focused on business results. Organizations can leverage these AI-powered managed services in two complementary ways: Transform delivery of mission-critical but nondifferentiating capabilities. Customer service, IT support, and back-office operations can be optimized through AI-powered managed services, freeing resources for strategic initiatives. Use these partnerships as learning laboratories. Understanding how AI models operate in managed services will provide valuable insights for future applications in core, differentiating business capabilities. Read our recent report to learn more about how generative AI is disrupting professional services. source

December is a good time to think about your next career move. Your colleagues may be on a go-slow when it comes to getting projects over the line ahead of the Christmas holiday period, but for those with an eye on 2025’s job-hunting prize, this month can be a really fruitful time to look for a new opportunity. Bonnie Dilber, who is a recruiting leader at Zapier and an HR influencer on TikTok, explains why, based on her nine-plus years of hiring experience. “Basically you have a situation at the end of the year where companies may have fewer roles posted, but they’re generally really urgent about closing out the ones they have posted, and you have less competition.” 5 jobs to discover all across Europe now Senior Fullstack Developer (m/w/d), Eberlein Kunz, Berlin Software Engineer, Staal Verbind B.V., Amersfoort Développeur Python, Alter Solutions, Paris Cloud Software Architect / Developer (all genders), Mazars GmbH & Co. KG, Berlin Outsystems Developer, TIP Group, Amsterdam “Budgets turn over at the end of the year and some companies are set up so if they don’t make that hire within the fiscal year, then they lose that budget. So they’re going to want to close out the roles that they have opened.” How Startup Amsterdam Boosts Innovation and Growth at TNW Conference Discover how the City of Amsterdam partnered with TNW to amplify its startup ecosystem, attract global talent, and foster innovation that drives economic impact. Dilber also explains why competition is diluted as the festive season approaches. “A lot of candidates actually opt out of interviewing at this time of the year. Folks who are employed have end of year bonuses, they may have shares that are vesting. Often they’re not wanting to start a new job until sometime like February or March.” Armed with this intel, what can you do to accelerate your 2025 career aspirations ahead of the pack? And when it comes to the best cities for software developers in Europe, where should you focus your attention? Strong tech performers Your mind might go to the big tech hubs of London, Berlin and Paris first. These cities are always in demand, and according to Startup Genome’s most recent Global Startup Ecosystem Report, London reigns supreme. The total value of London’s tech ecosystem was $621.5 billion in 2023. It’s home to 103 unicorns and has seen an 800% rise in VC investment over the past decade. In fact, VCs invested $12 billion in London in 2023. When it comes to Berlin, the picture is also rosy. The German government is investing heavily in the sector, and in 2023 launched Growth Fund Germany, a $1.76 billion pool of funds for investment in German VCs. In the same year, it launched a DeepTech & Climate Funds initiative, allocating $1.76 billion to growth-stage companies in these two sub-sectors. Additionally, Berlin saw 468 startups founded in 2023, and interestingly 49% of all startup employees are non-German citizens, offering workers one of the most diverse workforces in the world. In Paris, the news is also positive. The French capital boasts more than 8,000 startups, as well as the world’s largest startup campus, Station F. French startups raised €8.3 billion in 2023, and France was notable in that it was one of just two European countries that had more fund closings in 2023 than 2022. As a result it is not surprising that software developers are increasingly drawn to these major European cities––not just for the work opportunities. Competitive salaries, vibrant tech ecosystems, innovation-first approaches and cultural attractions all figure too. 5 more software roles to consider GiS Platform Engineer (f/m/d), Uniper, Landshut BI Spezialist, Business Intelligence (m/w/d), ETERNA Mode GmbH, Passau Cloud Engineer, YoungOrange B.V., Utrecht West System engineer Datapower, belastingdienst, Apeldoorn Fullstack developer | SaaS, Haystack People, Utrecht Tech cities to watch When it comes to the ones to watch, or the cities where developers may find interesting roles to flex their muscles at early stage companies, which cities and countries should they look to? Startup Genome’s report highlights Copenhagen as a top global fintech hub. In 2023, the Danish capital’s fintech startups secured the third most VC funding per capita among the top 10 European ecosystems. For developers proficient in creating forecasting models, developing trading algorithms, and building new apps and tools for customers, this city offers opportunities for those with Python, Java, C++, C#, Ruby, and SQL skills. And according to Levels, the median software engineering salary in Copenhagen is a cool €93,260, rising to €134.1K for highly experienced developers. Serbia is also experiencing a booming startup scene. Compensation is lower at a median figure of €60,619, but so is the cost of living. The cities of Belgrade and Novi Sad can offer developers exciting opportunities in gaming, blockchain, and life sciences. Investment in the country is on the increase with the report finding that the ecosystem saw $70 million in investments in 2023. The European Bank for Reconstruction and Development has also invested a record $846 million in Serbia, with China investing $2.18 billion in Serbian renewable energy too. In Italy, Turin is emerging as a burgeoning tech hub. Historically, it has been known as a centre for the automotive industry, but is now Italy’s second-largest city in terms of investment volume, recording $70.8 million across 60 funding rounds for its startups in 2023. Wages are modest here, at a median level of €36,474, but for developers interested in the areas of smart cities, AI, big data and analytics, as well as space tech, Turin is a city to consider. Budapest and Istanbul are also on the rise as tech hubs. Budapest is projected to outpace average EU growth with a 2.8% GDP increase in 2025, and has seen some excellent deals in the life sciences sector. Biotech firm Turbine’s cell simulation technology for cancer R&D secured a $6 million Series A in 2023, with VRG Therapeutics receiving a $5.5 million Series A the same year. In Istanbul, the startup sector

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google expanded access to the business version of its popular NotebookLM app, now called NotebookLM Plus, targeting enterprises, teams and individuals who rely on the app’s research tools.  The company also updated its podcast-like Audio Overview feature, which allows users to interact with the AI hosts and ask questions out loud.  The research tool, which lets people gather information into “notebooks” and ask questions with answers from the source material, launched in July last year on preview. It proved popular and became generally available in December. Originally built with Gemini 1.5, NotebookLM has been upgraded with an experimental version of Google 2.0 Flash, Google said. After the Google team noticed a lot of different use cases for NotebookLM — including many enterprise projects — the company launched NotebookLM Plus for enterprises, teams and individuals who use the application a lot. NotebookLM Plus will have five times as many Audio Overviews, notebooks and sources per notebook.  Premium users can also customize the style and tone of notebooks, share notebooks with team members, and see usage analytics. Google said it also added more privacy and security features.  NotebookLM Plus can be accessed through Google Workspace or Google Agentspace. Next year, NotebookLM Plus will be included in the Google One AI Premium subscription.    Google announced what was then called NotebookLM Business in October as a pilot program for new business-focused uses for the application.  Audio interaction  Audio Overviews, where users can generate an audio conversation based on the information in the notebook, came out in September and became an instant hit. The podcast-y nature of the audio offered a way to help people digest complex information via a conversation between two people and proved very popular. The tool often featured two AI-generated hosts chatting about the information in the notebooks; now, NotebookLM users can interject and ask questions using their voice to get more details or direct the conversation. Users can create a new Audio Overview, tap the “interactive” button and then click on “join” while listening, and the AI hosts will call on the user to ask their question.   Interacting with the hosts of AI Overviews will be available only on new Audio Overviews, not on existing ones.  Google warned in a blog post that interacting with the Audio Overview is still experimental, and the “hosts may also pause awkwardly before responding or [may] occasionally introduce inaccuracies.” Former NotebookLM product lead Raiza Martin had told VentureBeat that Google would introduce more controls and interactions with Audio Overview.  All-new look Google redesigned NotebookLM to help users “better manage content and ask the AI interface questions about their sources.” The new look introduces three panels: a Sources panel for all the documents or files uploaded to NotebookLM; a Chat panel to access the Gemini chat box to interrogate data sources; and the Studio panel for creating study guides, briefing documents and Audio Overviews.  “From the start, we wanted NotebookLM to be a tool that would let you move effortlessly from asking questions to reading your sources to capturing your own ideas. Today, we’re rolling out a new design that makes it easier than ever to switch between those different activities in a single, unified interface,” Google said in a blog post.  Enterprise interest Since its launch, NotebookLM has​​ had various uses​​, even in the enterprise space. Some have even claimed it is a “CRM killer.” Users posted on social media about the different ways they’ve been using NotebookLM. Sam Lessin, former vice president of product at Meta and general partner at Slow Ventures, said on X that his firm uses NotebookLM instead of a CRM.   Martin previously told VentureBeat that her team saw many users begin sharing notebooks with others, making some notebooks the repository of data around company policies or project research.  source

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“Seamless customer experience includes multiple aspects – personalization, omnichannel strategy, customer journey mapping, and relationship building,” said Bhanushee Malhotra, practice director at Everest Group. “While utilizing AI for task automation and predictive insight generation is helpful to achieve seamless CX, there is a huge risk of AI being used to create highly convincing and personalized scams.” The role of conversational AI in the future of customer service The proliferation of GenAI in daily life is shifting customer behavior. Gartner’s research revealed that 45% of customers already use generative AI either personally or professionally. This growing reliance is expected to lead to 70% of customer service interactions being initiated — and resolved — through third-party AI assistants like Apple AI or Google Gemini by 2028, the report added. These assistants offer customers a more intuitive and low-effort experience compared to traditional service portals. Quinlan cautioned that companies must reevaluate their investments in self-service portals. source

By Craig Clough ( December 13, 2024, 6:09 PM EST) — A California federal judge imposed terminating sanctions against a former engineer at TikTok’s parent company, finding he committed perjury in a suit alleging he was wrongly fired and ordered the dispute to arbitration…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

The Australian Signals Directorate and the Australian Cyber Security Centre have joined cybersecurity institutions from the U.S., Canada, and New Zealand in warning local technology professionals to beware of threat actors affiliated with China, including Salt Typhoon, infiltrating their critical communications infrastructure. The news comes weeks after the Australian Signals Directorate’s Annual Cyber Threat Report 2023-2024, where the agency warned that state-sponsored cyber actors had been persistently targeting Australian governments, critical infrastructure, and businesses using evolving tradecraft over the most recent reporting period. What is Salt Typhoon? Recently, the U.S. revealed that a China-connected threat actor, Salt Typhoon, compromised the networks of at least eight U.S.-based telecommunications providers as part of “a broad and significant cyber espionage campaign.” But the campaign is not limited to U.S. shores. Australian agencies did not confirm whether Salt Typhoon has reached Australian telco companies. However, Grant Walsh, telco industry lead at local cyber security firm CyberCX, wrote that it was “unlikely the ACSC – and partner agencies – would issue such detailed guidance if the threat was not real.” “Telco networks have invested in some of the most mature cyber defences in Australia. But the global threat landscape is deteriorating,” he wrote. “Telecommunications networks are a key target for persistent and highly-capable state-based cyber espionage groups, particularly those associated with China.” SEE: Why Australian Cyber Security Pros Should Worry About State-Sponsored Cyber Attacks More Australia coverage Salt Typhoon: Part of a wider state-sponsored threat problem Over the past year, the ASD has issued several joint advisories with international partners to highlight the evolving operations of state-sponsored cyber actors, particularly from China-sponsored actors. In February 2024, the ASD joined the U.S. and other international partners in releasing an advisory. It assessed that China-sponsored cyber actors were seeking to position themselves on information and communications technology networks for disruptive cyberattacks against U.S. critical infrastructure in the event of a major crisis. The ASD noted that Australian critical infrastructure networks could be vulnerable to similar state-sponsored malicious cyber activity as seen in the U.S. “These actors conduct cyber operations in pursuit of state goals, including for espionage, in exerting malign influence, interference and coercion, and in seeking to pre-position on networks for disruptive cyber attacks,” the ASD wrote in the report. SEE: Australia Passes Ground-Breaking Cyber Security Law In the ASD’s annual cyber report, the agency said China’s choice of targets and pattern of behaviour is consistent with pre-positioning for disruptive effects rather than traditional cyber espionage operations. However, it said that state-sponsored cyber actors also have information-gathering and espionage objectives in Australia. “State actors have an enduring interest in obtaining sensitive information, intellectual property, and personally identifiable information to gain strategic and tactical advantage,” the report said. “Australian organisations often hold large quantities of data, so are likely a target for this type of activity.” Common techniques used by state-sponsored attackers According to Walsh, China-sponsored actors like Salt Typhoon are “advanced persistent threat actors.” Unlike ransomware groups, they are not seeking immediate financial gain but “want access to the sensitive core components of critical infrastructure, like telecommunications, for espionage or even destructive purposes.” “Their attacks are not about locking up systems and extracting fast profits,” according to Walsh. “Instead, these are covert, state-sponsored cyber espionage campaigns that use hard-to-detect techniques to get inside critical infrastructure and stay there, potentially for years. They are waiting to steal sensitive data or even disrupt or destroy assets in the event of future conflict with Australia.” The ASD has warned defenders about the common techniques these state-sponsored threat actors leverage. Supply chain compromises The compromise of supply chains can act as a gateway to target networks, according to the ASD. The agency noted, “Cyber supply chain risk management should form a significant component of an organisation’s overall cyber security strategy.” Living off the land techniques One of the reasons state-sponsored actors are so difficult to detect, according to the ASD, is because they use “built-in network administration tools to carry out their objectives and evade detection by blending in with normal system and network activities.” These so-called “living off the land” techniques involve waiting to steal information from an organisation’s network. Cloud techniques State-sponsored threat actors adapt their techniques to exploit cloud systems for espionage as organisations move to cloud-based infrastructure. The ASD said techniques for accessing an organisation’s cloud services include “brute-force attacks and password spraying to access highly privileged service accounts.” SEE: How AI Is Changing The Cloud Security Equation How to defend against cyber threats There are some similarities in threat actors’ techniques and the weaknesses in the systems they exploit. The ASD said state-sponsored cyber actors often use previously stolen data, such as network information and credentials from previous cyber security incidents, to further their operations and re-exploit network devices. Luckily, companies can protect themselves from cyber-attacks. Earlier this year, TechRepublic consolidated expert advice on how businesses can defend themselves against the most common cyber threats, including zero-days, ransomware, and deepfakes. These suggestions included keeping software up-to-date, implementing endpoint security solutions, and developing an incident response plan. source

With the rapid rise of AI, especially GenAI, clients are evaluating risks from partner or vendor use of AI. CIOs and organizations are advised to consider how these risks may impact their operations and security and create contractual terms to address them. Specific areas of concern for CIOs and IT organizations are how a vendor uses its data, whether its data will be used in training public models, how data is protected, data access, results bias, and risks of hallucination and plagiarism. Clients wish to understand and mitigate the additional risk that AI may bring from their vendor and partner relationships. CIOs and organizations recognizing this risk (and following recommendations of research firms) are now embedding specific requirements in their vendor and partner contracts. They are demanding clear assurances on how AI-related risks are mitigated. These clients expect responsive, meaningful information about the safeguards in place, particularly around data use, adherence to data protection practices, and use cases that impact them. For vendors and partners, meeting these demands requires preparing comprehensive, transparent contractual responses that are accurate and will not delay contracting. Also, we are witnessing, on both the vendor side and client side, frustration with how these clauses are delaying contracting itself as both client and vendor legal teams struggle over them. To address this, vendors and clients need to develop a model for how they want to address, communicate, and understand AI risk. To help simplify the process and provide a leg up for developing AI risk clauses in contracts (for both clients and vendors), this overview covers the key elements that are essential for drafting and responding to AI risk provisions. On both the vendor and client side, standardized, flexible language should be developed immediately with the assistance of legal, because, even if a vendor or partner hasn’t had to respond to an AI clause in a contract yet, it will. Defining AI’s purpose and ensuring transparency Clients need to understand how AI will be used within their contracted services or product. To address this, vendors need to start by defining AI’s role in the service(s) or product provided, highlighting both its purpose and the potential benefits for the client. For instance, AI might be used to support data analysis, improve operational efficiency, or streamline routine tasks — all areas that can drive value when clearly communicated. A well-defined purpose of AI helps clarify that AI’s role is not arbitrary and establishes transparency, allowing clients to understand exactly how it aligns with their goals. Additionally, this section should cover any limitations around the use of AI (i.e., how it will not be used). Use of client data A key (if not primary) client concern is how its data will be used by a vendor or partner. While data usage should be stated as part of a vendor’s existing, standard, data protection policy, the concern is heightened due to some unique aspects of GenAI. In this regard, the contract should outline the vendor’s current practices regarding data security and privacy as well as adherence to regulations such as GDPR, CCPA, and other relevant data protection laws. A vendor should already have defined client data policies. GenAI should be a superset or expansion on existing vendor data protection policies. Clear guidelines around data handling practices help ensure that client data remains secure and protected from unintended uses. Clients are particularly concerned about their data being used to train AI models, as well as its visibility to other clients. This is a key risk clients want addressed. One practice involves stating a prohibition on using non-anonymized client data for AI training without prior consent. Another is stating that client data is not used in training. Addressing these details upfront not only enhances trust but also aligns with industry standards on ethical data management. Establishing an AI usage policy and human oversight Vendors having a formal AI usage policy gives clients clarity around the types of AI technologies being used. This policy should cover specific provisions on how AI may be used in generating client-related insights. For example, there should be provisions explaining how AI is used internally and applied to client-specific data to answer client needs. Incorporating human oversight into AI applications provides an additional safeguard. By establishing that all AI operations will undergo human review, vendors can assure clients that automated processes will be validated by human personnel. This not only mitigates risks but also reinforces quality control, especially in contexts where AI is used to support data analysis or insights. Having a human expert review and supervise these outputs helps ensure that client standards and expectations are met, reducing the likelihood of unintentional errors or oversights from automated processes. Vendors should make clear what, if any, oversight is being provided and where. Risk management and confidentiality Effective risk management is crucial for any vendor offering AI as part of its product, and clients want to know that measures are in place to handle potential AI-related risks. The contract should outline risk management strategies, including regular audits of AI systems, impact assessments for high-stakes AI use cases, mitigation of AI drift, and incident response plans for data breaches or misuse. Clients will feel reassured knowing that the vendor has measures in place to address issues before they impact service quality or data security. Confidentiality agreements also play a critical role in safeguarding client data. Reinforce the organization’s commitment to data privacy by referencing any confidentiality agreements that protect client information from unauthorized disclosure. By including terms that limit access to sensitive client data for AI systems or other technologies, clients are reassured that their data will be handled in line with privacy expectations. These agreements signal that sensitive information will not be disclosed or processed by AI without prior client consent. These agreements are typically in place for data handling even outside the scope of AI. In any AI-related contract, it is essential to ensure that the client or customer also has a robust AI policy in place.

By Andrea Pacelli, Michael DeVincenzo and Charles Wizenfeld ( December 13, 2024, 3:09 PM EST) — In global industries such as electronics and consumer products, parallel intellectual property litigation in multiple forums has become a key tool in the sophisticated litigants’ toolbox…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source