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Innovation and Technology

AI Ecosystems Transformation

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AI Ecosystems Transformation

Introduction to AI Ecosystems

We can’t emphasize enough the importance of interconnected networks and ecosystems to the enterprise application software market. Industry cloud providers and hyperscalers possess several key advantages in nurturing and leading these innovation networks. So what does this acceleration of AI software and services on industry cloud and hyperscaler marketplaces mean? Well, it depends on the customer segment the providers are vying to attract.

Customer Segments and Their Drivers

Enterprises are driven by strategic advantages, risk mitigation, maximizing the value derived from their AI investments, improving data locality, and reducing latency — all while prioritizing optimizing costs and operational performance.
Independent software vendors (ISVs) are driven by a unique set of business and strategic goals that focus on building trust and meeting customer requirements while protecting their IP and mindshare.
For regulated industries, because these ecosystems often involve third-party vendors and cloud platforms, the vetting of AI partners and solutions requires a heightened level of scrutiny. The desire for AI sovereignty is much stronger than a policy concern — it must comply with strict legal mandates and AI-specific legislation such as the EU AI Act; this is critical for national security and economic interests. They are driven by the control over key enablers of AI development, deployment, and the implications of global access and collaboration.

Benefits for Stakeholders

What do all the stakeholders have to gain from enterprise software markets operating within these massive ecosystems?

For Enterprises

  • Increased accessibility to advanced AI capabilities. Enterprises gain easier access to a wide array of cutting-edge AI tools and services without the need for significant upfront investment in infrastructure or specialized AI expertise. Hyperscalers’ marketplaces offer pretrained models, machine learning platforms, and AI-powered services for various applications — all of which can be procured through committed cloud spend, cutting out vendor onboarding cycles. For example, a B2C company can readily access AI-powered recommendation engines on a hyperscaler marketplace to personalize customer experiences and increase sales after a “try before you buy” proves its value.
  • Cost optimization. Pay-as-you-go (PAYG) pricing models can lead to cost savings compared to building and maintaining in-house AI infrastructure, but cloud migration efforts may still require upfront investments that can sometimes be offset or spread over time. Once you modernize with the cloud, enterprises can scale their AI usage based on actual needs and pace implementations that align with strategic priorities. Do beware, however, that mature governance is required to mitigate both buyer and provider challenges with the PAYG licensing model. If one software product isn’t paying off as expected, it’s easy enough to find an alternative on the marketplace and “recompose” your stack to adopt it.

For ISVs

  • Simplified go-to-market strategies. These marketplaces allow small ISVs to be part of integrated billing, procurement, logistics, and marketing tools, simplifying the way ISVs offer their AI-powered software and services to a wider audience, leveraging hyperscaler capabilities, and managing multiple listings across marketplaces.
  • Highly scalable infrastructure capabilities. ISVs can build their solutions on top of highly scalable infrastructure, enhancing the performance and scalability of their offerings while focusing on their unique industry expertise.
  • Faster innovation through integration. ISVs can integrate their AI solutions with other services available on the marketplaces, creating more comprehensive and valuable offerings for customers. This is quite valuable for the enterprise business application market. Even with their horizontal growth strategies, enterprise software continues to have persistent challenges with: 1) integration across apps with dependent functional capabilities; 2) interoperability with crucial business workflows; and 3) end user adoption — whether it’s a lack of consumer-grade experiences or the expectation of intuitive engagement. The adage “We’re essentially paying Cadillac prices for a Ford Pinto experience” continues to circulate corporate meeting rooms.

For Regulated Industries

  • Focus on specific industry needs. Industry clouds are tailored to meet the unique compliance, security, and operational requirements of regulated sectors such as government and healthcare, providing a trusted environment for deploying AI solutions. Ensuring ethics and AI sovereignty in government is a highly contentious and complex debate. New options continue to shape the landscape, but no one is dominating the headlines yet.
  • Force much-needed attention to governance, transparency, and risk mitigation. Traditional enterprise business applications faced the “black box” problem even before AI’s unique challenges amplified the complexity of governance and risk mitigation. Users didn’t understand the internal workings of the software; debugging and problem resolution were challenging; and third-party libraries and closed-source components had limited visibility. While modern platform ecosystems expedite procurement and deployment, they inadvertently introduce complexities around data governance, model oversight, and IP protection. These accelerated networks are forcing organizations to move faster and enter the market with stronger governance. Additionally, strict contractual clauses around vendor accountability and breach notifications are crucial for meeting compliance requirements and holding the vendors accountable to those commitments.
  • Maximize interoperability and data sharing (with safeguards). AI can facilitate secure and compliant data sharing and interoperability across different agencies or healthcare providers, leading to better insights and more coordinated services.

Conclusion

The transformation of business applications through AI ecosystems is a multifaceted phenomenon, offering various benefits to different stakeholders. For enterprises, it means increased accessibility to advanced AI capabilities and cost optimization. For ISVs, it provides simplified go-to-market strategies, highly scalable infrastructure capabilities, and faster innovation through integration. For regulated industries, it offers a focus on specific industry needs, forces much-needed attention to governance, transparency, and risk mitigation, and maximizes interoperability and data sharing with safeguards.

FAQs

Q: What are the key drivers for enterprises in adopting AI ecosystems?
A: Enterprises are driven by strategic advantages, risk mitigation, maximizing the value derived from their AI investments, improving data locality, and reducing latency — all while prioritizing optimizing costs and operational performance.
Q: How do ISVs benefit from AI ecosystems?
A: ISVs benefit from simplified go-to-market strategies, highly scalable infrastructure capabilities, and faster innovation through integration.
Q: What are the unique challenges faced by regulated industries in adopting AI ecosystems?
A: Regulated industries face unique challenges such as ensuring ethics and AI sovereignty, complying with strict legal mandates and AI-specific legislation, and maximizing interoperability and data sharing with safeguards.
Q: What is the role of industry clouds in supporting regulated industries?
A: Industry clouds are tailored to meet the unique compliance, security, and operational requirements of regulated sectors, providing a trusted environment for deploying AI solutions.

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Innovation and Technology

The AI Dilemma: How Machines are Raising Questions of Morality and Ethics

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The AI Dilemma: How Machines are Raising Questions of Morality and Ethics

With AI and automation for impact, the world is witnessing a significant transformation in the way we live and work. As machines become increasingly intelligent and autonomous, they are raising complex questions about morality and ethics that challenge our understanding of right and wrong. The AI dilemma is a pressing concern that requires careful consideration and nuanced discussion. In this article, we will delve into the intricacies of this dilemma and explore its implications for humanity.

Understanding the AI Dilemma

The AI dilemma refers to the ethical and moral concerns that arise from the development and deployment of artificial intelligence systems. As machines become more advanced, they are capable of making decisions that can have significant consequences for individuals and society as a whole. The dilemma arises when we consider whether machines should be programmed to prioritize human well-being, efficiency, or other values.

Autonomy and Decision-Making

One of the primary concerns surrounding the AI dilemma is the issue of autonomy and decision-making. As machines become more autonomous, they are able to make decisions without human input or oversight. This raises questions about accountability and responsibility, as well as the potential for machines to make decisions that are detrimental to human well-being.

Value Alignment

Another key aspect of the AI dilemma is the issue of value alignment. As machines are programmed to optimize certain objectives, they may prioritize values that are not aligned with human values. For example, a machine designed to optimize efficiency may prioritize productivity over human safety or well-being. This highlights the need for careful consideration of the values that are embedded in AI systems.

The Impact of AI on Society

The AI dilemma has significant implications for society, from the way we work and live to the way we interact with each other. As machines become more advanced, they are likely to have a profound impact on the job market, the economy, and our social structures.

Job Displacement and Economic Impact

One of the most significant concerns surrounding the AI dilemma is the potential for job displacement. As machines become more advanced, they are likely to automate many tasks currently performed by humans, leading to significant job losses. This raises questions about the economic impact of AI and the need for strategies to mitigate the effects of job displacement.

Social and Cultural Implications

The AI dilemma also has significant social and cultural implications. As machines become more integrated into our lives, they are likely to change the way we interact with each other and the way we understand ourselves. This raises questions about the potential for AI to exacerbate existing social inequalities and the need for careful consideration of the social and cultural implications of AI.

Addressing the AI Dilemma

Addressing the AI dilemma requires a multifaceted approach that involves governments, corporations, and individuals. It is essential to develop strategies that prioritize human well-being, safety, and dignity, while also promoting the development of AI that is aligned with human values.

Regulation and Governance

One of the key strategies for addressing the AI dilemma is the development of regulatory frameworks that prioritize human well-being and safety. Governments and corporations must work together to establish standards and guidelines for the development and deployment of AI systems.

Education and Awareness

Another essential strategy is education and awareness. It is crucial to raise awareness about the potential risks and benefits of AI and to educate individuals about the importance of prioritizing human values in AI development.

Conclusion

The AI dilemma is a complex and multifaceted issue that requires careful consideration and nuanced discussion. As machines become increasingly intelligent and autonomous, they are raising significant questions about morality and ethics that challenge our understanding of right and wrong. Addressing the AI dilemma requires a multifaceted approach that involves governments, corporations, and individuals, and prioritizes human well-being, safety, and dignity. By working together, we can ensure that the development of AI is aligned with human values and promotes a future that is beneficial for all.

Frequently Asked Questions

What is the AI dilemma?

The AI dilemma refers to the ethical and moral concerns that arise from the development and deployment of artificial intelligence systems.

What are the implications of the AI dilemma for society?

The AI dilemma has significant implications for society, from the way we work and live to the way we interact with each other. It raises questions about job displacement, economic impact, social and cultural implications, and the need for careful consideration of the values that are embedded in AI systems.

How can we address the AI dilemma?

Addressing the AI dilemma requires a multifaceted approach that involves governments, corporations, and individuals. It is essential to develop strategies that prioritize human well-being, safety, and dignity, while also promoting the development of AI that is aligned with human values. This includes regulation and governance, education and awareness, and the development of standards and guidelines for AI development and deployment.

What is the future of AI?

The future of AI is uncertain, but it is likely to be shaped by the decisions we make today. By prioritizing human values and promoting the development of AI that is aligned with human well-being, safety, and dignity, we can ensure that the future of AI is beneficial for all.

How can I get involved in addressing the AI dilemma?

There are many ways to get involved in addressing the AI dilemma, from participating in public discussions and debates to supporting organizations that prioritize human values in AI development. Individuals can also make a difference by educating themselves about the potential risks and benefits of AI and by promoting awareness about the importance of prioritizing human values in AI development.

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Innovation and Technology

AI Agents Deliver Productivity, But That’s Only Part Of The Story

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AI Agents Deliver Productivity, But That’s Only Part Of The Story

Introduction to AI Agents in the Workplace

The word on agentic AI’s ability to deliver on its promises is: so far, so good. With caveats. A majority of 300 senior executives adopting AI agents, 66%, say they’re delivering positive results in terms of productivity, a recent PwC survey suggests. But, let’s face it — all systems deliver some degree of productivity. What executives need is that extra edge that delivers extreme competitive differentiation.

Current State of AI Agents

At this point, few AI agents are ”transforming how work gets done," the PwC report’s authors state. “Many employees are using agentic features built into enterprise apps to speed up routine tasks — surfacing insights, updating records, answering questions. It’s a meaningful boost in productivity, but it stops short of transformation.” The biggest barrier isn’t the technology; “it’s mindset, change readiness and workforce engagement,” the PwC authors conclude.

Challenges and Limitations

Mahe Bayireddi, CEO and co-founder of Phenom, which offers agents for HR tasks, agrees this is where the challenge lies. “I think there’s a lot of learning in this whole process,” he said. “There are no experts dynamically saying how they can handle AI agents effectively.” Agents can bring up productivity almost by 20-30%, if they use it in the right format, do the change management effectively, and use the data in an engagement format,” Bayireddi continued. “The point is how do they make it fly, how do they manage the change management."

Importance of Context and Personalization

AI agents and the data they consume need to be domain-specific, and will vary industry to industry, company to company. “The data at the universal level is actually complex," he said. "The nuance of a context and that nuance of personalization is very critical for AI to work. It can’t be too general.” The rise of agents advances generative AI to more practical levels. Once put into place, agents can be “baked into the workflows," he said. “Up to now, everybody has had to go to ChatGPT and ask a question and get an answer. It’s not the way how people work.”

Future of Work with AI Agents

The emphasis needs to be on addressing the nuances of functions and processes to be automated with agents. “That has to manifest in an effective format with a context,” he said. "That can only happen with an agent being effective in a department.” Bayireddi doesn’t see agents as a threat to jobs, but they will change the nature of jobs. “There are new jobs which are going to come up because of agents. There is a new work which is also going to pop up because of agents. Skills is one thing, but also the work will change and the jobs will change.”

Conclusion

Don’t settle for too little when it comes to AI agents, the PwC authors advised. “Companies that stop at pilot projects will soon find themselves outpaced by competitors willing to redesign how work gets done. We see few companies moving early to define the future, building new operating models that integrate and orchestrate multiple AI agents. Fewer than half are fundamentally rethinking operating models and how work gets done (45%) or redesigning processes around AI agents (42%).”

FAQs

Q: What percentage of senior executives say AI agents are delivering positive results in terms of productivity?
A: 66%
Q: What is the biggest barrier to AI agent adoption?
A: Mindset, change readiness, and workforce engagement
Q: Can AI agents bring up productivity?
A: Yes, by 20-30% if used in the right format and with effective change management
Q: Will AI agents replace jobs?
A: No, but they will change the nature of jobs and create new ones.

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Innovation and Technology

Generative AI Tools For Lawyers

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Generative AI Tools For Lawyers

Introduction to AI in Law

For seasoned lawyers, as well as laypeople, simply trying to make sense of a tricky contract, new tools powered by generative AI promise to transform the way we engage with the law. Legal professionals often devote large chunks of their time to drafting contracts, researching previous cases, preparing documents for submission to court, or reviewing case law. Even for non-lawyers, many everyday tasks can require diving into legal concepts and principles—wading through corporate T&Cs, tenancy agreements, consumer rights advice or business compliance.

The Rise of GenAI Tools in Law

Fortunately, lawyers—professional and armchair varieties—are finding that there’s a wealth of genAI tools out there that can make their lives easier. So here’s my rundown of some of the leading apps, tools, and services that help with legal tasks. Some can help law firms and professionals automate dull and repetitive jobs, while others aim to make the legal systems and courts more accessible to laypeople.

Leading AI Platforms for Legal Tasks

Harvey AI is among the market-leading legal AI platforms. Like many of the tools here, it’s built on LLM technology (in this case, OpenAI’s GPT models that also power ChatGPT). However, it’s been fine-tuned to be particularly efficient when it comes to legal tasks such as research, contract analysis and compliance. As well as the vast amounts of training data at its disposal, it is further fine-tuned on domain-specific legal knowledge to ensure firms get assistance that’s tailored to their own way of working. Harvey now also offers agentic genAI capabilities, allowing it to work autonomously on carrying out longer, multi-step tasks.

LexisNexis has existed for more than 50 years as a database of legal information, including court decisions, judgments and case law. Today, it’s been given a generative AI upgrade in the form of Lexis+, which is designed to act as an AI legal assistant. Users—generally legal professionals—can engage through a conversational search interface in order to create tailored legal documents, and correspondence, as well as identify relevant case law, statutes and legal commentary. It also provides instant summaries of complex legal texts and integrates with Lexis’s Shepard Citation Service, ensuring citations are correct and up-to-date.

Consumer-Focused AI Tools

This is a web-based platform offering generative AI tools for a wide variety of legal tasks often faced by consumers and laypeople, including fighting parking charges, reclaiming debts and disputing bank fees. It isn’t purely AI-based—there is a strong community element to the service, too, and plenty of articles giving useful advice on a number of consumer rights, data protection and privacy issues. Billed as the "consumer AI champion," it can be used to generate dispute letters, file claims and navigate complex court processes no matter how inexperienced a user is in legal matters.

AI Assistants for Legal Professionals

Operated by Thompson Reuters, CoCounsel is an AI legal assistant that crunches through repetitive digital workloads like reviewing documents, researching case law, and identifying critical questions. It has developed a reputation as trustworthy among law firms due to a focus on robust data protection and privacy safeguards. CoCounsel has also added what it calls agentic functionality to its platform. Although it isn’t yet clear what their AI agents will do, it’s speculated that the legal industry will be heavily impacted by the adoption of these next-generation AI tools that promise even greater levels of automation.

Streamlining Legal Document Creation

GenAI-powered lawtech platform was built to streamline the creation of legal documents and the negotiation involved in contracts and agreements. Specifically tailored for startups and small businesses without full in-house legal teams, it offers AI-customized legal templates from employment contracts to NDAs, covering any standard documentation that smaller organizations might require. As it’s designed to be used by non-professionals as well as professionals, it features powerful functionality around reducing complexity and providing easy-to-digest explanations of contract clauses.

More Great Tools

That’s far from all of the tools out there designed for lawyers and armchair lawyers. If none of the above do exactly what you need, take a look at one of these:

  • Blue J: AI-powered research assistant designed to help accountants and other professional services understand tax laws.
  • ContractPod AI: Users of enterprise legal AI platform ContractPod can access Leah, a virtual legal ass
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