Hey AI, welcome to the team: Emergence of algorithmic enterprise

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Hey AI, welcome to the team: Emergence of algorithmic enterprise

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Do we fully understand what it means to be an AI-enabled enterprise? If a company does give primacy to Artificial Intelligence, what does that company look like? What does it take to be such an enterprise? How does it behave? What does its growth path look like and where is it headed? Who will it take along for the ride, and crucially, who will it leave behind? We unpack it for you here.

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What’s inside

  1. Transitioning to an AI-enabled businesses
  2. What if we humans stopped designing processes for humans to execute? Would we design processes differently if we had the limitless computing power of an algorithm to execute it?
  3. What becomes of humans in an AI enabled enterprise
  4. How comfortable are you with the Idea of a AI-Coworker
  5. The Algo-xperience

Transitioning to AI-enabled businesses

Companies today will begin their journey by becoming the AI-First Virtual Enterprise, evolving into the Autonomous Enterprise and finally into the Agent-enabled Enterprise

AI is now asserting its place as the cornerstone of the modern enterprise: an engine that brings alive a grand human vision by driving innovation, crafting strategy, and powering execution across every business function. To make this happen we are likely to see gigantic leaps in the way innovations scale up to become disruptions.

This point of view evaluates these disruptions, and distills the insights you need to embrace this oncoming change. Download Complete Research

What if we humans stopped designing processes for humans to execute? Would we design processes differently if we had the limitless computing power of an algorithm to execute it?

Today’s AI executes tasks designed for humans, better than what humans can do. What if, we start designing tasks to be executed by AI.

What becomes of humans in an AI enabled enterprise

We will see a proliferation of new roles that human beings will be required to play in an AI-Enterprise as it evolves. From AI psychologists who understand AI behavior, AI ethicists who guide the ethical aspects of AI behavior, to even AI trainers who train AI in much the same way as we train human apprentices today.

How comfortable are you with the idea of a AI-coworker

On a scale of 1 to 5, how comfortable are you with having an AI co-worker, or even an AI boss? As AI develops personhood, we will need to grapple with these questions as the culture of organizations evolve within the AI-enabled enterprise. This point of view explores this evolution.

Algo-xperience

If humans are hired today based on the quality of their ‘work experience’, how will AI algorithms be hired in the not too distant future? Enter the ‘Algo-xperience’ – a quantifiable measure by which you can evaluate one AI agent over another. A tangible metric of their past accomplishments, personality, biases and acquired knowledge. This point of view explores how Algo-xperience will play a role in the AI-enabled enterprise. Download Complete Research

Credits
Author@lab45: Nagendra Singh
Contributing Author: Jishnu Dasgupta

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The platform ecosystem business model: A blueprint for enterprises. Part 1

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The platform ecosystem business model: A blueprint for enterprises. Part 1

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Platform ecosystems are defined as open or closed networks where an orchestrator mediates relationships between a diverse set of complementary stakeholders. Orchestrators receive benefit from both accrued value in the platform ecosystem and in barriers to entry that ecosystems create for potential competitors. Platform ecosystems are also defined by a collaborative strategy that aims to create value for all stakeholders, including customers, partners, suppliers, and competitors.

What's inside

  1. Platform ecosystem overview
  2. Structure of Platform ecosystem
  3. Platform ecosystem typologies
  4. The rise of ecosystem orchestrator

Platform ecosystem overview

Platform ecosystem business models (platform ecosystem) have evolved in response to expanding business interconnection and complexity. They depart from traditional business models that emphasize internal control and efficiency. They establish and utilize connections with ecosystem stakeholders, who are independent actors, to produce value. They aim to establish a network effect that produces more value than any single entity could provide. Download Complete Research

They, ideally, conduct the following:

  1. Recruit multiple participants. At least one member acts as the orchestrator of the participants.
  2. Provide enterprises a structure for working together.
  3. Leverage the advantages and skills of related enterprises, groups, and participants in the ecosystem.
  4. Remove obstacles from the user journey and create additional value through ease of use.
  5. Enable every participant to use state-of-the-art technologies and systems to fulfil their individual needs.
  6. The orchestrator leverages the total value created by the platform ecosystem to benefit their business model.
  7. All stakeholders in the ecosystem also benefit from the value created by the ecosystem.

Structure of platform ecosystem

The essential elements of ecosystem business models are:

Suppliers(n): Businesses or institutions that provide the materials, components, or logistical support.

Orchestrator(Only one): Coordinates and controls the interactions between various ecosystem participants, such as suppliers and complementors, to add value for customers and foster the ecosystem's overall growth.

Complementors(n): Offer additional goods or services to the orchestrator's offers.

Customers(n): End-users or beneficiaries.

Ecosystem platforms rely on network effects to grow and prosper since they boost the platform's value as more users join and interact with the ecosystem. Network effects create a positive feedback loop that improves the overall value proposition is created as the user base expands and draws in new developers and enterprises to offer their services. Download Complete Research

Platform ecosystem typologies

Today, organizations across industries are exploring the potential of ecosystems to create additional value and minimize capital-intensive internal processes.

The business landscape is now characterized as the "age of business ecosystems," where enterprises that adopt ecosystems are better positioned to drive innovation and capital efficiency, and thus create more value for customers.

Partnership ecosystem: Partners within an organization work together to realize common objectives, capitalize on one another’s advantages, and co-create value.
Examples: Disney-Marvel, Amazon-Whole Foods Market

Aggregator marketplace ecosystem: A platform-based environment that brings together a variety of suppliers and buyers, facilitating transactions and generating value for all parties.
Examples: Amazon, Airbnb

Growth/expansion ecosystem: This ecosystem gives firms the resources to grow their operations and penetrate new markets. Resources include capital, knowledge, collaborations, and infrastructure.
Example: Uber expanding to Uber eats

Orchestrator ecosystem: A platform or network that facilitates transactions between ecosystem partners. In addition to setting up the infrastructure and tools needed for participants to work together and add value, the orchestrator also defines rules, standards, and protocols.
Example: AWS marketplace

Supply chain ecosystem: Enterprises leverage their existing capabilities (value chain ecosystem) to create an ecosystem of suppliers and partners to support their operations and growth.
Example: Apple’s supply chain ecosystem, Dell’s Direct Model

Community-based ecosystem: Businesses that are create social and environmental impact alongside financial goals.
Examples: Amul, Patagonia

Crowdsourcing ecosystem: Ecosystems that enable individuals to collectively contribute their knowledge, skills, and resources towards solving problems, generating ideas, or completing tasks.
Example: OpenStreetMap, Kickstarter

Technology/Innovator platform-based ecosystem: Ecosystem of interconnected IT resources that can function as a unit. Comprised of suppliers, customers, applications, and third-party service providers.
Example: SAP platform, Apple iOS ecosystem

The Rise of the ecosystem orchestrator

Ecosystem orchestrators create strategic partnerships and alliances to connect companies in a value chain.

They offer products and services that are mostly limited to their original product range and they share customers and data with their partners.

Orchestrators connects various stakeholders and create shared value for an ecosystem community.

They take on the risk, complexity, and challenges of supporting stakeholders. They enable others to create and sell goods and services through their ecosystem platform. They maintain a high level of quality within their ecosystem. Download Complete Research

Their roles are as follows:

  • They create platforms that drive exceptional customer experiences.
  • They define the reference architecture, creating the “true north” for ecosystem participants.
  • They collaborate with their partners to co-create and integrate services that address business issues and deliver valuable outcomes.
  • They ensure they are delivering differentiated customer experiences.

Platform ecosystem business models have proven to be a powerful modality, both for creating value and fostering collaboration among stakeholders.

Niche players in emerging industries may find it challenging to participate in global transactions or initiatives independently, but when positioning themselves within the ecosystem they can leverage the broader ecosystem’s resources or network effect to engage in larger endeavours and expand their customer reach.

Larger enterprises that struggle with agility in producing niche goods can benefit from partnerships with more nimble businesses within ecosystems. They can gain access to specialized knowledge and capabilities that enable them to deploy innovative products to market at speed.

As technology continues to reshape businesses and economies, the importance of platform ecosystem business models are expected to grow. Competent management of platform ecosystems will be a defining characteristic of successful leadership teams. Platform ecosystem owners can enhance their operations and user experience by establishing effective feedback channels. Download Complete Research

Credits
Author@lab45: Poonam Pawar
Contributing Author: Hussain S Nayak

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The AI paradigm

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42:35 Minutes The average duration of a captivating reports.

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The AI paradigm

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Ready to navigate the AI revolution? Lab45's special report demystifies the science behind generative AI and its real-world applications. Whether you're a business leader, government official, or curious citizen, this comprehensive guide offers actionable insights for everyone. From VC investments fueling AI's growth to ethical considerations, we've got you covered. Prepare to shape a future where AI doesn't just serve us, but also serves the greater good.

What's inside

  1. Breakthroughs leading to GenAI
  2. Building blocks of GenAI
  3. How is the industry organizing itself to deliver value around GenAI?
  4. How can enterprises harness the power of GenAI?
  5. The new AI economy: New careers, new lifestyles
  6. GenAI— safety, legal, and ethical implications
  7. How can we build a better world with AI?

Breakthroughs leading to GenAI

Advanced AI models like GPT-4 are game-changers, acing academic exams but also revealing logical inconsistencies. The evolution from foundational AI models to GenAI marks a significant shift. Instead of just retrieving information, these new models compose tailored outputs like text, audio, or video. This shift has far-reaching implications, affecting enterprises, governments, and consumers alike, and changing the way we interact with technology.

Building blocks of GenAI

Building Generative AI in an organization is akin to assembling lego blocks, each serving a specific function in the overall architecture. Two critical factors, data quality and model training techniques, form the bedrock of machine intelligence. Without robust datasets and effective training, the AI's performance will be compromised. The technology stack of GenAI is layered, allowing for specialized contributions from various entities—be it startups or established companies. This modular approach enables organizations to either build their own comprehensive AI systems or collaborate with others who offer specific expertise in certain layers. Download Special Report

How is the industry organizing itself to deliver value around GenAI?

The GenAI landscape is booming, fueled by a surge in investments and an influx of new players. At the core of this excitement are foundation models like GPT-4, which are increasingly being fine-tuned for specialized industry applications. Data stands as the linchpin in this race, urging enterprises to focus on developing valuable, unbiased datasets. The impact of GenAI is already palpable across various application segments, including chatbots and search functionalities, with image and text generation emerging as new consumer app categories.

How can enterprises harness the power of GenAI?

Navigating today's competitive business landscape requires enterprises to have a well-defined AI strategy. Initially, the focus should be on leveraging Generative AI for productivity gains. As they mature, enterprises can shift towards revenue gains by developing products and services using fine-tuned or foundational models. Ultimately, the goal is to achieve innovation gains. Despite the advancements in AI, the human element remains crucial, especially in product development where GenAI aids in ideation but humans execute the final design. Additionally, GenAI is set to revolutionize marketing by enabling hyper-targeted, personalized content, tailored to individual consumer preferences. Download Special Report

The new AI economy: New careers, new lifestyles

GenAI is poised to become a catalyst for unprecedented innovation and creativity, lowering the barriers to creative expression that have traditionally required specialized skills. This technology allows anyone to generate content from a simple prompt, democratizing creativity and potentially reducing the cost of creation to near zero. However, the rise of GenAI also brings challenges and questions about the future of various professions, from legal and medical to graphic design and call centers. As GenAI becomes more integral to our lives, there's an increasing need for skilled AI trainers who can ensure the ethical and fair use of these models. These trainers will focus on eliminating biases, ensuring privacy, and maintaining regulatory compliance. Download Special Report

GenAI— safety, legal, and ethical implications

As enterprises forge ahead in the development of GenAI products and services, the need for a balanced approach that incorporates AI safety is paramount. GenAI holds the potential for both transformative benefits and significant risks, making ethical design a non-negotiable aspect of its development. Regulatory landscapes are further complicated by differing governmental priorities and concerns, leading to fragmented and often challenging-to-enforce laws. For responsible and effective regulation of GenAI, a collaborative approach between enterprises and governments is essential. Both parties must work in tandem to navigate the complex risks while enabling the technology to enhance lives, whether people directly engage with AI or not. This dual responsibility underscores the critical role of ethical considerations and safety in the fast-paced evolution of GenAI.

How can we build a better world with AI?

As we stand on the cusp of a transformative era powered by AI, Lab45 presents a compelling vision for a future where technology serves the greater good. From revolutionizing medicine and combating climate change to democratizing education and beyond, the potential for positive impact is boundless. However, this optimism comes with a note of caution. The rapid popularization of GenAI in 2023 marks a critical juncture, requiring responsible stewardship from businesses, governments, and organizations alike. The societal disruptions and ethical dilemmas posed by AI, particularly its uneven impact across socio-economic classes, necessitate careful management. As AI continues to evolve, it offers us an unprecedented tool for innovation and progress. The challenge lies in harnessing this power responsibly, ensuring that the benefits of AI are universally accessible and contribute to a better world for all. Download Special Report

Credits
Author@lab45: Nagendra Singh, Tommy Mehl, Siddhant Raizada, Arvind Ravishunkar
Contributing Authors @lab45: Arindam Chatterjee, Noha El-Zehiry, Robert Walker Cohen

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