Beyond the Hype: Decoding Generative AI for Enterprise Adoption

C-Level's guide to embrace and adopting a Generative AI Strategy applied to creativity, innovation and digital transformation processes.

In this post I’m sharing a version of my Masterclass “Generative AI: Beyond The Hype for Enterprise Adoption” that I’ve given over the past few months, to various C-Level & Business Leaders spanning Financial Services, Insurance, Energy, Telco, FMCG, Healthcare, Retail, and Real Estate companies. AI use cases & applications change everyday, depending on the audience. But the underlying strategic framework stays the same. I hope you find it useful and actionable.

TL;DR: Every C-Level is being asked “What’s your Generative AI Strategy?” The big challenge here is that no one knows where AI is headed (not even the people creating it).

So you need a way to think about?Generative AI (Gen AI) Strategy?that is:

• Sustainable:?will remain relevant for the next few years
• Flexible:?can accommodate the inevitable new developments
• Actionable:?that empowers your frontline teams to anticipate change
• Consistent:?ensures that your teams have a shared vision and goals about where and why they are applying Gen AI

Introduction

Over the past several months, hype about GenAI has flooded popular discourse. Seemingly overnight, every CEO began adding GenAI to their business strategy. Many influencers and leaders have opposing views of GenAI: some are worried about highly competent AI systems causing mass job displacement, while others find these models are not enterprise-ready due to privacy and security concerns.

I’m here to get past the hype and help you separate the signal from the noise on what it really takes to adopt GenAI to help you solve your most important business challenges. In this post we’re going to explore the real impact of GenAI, how every industry can adopt AI to accelerate innovation, and the best practices companies can follow to maximize their investment in the technology.

The GenAI industry is predicted to witness significant expansion, going from a value of USD $81B in 2022 to an astonishing USD $1.2 trillion by 2030. This growth is projected to occur at an impressive 35% CAGR between the years 2022 and 2030. Today, out of the five forefront AI technologies gaining traction. GenAI synonymous with "creation" in our AI value chain, exhibits the most rapid growth.

Companies see the potential of generative models to improve their business, but getting them into production is challenging. To unlock the power of their data and take full advantage of these models, companies need machine learning expertise, fine-tuning infrastructure, and the resources to perform Reinforcement Learning with Human Feedback (RLHF) at scale.

This high-level summary zeroes in on GenAI and provides a strategic framework for considering each element of the AI value chain:

• GenAI is poised to potentially disrupt businesses in every sector in the imminent future.
• As the accuracy of GenAI improves alongside its ability to offer trustworthy factual counsel, its influence will permeate various sectors and business operations.
• Applications span a wide spectrum, from customer assistance chatbots and production of news and marketing content, to code generation and product development.

Generative AI: Overview

GenAI refers to a category of Artificial Intelligence techniques and Machine Learning algorithms that are designed to generate new data or content that is similar to what it has been trained on. This can include text, images, audio, video, speech, design, and software code. Examples of GenAI include image, video and data synthesis, text generation, music composition etc.

Why Gen AI is a great equalizer

These new types of GenAI have the potential to significantly accelerate AI adoption, even in organizations lacking deep AI or Data Science expertise. While significant customization still requires expertise, adopting a generative model for a specific task can be accomplished with relatively low quantities of data or examples through APIs or by prompt engineering.

The capabilities that GenAI supports can be summarized into three categories:

• Generating Content and Ideas.?Creating new, unique outputs across a range of modalities, such as a video advertisement or even a new protein with antimicrobial properties.
• Improving Efficiency.?Accelerating manual or repetitive tasks, such as writing emails, coding, or summarizing large documents.
• Personalizing Experiences.?Creating content and information tailored to a specific audience, such as chatbots for a personalized customer experiences or targeted advertisements based on patterns in a specific customer's behavior.

Today, some GenAI models have been trained on large of amounts of data found on the internet, including copyrighted materials. For this reason,?responsible AI?practices have become an organizational imperative.

How is Gen AI governed

GenAI systems are democratizing AI capabilities that were previously inaccessible due to the lack of training data and computing power required to make them work in each organization’s context. The wider adoption of AI is a good thing, but it can become problematic when organizations don’t have appropriate governance structures in place.

How is Gen AI beneficial for businesses

GenAI has massive implications for business leaders—and many companies have already gone live with GenAI initiatives. In some cases, companies are developing custom AI model applications by fine-tuning them with proprietary data.

The benefits businesses can realize utilizing GenAI include:

• Expanding labor productivity
• Personalizing customer experience
• Accelerating R&D through generative design
• Emerging new business models

Gen AI Market Drivers

Staving off commoditization

• Cloud giants want to attract low-coders and non-coders to their respective platforms e.g., Azure, AWS, Google Cloud.
• Advanced AI services can eliminate complex baseline coding requirements.

AI-injected Apps

• Providers are operationalizing GenAI for use within bespoke applications.
• Business transformation efforts are demanding GenAI integrations to move up the development stack and enhance customers’ image, video, and audio experiences.

Data Scientists

• At a time when the industry is experiencing a global technology skills gap, tools based on AI, low-code platforms, and automation are more relevant than ever.
• Companies scrambling to re-skill internal personnel are seeking out relevant tools including GitHub Copilot for abstracting time-consuming tasks such as baseline coding.
• GenAI will significantly bypass similar levels of complexity.

Consumer Momentum

• The ease with which individuals can use ChatGPT has propelled GenAI into the headlines and onto the agendas of non-technical business leaders.
• It has stirred up widespread public interest in the technology and driven extensive curiosity into potential business applications.

Operational Efficiency

• Years of investment in Deep Learning, NLP, and LLM, have been enabled through increasingly powerful computing frameworks and algorithms.
• GenAI is poised to enhance intelligent automation solutions, alongside app deployments, networking, coding, security, etc.

Giant Leap of Generative AI

Generative models are already transforming how we create art, understand our world, and conduct business.

Large Language Models (LLMs) help us write content such as blogs, emails, or ad copy more quickly and creatively. They summarize long-form content so that we can quickly understand the most critical information from reports and news articles. Diffusion models streamline marketing workflows, enabling marketers to generate unlimited and infinitely creative product imagery.

Developers use LLMs to write code more efficiently and help them quickly identify and fix bugs. Advanced chatbots enable businesses to improve their customer service at a lower cost. Finally, organizations are unlocking the power of their knowledge bases by customizing LLMs with their proprietary data to perform better on tasks unique to their business.

Generative models are now more widely available as many large model developers provide APIs or make them open-source, and companies are quickly adopting these large models to their specific business use cases.

While Generative Models are great generalists, they are poor specialists when solving problems outside of their data distribution. Since a significant portion of data is proprietary to individual organizations, base large language models are not well adapted to these specific domains.

To improve performance on the specific tasks of, say, an insurance company, an eCommerce company, or a logistics company, these models must be fine-tuned and aligned to excel at those particular tasks and provide responses that are useful to customers and employees.

Current State of Gen AI

Increased investment in AI

As GenAI is now more capable and widely available, companies are quickly incorporating it into their operations. 72% of companies will significantly increase their investment in AI each year for the next three years.

Increasing capabilities of generative?models

Many organizations are now building their own large generative models. These models are being incorporated into search engines and paired with other tools, including internal knowledge bases, to become powerful business tools. These models will also become multimodal, meaning that they will be able to consume and generate text, images, and video, making them even more useful than they are today.

Widespread accessibility of generative?models

Much like the cloud, widely accessible generative models represent a paradigm shift for companies. Incorporating this type of AI will quickly become the status quo, and those who are slow to adopt will be left behind.

Proprietary data will unlock the power of generative models

On their own, base generative models are valuable tools. Paired with a business’s proprietary data, they become strong differentiators, improving the customer experience, product development, and profitability.

Enterprise Adoption of Generative AI

C-Level & Business Leaders have identified that AI is critical to the future of their companies and are looking to adopt it as quickly and with as much impact as possible. As companies view AI as more critical to the future success of their business, they are increasing AI investments over the next three years.

Companies are spending less time and effort on traditional computer vision applications and instead focusing on GenAI and LLMs. Of Companies making significant investments in AI:

72% of companies plan to increase their investment in AI each year for the next three years

52% are in investing heavily in LLMs

36% are in investing heavily in Generative Visual Models

30% are in investing in Computer Vision Models

Source: Scale AI Readiness Report 2023

What outcomes are achieved by companies that adopt Gen AI

Companies adopting AI are seeing positive outcomes from improved customer experiences, the ability to develop new products or services and improve existing products, and improved collaboration across business functions.

Across the board, companies adopting AI are achieving positive outcomes in almost every category. Like any technology program, the success of AI programs depends on the ability to implement AI and align implementation efforts with measurable organizational goals.

Which resources are enough to implement a Gen AI Strategy

Companies that view AI as critical to their business indicate they have the executive support, strategy and vision, and budget they need to succeed in implementing their company’s AI Strategy. However, these companies generally lack the necessary expertise, software, and tools required to achieve success.

While leaders have identified the need to adopt AI, the execution of these strategies is difficult, nuanced, and heavily dependent on expertise. The field is moving so quickly that it is difficult to keep up with the pace of advancement. Highly talented people with expertise in GenAI are simply not available to most organizations.

Similarly, selecting, standardizing, and updating the software and tools associated with GenAI, MLOps, and even DevOps is challenging for companies without dedicated teams to keep up with these changes as the requisite tech stacks are constantly evolving.

Gen AI Adoption by Industries (Use Cases)

While all industries are increasing their AI budgets, each industry has unique use cases. These range from Insurance companies looking to reduce claim processing times to eCommerce companies implementing customer service chatbots.

Financial Services

• Financial services companies look AI to help them enhance the customer experience, grow revenue, and increase operational efficiency.
• To help achieve these goals, financial services companies are looking to adopt AI to improve investment research, fraud detection, customer-facing process automation, and to power personalized chatbots.
• For investment research in particular, financial services companies are applying AI to summarize content, detect trends, and classify topics to improve investment decisions, resulting in increased revenue and improved operational efficiency.

Insurance

• Insurance companies look to AI to help them improve customer experience and improve operational efficiency. To help achieve these goals, insurance companies are looking to adopt AI to improve claims processing, fraud detection, and risk assessment/underwriting.
• For claims processing in particular, insurance companies believe that AI can help reduce time to process claims and reduce processing errors which will result in a better experience for their customers and improved operational efficiency.

Retail and eCommerce

• Retail and eCommerce companies look to AI to help them grow revenue, improve the customer experience, and increase operational efficiency.
• To help achieve these goals, Retail and eCommerce companies are adopting AI to improve the customer experience with more capable chatbots. They also want to improve operational efficiency with more productive content and marketing operations built on AI-generated product imagery and descriptions.
• These companies are also enhancing their operational efficiency with better forecasting, purchasing, pricing, and inventory management.

Logistics and Supply Chain

• Logistics and supply chain companies adopt AI to help them improve operational efficiency, improve customer experience, and grow revenue.
• To help achieve these goals, logistics and supply chain companies are looking to adopt AI for better inventory management and demand forecasting, improved route optimization, to deploy autonomous vehicles, and improve document processing throughput and quality. These tools directly impact operational efficiency, which has downstream impacts on the overall customer experience, with reliable delivery and fewer delays.
• For inventory management and demand forecasting, logistics and supply chain companies are adopting AI to help reduce costs, improve customer satisfaction, and improve forecast accuracy.

Key Takeaways

• GenAI is rapidly transforming the world, and businesses need to understand how to adopt this technology quickly or get left behind.
• The most significant AI and ML readiness trend has been the enormous impact of GenAI on companies, large and small, across all industries.
• Many companies plan to work with or experiment with foundation models, but many lack the expertise and tools to get these models into production.
• Most companies are adopting AI to enhance the customer experience, optimize operational efficiency, or improve profitability.
• Early adopters of AI are seeing the improved ability to develop new products or services, enhanced customer experience, and better collaboration across business functions.
• Companies that fine-tune foundation models find their most significant challenges are acquiring training data, ML infrastructure, and experiments across different models.

How C-Level & Business Leaders can get started with Gen AI

C-Level & Business Leaders should work with their data engineers to identify creative ways to discover new GenAI solutions and assess which solutions are likely to bring the most value to the company. GenAI is still in its infancy and companies must think outside the box to identify unique or hidden applications that will provide unique competitive advantage.

To get started experimenting to find new use cases, leaders need to ask themselves four questions:

1. Where do we have underutilized data that is critical for our business functions?
2. Can this data be easily used to fine-tune an existing generative AI model?
3. Can we transform this data into another format (from numerical data to visual data, for example) to leverage existing generative AI systems?
4. What outputs do we expect and where in our organization could these outputs be used?

Emerging use cases of Gen AI are identifying value drivers across five domains:

(1) Marketing:?We are seeing a?rapid emergence of new GenAI marketing apps, ranging from generative product design to content personalization. This also includes the automatic generation of user interface designs which can accelerate the creation of ad campaigns and product landing pages. In fact, some of these innovations can?instantly generate hundreds of campaign ideas?based on a single prompt, enabling an exponential growth in experimentation and personalization. These innovations create the opportunity to reduce marketing cycle times and improve delivery of more targeted campaigns.

(2) Training:?While tools like ChatGPT may not yet be ready for end user consumption, they can be used to create first drafts or summaries of existing knowledge bases. As stated before, these GenAI outputs need to be reviewed by humans to produce final content. In addition to text creation, there are emerging GenAI technologies, such as ElevenLabs and?Synthesia.io, that can generate voice and video. The?intersection of text, voice, and video?will accelerate training development workflows, which can help with employee onboarding, product training, and many other use cases.

(3) Code Generation:?One of OpenAI’s most notable innovations, Codex, can parse natural language and automatically generate code output. Codex is used to power Github’s Copilot which can generate code from text prompts. Copilot can be installed as a plugin to common coding tools such as Visual Studio Code. Code generation can rapidly?improve engineering productivity to the order of 88%, according to Github. It can also reduce quality issues and help developers focus on value-added activities such as testing and feature improvements.

(4) Enterprise Search:?Over the years organizations have acquired large amounts of internal knowledge, often organized in internal portals and search engines. However, access to this knowledge required navigation based on a previously designed index. GenAI has the potential to provide companies with a way to search for knowledge using text prompts. With enterprise search companies will have immediate access to all their corpus of data. This will enable companies to disseminate access to knowledge to a wider audience, and much faster. Early adopters of GenAI are?curating their internal knowledge base?and feeding it to GenAI models. Employees will be able to ask much more relevant questions when inquiring for knowledge. It will help them perform their jobs better and accelerate learning curves.

(5) Customer Service:?Nowadays, customers expect personalized experiences and demand more from the companies they do business with. GenAI can help companies meet these expectations by generating personalized recommendations and dynamic content, and providing fast and convenient customer support. In the past decade customers have become more digital-savvy, tech-savvy, and data-savvy. As a result, customers demand personalization and speed, across the channel of their choice (chat, mobile, web, person) at the time of their choice. Companies that can deliver intelligence at the point-of-contact with customers will stay ahead of the pack.

(6) Analytics:?GenAI can help companies generate data-driven insights that would not have been possible through traditional means. At the writing of this article, GenAI models struggle with providing accurate results and dealing with numerical or logical problems. However emerging performance results show GenAI models outperform traditional AI classification models, such as image classification, in both training costs and effort to implement. Since GenAI space is evolving rapidly I expect the accuracy shortcomings to be addressed in the coming months.

(7) Process Automation:?GenAI can help companies save time and resources by automating repetitive tasks and streamlining processes. This can reduce costs and increase efficiency, freeing up resources for more strategic initiatives. Early experimentation results are finding 50% to 90% improvement in tasks related to text content generation, with 85-90% accuracy.

How to structure Gen AI experimentation

Rapid experimentation will be important to accelerate GenAI adoption. By quickly testing and iterating on models and use cases organizations will learn what works and doesn’t work and will be better prepared to determine which approaches are most effective and are ready to scale.

Businesses will need to take a holistic view of their current and future models and identify use cases across a comprehensive pattern of opportunities. First, they will need to understand how GenAI will impact their customer experience. Second, companies need to decide how GenAI impacts product and service innovation. Third, companies will need to look at how Gen AI will impact operations and drive operational efficiency. Last, companies will need to build capabilities to scale GenAI adoption.

(1) Customer Experience:?focus on use cases that improve the end-to-end customer experience. These use cases include personalized product or service recommendations, chatbots and virtual assistants, dynamic content generation (e.g. website copy, marketing materials), and automated customer service. For example:

• By leveraging the power of GenAI, companies can improve customer engagement, increase customer satisfaction, and drive business growth
• Digital marketing and chatbot capabilities can benefit from improved GenAI NLP engagement

(2) Products and Services:?focus on use cases that help accelerate product innovation by automating repetitive and time-consuming tasks (e.g. automatically generate product descriptions, propose product features). For example:

• In Healthcare R&D, GenAI is used to propose new therapies and generate molecule combinations
• GenAI will be used to create more personalized product features (e.g. personalized terms, personalized medicine, and much more)

(3) Business Operations:?focus on efficiency, reduction of manual efforts, and content generation. For example:

• In Digital Marketing, GenAI is used to quickly generate and iterate on product images and marketing copy
• When responding to RFPs, organizations are using GenAI to develop the first drafts
• GenAI can summarize documents (e.g. meeting minutes, legal documents)
• Companies are testing with training LLM models with their own knowledge base. This will help employees access all enterprise knowledge using a natural language interface

(4) Gen AI Capabilities:?in this domain companies will focus on critical path capabilities that need to be in place to unlock adoption. For example:

• Responsible AI will enable companies to test AI models for bias and toxicity, and remove undesired behaviors and responses. This will give ELT, and regulators, the confidence to move forward in careful and thoughtful stages
• Data Science will help implement and fine tune models. They will be critical to continuously test with new LLM models and build special purpose models

“Experimenting with GenAI will require a deep understanding of the industry, technical expertise in Data Science and Responsible AI, and experience in product development” — Emad Mostaque, Founder & CEO Stability AI

How to prioritize Gen AI use cases

When considering GenAI experimentation, it is important to carefully prioritize use cases that is most likely to benefit from this technology. This may involve considering factors such as the type of content being generated, the target audience, and the resources available for experimentation.

Here is a preliminary list of criteria to help prioritize GenAI use cases:

(1) Value Creation:?This criterion refers to the potential impact of the GenAI use case on value creation. Use cases that have the potential to significantly improve revenue, reduce costs, or increase efficiency are likely to have a higher priority. This criterion will help create a flywheel effect and build momentum early on.

(2) Strategic Fit:?Next, organizations should pursue use cases that advance their business agenda and priorities. This includes improving customer experience, accelerating product launches, and reducing costs. For example, if a company's goal is to improve customer experience, a GenAI use case that can help create personalized recommendations or generate creative product descriptions.

(3) Functional Feasibility:?Focus on use cases that benefit from generation of text, image, voice, video, and other types of content. At the time of this article, there are several GenAI applications around text and images, and I expect other types of content to rapidly follow in the coming months.

(4) Technical Feasibility:?This criterion covers factors such as data availability, content curation, technical capabilities, security, data privacy, performance, and other non-functional requirements.

(5) Risks and Benefits:?Last, it is important to assess the risk of implementing GenAI use cases. One of the risks to consider is the protection of internal knowledge. Organizations need to keep in mind that data shared with public language models such as OpenAI’s ChatGPT can be accessed by anyone, and therefore we recommend the implementation of private models or the sanitization of data before sharing. Another risk to consider is the outputs of models which can be inaccurate or incorporate bias or toxic content. For now, it is recommended the use of GenAI for internal users only, instead of end customers. To address these risks, organizations need to establish a Responsible AI practice to monitor risks and provide guidance on implementation.

What is a robust Gen AI platform tech stack

The ideal GenAI platform for experimentation will depend on the specific use case and the resources available. Some of the key factors to consider when selecting a platform include the ability to quickly train and test models, the ability to integrate with existing systems, and the level of technical support available.

The GenAI experimentation platform tech stack can be structured in three architecture layers: Application, Model, and Data.

• Application:?Enables end users to interact with language models. This layer can include a text prompt user interface, an enterprise search engine, data visualization application, API integration, and many others. There are many proven technologies in this space including cloud native solutions.
• Model:?Trains and provisions AI language models to enable each generative use case. There is a growing number of large language model (LLM) platforms to choose from (OpenAI, Cohere, Microsoft Azure OpenAI) and we will see many more in the coming months. In addition, there are many developers building special purpose models and applications and publishing on model hubs like Hugging Face. An important capability to consider is Machine Learning Operations (MLOps) tools to enable activities such as model development, training, and deployment.
• Data:?Supports data ingestion, storage, curation, and enrichment. This layer of the platform stack tends to be mature and with many technology options to choose from, both on premise and cloud-based, with structured and unstructured data. One relevant aspect to consider is the ability to curate and index knowledge, and design it with a specific purpose to train GenAI language models. For example, organizations need to decide how to tag knowledge data with the right keywords, build data representations (known as embeddings), and experiment with training data design.

At this stage it is important that companies remain agnostic of LLM solutions and instead design an AI experimentation stack that reduces the switching cost between LLMs, models, and application technologies. This will help organizations test with various LLM models and select the ones that best fit the needs of their use cases.

How can Gen AI help to Innovators?

In today's rapidly evolving business landscape, it’s crucial for corporates to stay in touch with their markets and anticipate disruption from various sources. It follows that, to future-proof their businesses, corporates must adopt more proactive and forward-thinking approaches to innovation, going beyond “regular innovation strategies”.

In fact, these strategies typically involve incremental improvements or optimizations to existing products, services, or processes. In a market that is constantly evolving, they may not be sufficient.

On the other hand, “active innovation strategies” involve exploring new markets, developing entirely new products or services, or adopting new technologies or business models. These strategies require a higher level of investment and may involve more risk-taking and experimentation than regular strategies.

They also involve continuously scanning the environment for new opportunities and emerging trends, and being willing to adapt quickly to changing circumstances. In contrast, regular innovation strategies tend to be more reactive, responding to market demands or customer feedback rather than actively seeking out new opportunities.

GenAI tools and platforms represent a potent means for creating and implementing active innovation strategies. By enabling corporates to analyze extensive amounts of data, identify patterns and trends, and generate insights to inform their innovation strategies, AI facilitates the identification of new opportunities, the development and testing of new ideas, and the successful implementation of innovations.

Where and how is Gen AI changing the innovation process?

GenAI can help innovators generate new ideas, optimize processes, and analyze data to make informed decisions. However, it can’t replace the intuition and judgment of innovators. As innovators, we bring a unique combination of creativity, problem-solving abilities, and domain expertise to the table, which GenAI is not yet capable of replicating- GenAI can only work with the data it has been trained on.

On the other hand, innovators who use GenAI will likely have an advantage over those who do not. GenAI can help them analyze large amounts of data and identify patterns that would be difficult to discern otherwise. This can give them a competitive edge in fields where data analysis is critical, such as finance and healthcare. Furthermore, as GenAI continues to evolve, it’s increasingly integrated into various industries and fields. Thus, innovators who are able to leverage GenAI to enhance their work will be better positioned to succeed in these changing environments. However, it’s crucial to remember that your distinct skills and insights remain irreplaceable.

1. Natural Language Processing:

The AI branch "Natural Language Processing" (NLP) focuses on the interaction between computers and human language. It involves the ability of computers to process and analyze human language in a way that allows them to understand the meaning behind the words, as well as the context in which they are used. NLP can be especially useful for corporates that need to analyze large amounts of customer feedback, social media posts, or other unstructured data sources to gain insights into customer preferences, sentiment, and behavior.

An example of this is Otter, a meeting assistant. Otter is an AI-powered transcription and collaboration tool that efficiently transcribes and summarizes meetings, interviews, and other important conversations. With Otter, you can capture, share, and search for information seamlessly, making communication and collaboration more efficient and effective.

2. Customization:

Customization is a great way to cater to individual tastes by tailoring your offerings to meet their specific needs. With the help of AI, this process becomes even easier. Synthetic Users is a cutting-edge AI technology that allows product teams to test their innovations and user interfaces with simulated users. This saves a lot of time and resources, as getting user feedback through interviews can be a tedious and lengthy process.

Synthetic Users helps automate the process by simulating how real users would react to your innovation. It provides valuable insights such as the desirability of your product, suggested features to add, and optimization paths to take. All of this information is available within minutes. Using Synthetic Users, corporates can identify and fix potential issues before releasing their innovations to the market. This results in better user experiences and increased customer satisfaction.

3. Decision Making:

The power of AI lies in its ability to gather and analyze historical and other data. It will present facts and approximations to help you make better-informed decisions. As such, it has many applications, from medical diagnosis to trading to financial decisions.

Rationale is a powerful AI tool that helps corporates make data-driven decisions by analyzing large amounts of data and providing insights and recommendations. If you have a tough decision to make or are hesitating between alternatives, Rationale simplifies the decision-making process by generating a causal chain analysis with all possible consequences, valuable insights, and recommendations. This tool can also create pros and cons, SWOT, and Multi-Criteria analysis, enabling you to ultimately make rational decisions.

4. Content Generation:

AI is also capable of automating content generation. Seenapse, for instance, is a unique combination of human lateral thinking and AI’s speed that allows you to generate many divergent, creative possibilities in just a matter of minutes. This tool can help you come up with a multitude of different ideas for naming, content creation, video scripting, and more. These ideas can be used directly or as inspiration to further develop your creative thinking.

5. Automation:

Artificial intelligence technology has advanced to a point where it can make decisions and operate independently without human input. This is particularly evident in autonomous cars, where the vehicle is capable of making decisions on its own without input from the driver or passenger. Such technology is commonly referred to as “automation", and it’s often used to streamline complex workflows like logistics, legal processes, administrative tasks, and many more.

UIzard is an innovative design tool powered by AI that creates visually stunning and functional user interfaces. Using machine learning algorithms and other AI technologies, UIzard analyzes design trends and generates editable, customizable UI templates quickly and easily, even from screenshots, hand-drawn sketches, or mockups. These templates can be easily customized and adjusted to meet the specific needs and preferences of individual businesses. Using UIzard, corporates can enhance user experiences and increase customer engagement quickly and easily.

6. Data Management:

Artificial intelligence has proven to be extremely valuable when it comes to processing and extracting insights from large, complex, and unstructured datasets that would be impossible for humans to analyze. It has been successfully implemented in Smart Cities and cybersecurity, where it can be used to identify and address potential threats.

As the prevalence of AI-generated content continues to increase, it can become difficult to distinguish between content created by humans and content generated by AI. Check for AI is a useful tool that can identify whether AI was involved in the content creation process, which helps corporates ensure compliance with ethical and regulatory standards while mitigating any potential risks associated with the use of AI systems. By using Check for AI, it’s possible to confidently leverage the full potential of AI technology while maintaining ethical and legal standards.

Enhancing creativity, innovation and problem-solving with Gen AI

Using GenAI for innovators can enhance creativity and innovation by providing fresh perspectives and ideas. Here are some of the ways that GenAI tools can help drive innovation:

1. Idea Generation:?GenAI can generate a wide range of responses to text-based prompts, providing new ideas and perspectives that can inspire innovation.
2. Divergent Thinking:?GenAI can generate responses that are not limited by human biases or preconceptions, allowing for more divergent thinking and exploration of new ideas.
3. Problem-Solving:?GenAI can provide novel insights into complex problems, helping to drive innovation and improve problem-solving capabilities.
4. Enhanced Collaboration:?GenAI can facilitate collaboration by generating responses to prompts that are open to interpretation and can be built upon by others.

“It’s the things that don’t change – those basic human needs and wants – that should be the foundation of your strategy” — Bill Gates

GenAI is just a technology. People should be what you focus on first. Get clear on why people care you exist. Then you can ask, “how will GenAI help me deliver on these basic needs faster, cheaper, or better?”.

Business value of Gen AI means developing strategy, not just tactics

GenAI is one type of AI that executives suddenly want to try in their business, but to capture its value and manage risk in a sustainable way, executives need a sound, holistic and achievable Gen AI Strategy.

Consider the four key elements of any Gen AI Strategy to capture business value:

• Set?Gen AI goals, benefits and success metrics
• Tie your?Gen AI vision?to business impact
• Assess and mitigate major?Gen AI risks
• Prioritize?Gen AI initiatives?and use cases

4 Gen AI Strategy pillars keep you focused on business impact

Building an Enterprise AI Strategy inclusive of GenAI requires a rigorous approach — from developing a business-driven vision to planning which initiatives to adopt and why.

1. AI Vision:?Identify the strategic opportunities of generative or other AI models
2. AI Risks:?Prepare to assess and mitigate a range of AI risks
3. AI Value:?Remove barriers to capturing AI’s value effectively
4. AI Adoption:?Prioritize AI use cases based on business impact and feasibility

Feasibility is as or more important than Business Value

Identifying the most valuable use cases — should target concrete improvement projects coupled with tangible business outcomes. Feasibility is critical.

Typically, returns are higher when risk is high and feasibility is low, but projects that are impossible to accomplish with available technologies and data aren’t worth pursuing regardless of the apparent business value.

Feasibility criteria include:

• Technical:?How well can the existing technology options improve the stated business use case to the level of “state of the art”?
• Internal:?Considerations such as (lack of) culture, leadership, buy-in, skills and ethics.
• External:?Considerations such as (lack of) regulations, social acceptance and external infrastructure.

A use case with an outstanding contribution to business value and easy feasibility is either a breakthrough or the market is missing a great opportunity.

What’s your Generative AI Strategy?

My new proposal?AI-Driven Innovation Framework?show you the?Human + Machine superpowers. Have your team experience the use of Gen AI first hand, through a highly structured on-site or remote program, facilitated by AI strategists, designers, coaches and experts supercharged with “learning by doing” practices and latest curated list of tools.

Empower your team’s innovation process with the power of Gen AI. Get to grips with the tools and techniques that can fuel and speed up your innovation processes, co-create concepts in a few minutes, and get a clear view of what ethical Gen AI usage means for your business.

With our?AI-Driven Innovation Framework, your team will gain?hands-on experience co-creating concepts with AI through an end-to-end strategy and innovation process.?We’ll guide you through the maze of applying Gen AI in your specific context and innovation challenges. Take your team on an?AI-Driven Innovation Sprint?and define a?roadmap to integrate Gen AI Strategy into your organization.

I also give inspiring and actionable?Keynotes,?Workshops?&?PoC Development?that help your team spot and seize emerging opportunities.?Get in touch ?if you'd like to discuss an upcoming event, training, or innovation project.

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