In its 27th Annual CEO Survey, PwC asked successful CEOs in different industries about the benefits they expect to get from generative AI in the coming year. 68% of the respondents believed Gen AI will enhance employee efficiency, 44% betted on increased profitability, and 35% hoped that their investment will pay off in increased revenue. And PwC itself reports that its employees who systematically use Gen AI tools are 20%-40% more productive than their more conservative colleagues.
Do you also have high hopes for this technology?
Then keep reading to understand the generative AI value chain, which will help you get the most out of this technology. Also, this article includes tips from our generative AI development company that will help you streamline Gen AI adoption.
What is generative AI, and how can it transform your operations?
Let’s clarify the terminology first.
Gen AI differs from traditional AI technologies in its ability to produce new digital content, be it legal documents, report summaries, images, product designs, etc., while classic AI focuses on predictive analytics like forecasting demand.
Generative AI is also different when it comes to implementation and usage. Gen AI models are typically larger and need more computational power to build, train, and operate. They are immensely powerful, but they also come with unique challenges, such as hallucinations. You can learn more about the pros and cons of Gen AI on our blog.
The six building blocks of the generative AI value chain
Here are the six key links of the Gen AI ecosystem.
Let’s take a closer look at each of these building blocks.
Hardware
Generative AI models usually need enormous computational power, high-bandwidth memory, large storage systems, and efficient cooling equipment to function. As these models have grown exponentially for the past decade, traditional computer hardware isn’t up to the task anymore.
For example, the ELMo model for image recognition that was trained in 2018 contained over 20 million parameters. Google’s BERT, which came shortly after, already exceeded 300 million parameters. And the more recent large language models (LLMs) can easily contain hundreds of billions of parameters. For instance, GPT-3 has 175 billion parameters. OpenAI trained this model on 45 terabytes of data, which equates to a million feet of bookshelf space. GPT-4, which was released in 2023, comprises 1.8 trillion parameters, while the upcoming GPT-NeXT is expected to be 100 times more powerful than its predecessor.
Gen AI needs advanced chips and computational resources. It takes innovative processors, such as graphic processing units (GPUs) and even tensor processing units (TPUs) with accelerator chips to build and train these tools.
Such hardware is rather expensive. You can consider buying this equipment if your company falls into one of the following categories:
- You specialize in training Gen AI models for other companies
- Your Gen AI models operate in a private cloud
- You work in the security sector
- You are a telecommunications company that can’t upload customer data to the cloud due to regulations and privacy concerns
- You own a private data center or build data centers for other companies
For other organizations, it makes sense to implement Gen AI in the cloud.
Cloud platforms
Cloud infrastructure provides access to expensive computing and storage resources. It enables companies to use hardware on demand and scale rapidly as their business expands. So, instead of buying pricey GPUs and TPUs and installing comprehensive cooling systems, many organizations turn to cloud computing.
Currently, there are three major cloud service providers on the market-Amazon AWS, Microsoft Azure, and Google Cloud.
Keep in mind that you can combine on-premises and cloud deployment. For example, you can retrain a Gen AI model on your proprietary data on premises to avoid uploading the data to a third party. Afterwards, you can run the mode in the cloud.
Foundation models
Foundation models are built and pre-trained on massive datasets of private or public data, making them suitable for a variety of general-purpose tasks, such as generating realistic images and summarizing lengthy texts. One foundation model can power several independent applications developed by different companies.
Organizations can fine-tune these models on proprietary datasets to perform more specialized tasks. You can use a commercially available model and pay license fees, or you can opt for an open-source solution. The second option gives more room for flexibility and customization.
Building and training a foundation model from scratch is an extremely expensive process. OpenAI is believed to have spent at least $4 million on training its GPT-3 large language model, which drives many text-generating applications today. But price is not the only prohibitor. Building Gen AI models takes diverse expertise, including AI consultants who will design and build the model, data scientists to prepare the data, and domain experts to verify the output and give feedback.
You can find more information on how much Gen AI costs on our blog.
Applications
Apps serve as an interface between Gen AI models and the end user. Even though foundation models can complete dedicated tasks, they can’t deliver value without applications.
An LLM that was trained to generate high-quality text will just sit idle until someone develops an app that activates it. One company can utilize the same LLM to create applications for different use cases. For instance, an HR department can use this Gen AI model to generate vacancy descriptions, while customer support specialists can envelop the model in a chatbot app that interacts with customers, and yet another application can use this model to summarize documents.
You can hire an app development company to design and build an application that leverages a foundation model of your choice and fits seamlessly into your workflow. We also encourage you to visit our blog to learn more about the application development process and the associated costs.
MLOps tools
Companies need dedicated tools to deploy and maintain Gen AI models and adapt them if needed. And that’s where MLOps comes in.
MLOps tools and technologies enable AI teams to maintain and interact with the model. For example, the ITRex MLOps consulting services include aggregating and preparing data for model retraining, validating the model, implementing tools for performance tracking, building APIs to allow applications to interact with the model, deploying the model, and more.
You can learn more about what MLOps can do for your business on our blog.
Human talent
No matter how powerful, Gen AI is just a technology, and you need people to operate it. Skilled professionals are still in the driver’s seat for innovation, reliability, and ethical standards. A talented workforce will give you a fresh perspective on emerging opportunities, spot and correct the mistakes AI makes, and ensure that AI models are ethical and free of bias.
If you don’t have the required expertise in-house and don’t want to recruit new people on a full-time basis, you can opt for the dedicated team hiring model. You can contact a specialized outsourcing company that will suggest a list of trusted professionals for you to choose from. The people that you select will work for your company on a flexible schedule for the duration of your project.
The path to generative AI value delivery
After learning about the six pillars of a generative AI value chain, let’s dive into the steps that organizations can take to maximize the value of their Gen AI endeavors.
Step 1: Identify key use cases with the greatest potential
Did you know that the top five Gen AI use cases constitute 50%-80% of the overall value the technology can bring to your business? How can you identify these?
For every company, these mission-critical applications will differ. There are two approaches that you can follow to identify the relevant use cases for your company.
One option is to focus on short-term benefits and consider use cases that will give a rapid return on investment. Or you can look into the technology’s long-term potential and search for ways to transform your processes entirely. In this case, your CTO and the tech team will work closely with business stakeholders to take a holistic approach to change and rethink your business processes. In the end, this team will come up with a global technical roadmap for possibly disrupting the business in its current state.
If you take the second approach, generative AI can enable your company to deliver value in novel ways, leading to an exponential revenue increase.
Step 2: Assess the potential value, risks, deployment speed, and costs for each candidate use case
Make a strategic assessment of the potential value that each use case can add to your businesses and the risks and difficulties associated with AI implementation. Besides values and risks, you can also consider other factors, such as deployment time, the associated costs, scalability, and complexity.
Additionally, it’s important to consider your company’s corporate culture, existing workflows, and core products and services and evaluate your business partnerships, competitive landscape, and regulations.
Step 3: Select your Gen AI tools
Now it’s time to choose the foundation models, cloud providers, AI consultants, and any other partners, vendors, and tools that you might need during your Gen AI journey.
When it comes to foundation models, it’s not feasible for most organizations to build them from scratch. Especially since there are many off-the-shelf solutions that were trained on large datasets to perform specific tasks. You can select one of these. Should you follow this path, it’s recommended that you retrain the readily available models on your proprietary data to achieve superior performance. But you could also use a ready-made Gen AI tool as is under the following circumstances:
When you don’t have any proprietary data to fine-tune the model. You can still retrain it if you obtain the data in the future.
When the task you want the model to perform is generic, like analyzing customer sentiment on social media, and an existing model already excels at it
Open-source vs. commercially available Gen AI models
There are two types of off-the-shelf Gen AI models that you can retrain and customize:
Open-source models that you can use for free
Commercial models where you pay licensing fees
Let’s take a closer look at each type.
| Open-source models | Commercial models | |
|---|---|---|
| Characteristics |
Smaller Better optimized to limit memory usage while making computations Tailored to perform a specific task, such as code completion |
Larger Perform well on generic tasks like text summarization |
| Ease of integration into your workflows | You need a programmer to integrate the model into your workflows | Easy to integrate, as the vendor offers you an API to access the model |
| Scalability | If the model is deployed on your premises, you might need additional/more powerful servers. If you rely on a cloud provider, they can handle it for you. | The number of interactions with the model grows, resulting in larger licensing fees |
| When to use each model |
Use open source when: You don’t want to share your data with a third party You are planning to use the model intensively, and a commercial solution will be expensive Your use case is rather specialized You want to minimize upfront investments |
Use commercial models when: You won’t use the model very often You want it to easily integrate into your workflows You’re exploring a generic use case, such as sentiment analysis You’re looking to rapidly prototype your Gen AI solution |
| Deployment | Your in-house or outsourced AI team deploys the Gen AI solution | The Gen AI model vendor deploys the model on their premises |
| Maintenance | You are responsible for the solution’s maintenance | The vendor is responsible for ongoing maintenance and model updates |
| Associated costs | You can use the model for free, but you need to handle deployment and maintenance | There are ongoing model usage fees that increase proportionally with the workload |
Choose your architecture approach
During this step, you also need to decide on the architecture approach and address questions, such as:
- Will one Gen AI model be enough, or do you need to combine several models into a pipeline?
- How will this pipeline look?
- How will the models interact with each other?
Step 4: Retrain and customize the selected model(s)
As mentioned above, a ready-made generative AI model is seldom enough as is. Companies, in most cases, need to familiarize the selected foundation model with the specifics of their business. This will also give you an edge over the competition that took this model without change.
To fine-tune a Gen AI model on a proprietary dataset, firms need to collect and aggregate this data, prepare it for AI consumption, and make sure it’s bias-free and representative of the target population. Also, address any ethical concerns, including data privacy, and obtain consent when needed.
If you already have a data management framework, it will save you time and money. If you don’t, then this is the right time to establish one.
Step 5: Deploy, test, and adapt when needed
Deploy
After deciding which foundation models you want to use, it’s time to think about where to host them and how to scale in the future.
If you opted for a proprietary Gen AI model, the vendor will deploy the model on their premises and scale the allocated resources as your operations expand. You will just have access to an API through which you can interact with the model. But if you choose an open-source solution, you have several options:
Deploy the Gen AI model on your premises. This option is expensive as you need to purchase all the hardware, and even more hardware if you want to scale in the future.
Rely on a cloud vendor who will allocate servers based on your demand, allowing you to easily scale horizontally and vertically. But if you start scaling horizontally, you will still need to manage request distribution, deciding which request goes to which server.
Test
As both technology and your business evolve, you might want to systematically reassess your generative AI tools to make sure they are still fit for their purpose. Besides business relevance, it’s preferable to audit the models for accuracy and prejudice, such as bias. If left unnoticed, these can cause inconveniences and, in the worst case, hefty fines and reputational damage.
Adapt
If you find that the model produces erroneous output, which can happen if you introduce new, unfamiliar data, you can retrain it with an updated dataset. If that won’t be enough, you can go back to Step 3 to look for a different Gen AI model.
Step 6: Scale to other use cases
After you successfully deploy Gen AI for one application, you can look for other related use cases that can benefit from this Gen AI tool. Scaling the technology to the next application will be much cheaper and faster.
Even though you can expand Gen AI to other existing applications, you might also consider reimagining some of your workflows with the help of the technology.
Strengthen your generative AI value chain
Contact AI consultants
Are companies actually reaping the benefits of Gen AI?
As we hear about Gen AI everywhere, it might seem that most companies have implemented the technology and are already reaping the rewards. Or are they? In its recent study, the Boston Consulting Group (BCG) surveyed senior executives across ten sectors and found that only 10% of the companies are scaling their Gen AI initiatives, with 50% being stuck in the piloting stage and 40% still observing and not taking any action.
Companies that haven’t experimented with the technology yet can still start their Gen AI journey and, according to BCG, even catch up with their piloting competitors. But they need to act fast. The longer they postpone the decision to collaborate with generative AI consultants and put the technology to work, the wider the gap becomes.
Here are valuable tips from ITRex that you can use along with the path to generative AI value delivery described above.
Tips from ITRex that will help you streamline Gen AI adoption and minimize costs
Maintain an up-to-date backlog of your Gen AI initiatives. Document any tasks and processes that can benefit from AI and specify how the technology can improve them. Assign a responsible person/department who will carry on the following tasks:
Systematically update the document with input from different stakeholders
Validate the potential of each entry using a simple metric with business impact, implementation complexity, and risks
Enforce testing the entries that passed the assessment. The initiatives that pass the testing phase can serve as use cases for AI implementation in the future.
This tip is not limited to AI. You can use the same approach with any cutting-edge technology.
In the very beginning, don’t start from scratch. Use a ready-made model that you can access through an API to test your hypothesis.
Adapt your organization’s AI guide. You probably created this document when preparing for classic AI, and it may not be suitable for the speed and scale of Gen AI-powered tools.
Combine Gen AI with big data and traditional AI tools for better results
Make sure your staff relies on Gen AI for the right tasks. A study by Boston Consulting Group indicates that employees who use ChatGPT for tasks that the model is not designed for tend to perform worse than their colleagues who don’t use Gen AI.
Don’t forget that Gen AI models can hallucinate. Arrange the workflows in a way that these errors can be easily captured and corrected. For instance, use the human in the loop approach or let employees fully take over the last mile of a task that can’t be safely automated.
Beware that AI can open security vulnerabilities, amplify human bias, and cause ethical concerns. In fact, AI is extremely susceptible to cyberattacks. It’s vulnerable at the code level, during training, after deployment-practically, at any stage.
Keep in mind that when using commercial ready-made Gen AI models, you are sending your data to the vendor, possibly causing a data privacy breach. For instance, if you are using a GPT model, you voluntarily submit your data to OpenAI, a company that was accused of breaching privacy rules.
Opt for responsible AI. With this strategy, you will set accountability and governance policies so that your people will uphold legal and ethical standards and minimize the risk of negative outcomes. With responsible AI, the technology powers your applications, but humans still control the process.
ITRex as your trusted partner in the generative AI value chain
Investing in the generative AI value chain building blocks and following the six steps described in this article will help you with Gen AI deployment. But the success of such projects also depends on the people involved. ITRex will make a great Gen AI partner that will assist you every step of the way. Here is why.
We offer an AI proof of concept (PoC) service that enables you to test different Gen AI options quickly and without investing in a full-scale project. You can find more information in our comprehensive AI PoC guide.
ITRex will look for the optimal set of tools for your project. Our team will evaluate different alternatives and conduct a quick PoC to identify the best fit in terms of costs, quality, and time to market.
We have partnerships with major cloud providers, including Google, Amazon, and Microsoft
We have a lot of experience in different IT domains, including classic AI, embedded development, back-end development, data, and so on, which allow us to holistically look at the solution and provide the best option for how to add value to your business. We can combine Gen AI with any other technology to amplify its transformative impact.
We have experienced data consultants who will help you with data management. Data will be a big part of your Gen AI efforts, and having a strong data strategist on the team will be an advantage.
We invest heavily in our AI team’s education. We encourage them to constantly learn and look for innovative ways to apply the technology and resolve implementation challenges. This is probably something that every tech vendor will tell you, but our consultants are actually paid to learn during working hours. And our clients will reap the full benefits of this approach, as our team might already have a solution to your problem without the need to conduct a PoC or do extensive research.
And don’t be afraid to experiment with generative AI. As ITRex CEO Vitali Likhadzed says:
Companies need to learn to work with cutting-edge technologies, be it Gen AI, IoT, or anything else. Establish a dedicated department, even if it’s a small R&D unit, that can deal with technology that is still not fully understood. You can copy the approach of successful innovation departments at other companies. Invest in attracting the right talent and pay attention to people whose ideas seem unconventional. And don’t be afraid of failure; just make sure you restrict the budget allocated to R&D experiments. One disruptive breakthrough can compensate for all the efforts.
Cutting-edge technologies involve considerable uncertainty and risks, and people don’t like uncertainty. But it also provides a great opportunity for you to leave the competition far behind.
– Vitali Likhadzed
…………………………………………………
Originally published here
The post Zooming in on the Generative AI Value Chain appeared first on Datafloq.
Source link
lol
