AI-Led Data Transformation for Data Leaders
Let’s explore practical steps to assess the technical feasibility of generative AI and set your business up for success. A supportive work environment rewards innovation, collaboration, and personal growth. “You’ll be surprised at how quickly some teams will outpace others in leveraging these technologies, accelerating the transformation, ideating new products, and leading pilot projects.
Organizations that start now rather than waiting gain the advantage of early adopters.
Choosing the Right Generative AI Tools
Krista provides a single platform to deploy, monitor and manage AI solutions in an enterprise. Its intuitive dashboard allows businesses to quickly identify areas of improvement and monitor performance. Krista’s AI iPaaS also offers a no-code studio for non-technically skilled business users to modify conversations and automation logic to provide greater flexibility and speed. Effective generative AI must combine data from company-specific knowledge, understand the requester’s context, and maintain security, as not every answer should be accessible to all employees. For instance, when inquiring about vacation days, employees may be asking about PTO, sick leave, or flex days. The AI must provide answers considering the employee’s country, state, employment type (full-time, part-time, or contractor), and other relevant factors.
“I think the risk is not that there’s new technology. The risk is that we don’t lean in and don’t invest the extra time, extra hours. Because it needs a little bit of time; it is a little complex,” he said. “Everyone needs to lean in, learn. That, I think, is the largest risk. Then you have divergence in society.” Commercial real estate is “centered at delivering insights,” so adoption of AI is necessary, Salumeh “Sal” digital and information officer, told BI. Ancestry is concerned with hate, abuse, historical facts being misrepresented, and AI hallucination, Thiagarajan said.
How to Integrate Generative AI into Your Enterprise
Encouraging your team to attend conferences, participate in online courses, and engage in knowledge-sharing activities will help them stay at the forefront of generative AI integration. Generative AI, with capabilities like text completion, summarization, and content generation, can handle small amounts of proprietary data, typically between 2,000 and 6,000 bytes, or about 3 to 4 pages of content. However, such limited data per request may prove insufficient for many enterprise use cases, requiring the input of larger datasets to obtain relevant and contextual answers from backend systems. Even experienced employees can make mistakes, and correcting even minor errors can take hours out of the work week. In some industries, data mistakes can cost more than time, as they may result in lost business, financial errors or misdirected strategic decisions. That makes AI in corporate finance and similar sectors a highly competitive advantage.
In a more sensitive application, like responding to cyber threats, those savings are even more impactful. AI can identify suspicious activity and isolate the suspicious user or application immediately, preventing costly data breaches. Organizations that don’t capitalize on AI may quickly fall behind their competitors that do, but this integration can be challenging if leaders don’t know where to begin. To integrate AI into your business, you must first understand what specifically it can do for you.
An AI integration platform logs activity to support compliance and role-based access. The same logs support long-running conversations and automations to help employees and customers on longer journeys. For instance, employee onboarding is sometimes a year-long process that includes initial data entry at the beginning of an employee’s tenure through training and then follow-ups and reviews. To facilitate these extended interactions, AI tools must recognize users and identify their positions within their individual journeys on several types of processes and workflows.
Generative AI is also often referred to as “prompt AI,” where you only need a simple query input to generate quality results – no tweaking of models or parameters required. This ease of use supports the potential to more broadly leverage the AI that was once typically reserved for coders exclusively. Generative AI could be compared to the first low-code platforms, but with the added elements of user-friendliness and high-quality output, making it more accessible than its predecessors.
Assessing Your Business Needs
As a new technology that is constantly changing, many existing regulatory and protective frameworks have not yet caught up to generative AI and its applications. A major concern is the ability to recognize or verify content that has been generated by AI rather than by a human being. Another concern, referred to as “technological singularity,” is that AI will become sentient and surpass the intelligence of humans. In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of applications. DALL-E is an example of text-to-image generative AI that was released in January 2021 by OpenAI. It uses a neural network that was trained on images with accompanying text descriptions.
This level of understanding is crucial because it enables AI systems to offer tailored assistance and insights, fostering a sense of continuity and familiarity for users. Deploying generative AI inside your enterprise quickly is a challenging endeavor. Traditional software development cycles are slow and time-consuming stifling the speed of innovation. Most IT teams are near or at capacity and can’t fulfill the sudden demand to deploy the different AI tools that the business is demanding. If there is capacity, by the time you select and deploy an AI model or service, the scope will change and there will be hundreds of new AI alternatives to choose from. It’s no wonder that so many organizations are struggling to build generative AI applications into their enterprises quickly and securely.
Training and upskilling your team
The services-oriented cloud architecture ensures more flexibility and a higher fault tolerance. With generative AI baked in, MicroStrategy ONE has everything you need to modernize your data processes right out of the box. For instance, language models like GPT-3 can generate human-like text, while image generators like DALL-E can create images from textual descriptions. It’s essential to familiarize yourself with the capabilities and limitations of these models to select the right one for your business.
It’s crucial to address potential challenges related to latency to maintain a seamless user experience. By understanding the augmenting power of generative AI, you can empower your employees to take on new responsibilities and focus on critical and creative tasks that require a human touch. “In this environment, it’s important that leaders demonstrate their usage of AI applications for productivity gains, such as email drafting or content creation. “These tools can significantly increase productivity, but their usage should align with your organization’s risk appetite and data privacy policies.
When assessing the technical feasibility of generative AI, it’s essential to evaluate its business viability based on unit economics and margins. By involving legal experts from the outset, you can establish a strong foundation of compliance, building trust with customers, stakeholders, and regulatory bodies. Kate also pointed out that understanding your customers’ perceptions of generative AI is key.
To ensure that your generative AI tools are delivering value and meeting your business objectives, it’s crucial to track and analyse their performance. Monitor the KPIs you defined in your AI integration plan and use data analytics to gain insights into how well the AI tools are performing. This will help you identify areas where the AI is having a positive impact and areas where improvements can be made.
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