As the CEO of Slang.ai, I’ve always been fascinated by the intersection of technology and human experiences, especially within the realm of hospitality. The focus on customer service and the overall experience, the high energy and pace of restaurants and the ability to create memorable dining moments have always inspired me to explore how advanced technologies like Artificial Intelligence (AI) can revolutionize the way we engage with guests and optimize operations.

We realized early on when we were creating Slang.ai, our restaurant phone/concierge service based on AI, the profound shift in how technology can impact a business and a guest’s experiences. This can also have a major impact on overall and operational efficiency. As an advocate for advanced technology, I am deeply invested in exploring the transformative potential of AI within the hospitality sector, particularly its role in reshaping the restaurant landscape.

The Current State of the Restaurant Industry

Before delving into AI’s impact, it’s crucial to understand the challenges facing the restaurant industry today. In recent years, restaurants have dealt with evolving consumer preferences, intense competition, and rising operational costs. The COVID-19 pandemic accelerated the need for innovative solutions to adapt to changing consumer behaviors and ensure business continuity.

One of the fundamental shifts in consumer expectations revolves around personalized experiences. Diners seek more than just a meal—they want a memorable interaction and tailored services that cater to their preferences. This shift necessitates a paradigm change in how restaurants operate and engage with their patrons.

AI: Empowering Personalization and Customization

AI has many uses in the restaurant industry. It has emerged as a game-changer – from enabling restaurants to analyze vast datasets and glean actionable insights to personalize guest experiences, and phone answering services.

Imagine a scenario where a guest, based on their reservation, is greeted by name, and offered their favorite dishes based on past dining history. This level of personalization not only enhances guest satisfaction but also cultivates customer loyalty—a critical factor in a competitive market.

AI-driven recommendation engines can anticipate diner preferences, curate customized menu suggestions, and even adjust portion sizes based on individual dietary requirements. By leveraging AI, restaurants can elevate service standards and create memorable dining experiences tailored to each guest’s preferences.

Predictive Analytics: Anticipating and Responding to Consumer Trends

Beyond personalization, AI-driven predictive analytics empowers restaurants to anticipate consumer trends and adapt their offerings accordingly. By analyzing historical data such as order patterns, dining preferences, and seasonal variations, AI can forecast demand, optimize inventory levels, and fine-tune pricing strategies in real-time.

For instance, a restaurant equipped with AI-powered analytics can predict which dishes are likely to be popular on a given day, adjust ingredient procurement to minimize waste, and optimize menu pricing based on market demand. This proactive approach not only enhances operational efficiency but also ensures that restaurants stay agile and responsive to changing consumer behaviors. And, it will sync with the phone answer system so when guests call or inquire about new menus, or dietary questions, AI can respond with accurate and updated information.

Enhancing Operational Efficiency through Automation

AI’s impact extends beyond guest interactions to streamline restaurant operations and enhance overall efficiency. Automation powered by AI can revolutionize various facets of restaurant management, including reservations, order processing, inventory management, and customer service.

Consider the role of AI in handling customer inquiries and reservations – reducing wait times and enhancing service efficiency. In the kitchen, AI can optimize food preparation processes by predicting cooking times, minimizing errors, and improving consistency. AI-enabled analytics tools provide actionable insights to restaurant managers, enabling data-driven decision-making to optimize resource allocation and drive profitability.

Addressing Labor Challenges

Another significant benefit of AI adoption in the restaurant industry is its potential to address labor shortages and rising labor costs. With AI-powered automation handling routine tasks, restaurant staff can focus on delivering personalized service and enhancing guest experiences versus answering the phones or taking reservations. This shift not only improves operational efficiency but also empowers employees to leverage technology as a tool to augment their skills and productivity.

Preserving the Human Element of Hospitality

Despite the transformative power of AI, it’s essential to strike a balance between technology-driven innovation and the human touch that defines hospitality. As the renowned chef Julia Child famously said, “The secret of a successful restaurant is sharp knives and sharp people.” Technology should complement—not replace—the passion, dedication, and expertise of restaurant staff.

AI serves as an enabler that empowers restaurant teams to elevate their service standards and focus on creating meaningful connections with guests. By leveraging AI to automate repetitive tasks and streamline operations, such as answering the phones, restaurants can allocate more resources to training and empowering their staff to excel in delivering exceptional experiences.

The Road Ahead: 

As we look ahead, the integration of AI into the restaurant industry represents a transformative opportunity to redefine guest experiences and operational efficiency. AI’s ability to take on all the aforementioned tasks underscores its potential as a catalyst for innovation.

Successful AI adoption requires a strategic approach that prioritizes collaboration, experimentation, and continuous improvement. Restaurant owners and operators must invest in AI technologies that align with their business goals, engage with industry experts to navigate implementation challenges, and cultivate a culture of innovation that embraces technology as an enabler of excellence.

As we embark on this transformative journey, let us embrace AI as a catalyst for innovation while preserving the timeless principles of hospitality that define our industry. Together, we can usher in a new era of dining excellence where technology enhances—rather than diminishes—the human connection at the heart of every memorable dining experience.

Let’s continue to explore, experiment, and innovate, guided by our shared vision of leveraging AI to create a more personalized, efficient, and sustainable future for the hospitality industry.

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Background:

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

Code optimization in AI is all about maximizing results while minimizing inefficiencies and operational costs. It’s a key factor in driving the success and sustainability of AI initiatives in the dynamic and competitive landscape of modern businesses.

Code Optimization:

What are some common challenges and issues businesses face with code optimization when implementing AI solutions?

Businesses implementing AI solutions often encounter several challenges with code optimization, mainly due to the dynamic and complex nature of AI systems compared to traditional software optimization. Achieving optimal AI performance requires a delicate balance between code, model, and data, making the process intricate and multifaceted. This complexity is compounded by the need for continuous adaptation of AI systems, as they require constant updating to stay relevant and effective in changing environments.

A significant challenge is the scarcity of skilled performance engineers, who are both rare and expensive. In cities like London, costs can reach up to £500k per year, making expertise a luxury for many smaller companies.

Furthermore, the optimization process is time- and effort-intensive, particularly in large codebases. It involves an iterative cycle of fine-tuning and analysis, demanding considerable time even for experienced engineers. Large codebases amplify this challenge, requiring significant manpower and extended time frames for new teams to contribute effectively.

These challenges highlight the necessity for better tools to make code optimization more accessible and manageable for a wider range of businesses.

Could you share some examples of the tangible benefits businesses can achieve through effective code optimization in AI applications?

AI applications are subject to change along three axes: model, code, and data. At TurinTech, our evoML platform enables users to generate and optimize efficient ML code. Meanwhile, our GenAI-powered code optimization platform, Artemis AI, can optimize more generic application code. Together, these two products help businesses significantly enhance cost-efficiency in AI applications.

At the model level, different frameworks or libraries can be used to improve model efficiency without sacrificing accuracy. However, transitioning an ML model to a different format is complex and typically requires manual conversion by developers who are experts in these frameworks.

At TurinTech AI, we provide advanced functionalities for converting existing ML models into more efficient frameworks or libraries, resulting in substantial cost savings when deploying AI pipelines.

One of our competitive advantages is our ability to optimize both the model code and the application code. Inefficient code execution, which consumes excess memory, energy, and time, can be a hidden cost in deploying AI systems. Code optimization, often overlooked, is crucial for creating high-quality, efficient codebases. Our automated code optimization features can identify and optimize the most resource-intensive lines of code, thereby reducing the costs of executing AI applications.

Our research at TurinTech has shown that code optimization can improve the execution time of specific ML codebases by up to around 20%. When this optimized code was tested in an Azure-based cloud environment, we observed cost savings of about 30% per hour for the virtual machine size used. This highlights the significant impact of optimizing both the model and code levels in AI applications.

Are there any best practices or strategies that you recommend for businesses to improve their code optimization processes in AI development?

Code optimization leads to more efficient, greener, and cost-effective AI. Without proper optimization, AI can become expensive and challenging to scale.

Before embarking on code optimization, it’s crucial to align the process with your business objectives. This alignment involves translating your main goals into tangible performance metrics, such as reduced inference time and lower carbon emissions.

Empowering AI developers with advanced tools can automate and streamline the code optimization process, transforming what can be a lengthy and complex task into a more manageable one. This enables developers to focus on more innovative tasks.

In AI development, staying updated with AI technologies and trends is crucial, particularly by adopting a modular tech stack. This approach not only ensures efficient code optimization but also prepares AI systems for future technological advancements.

Finally, adopting eco-friendly optimization practices is more than a cost-saving measure; it’s a commitment to sustainability. Efficient code not only reduces operational costs but also lessens the environmental impact. By focusing on greener AI, businesses can contribute to a more sustainable future while reaping the benefits of efficient code.

Generative AI and Its Impact:

Generative AI has been a hot topic in the industry. Could you explain what generative AI is and how it’s affecting businesses and technology development?

Generative AI, a branch of artificial intelligence, excels in creating new content, such as text, images, code, video, and music, by learning from existing datasets and recognizing patterns.

Its swift adoption is ushering in a transformative era for businesses and technology development. McKinsey’s research underscores the significant economic potential of Generative AI, estimating it could contribute up to $4.4 trillion annually to the global economy, primarily through productivity enhancements.

This impact is particularly pronounced in sectors like banking, technology, retail, and healthcare. The high-tech and banking sectors, in particular, stand to benefit significantly. Generative AI is poised to accelerate software development, revolutionizing these industries with increased efficiency and innovative capabilities. We have observed strong interest from these two sectors in leveraging our code optimization technology to develop high-performance applications, reduce costs, and cut carbon emissions.

Are there any notable applications of generative AI that you find particularly promising or revolutionary for businesses?

Generative AI presents significant opportunities for businesses across various domains, notably in marketing, sales, software engineering, and research and development. According to McKinsey, these areas account for approximately 75% of generative AI’s total annual value.

One of the standout areas of generative AI application is in data-driven decision-making, particularly through the use of Large Language Models (LLMs). LLMs excel in analyzing a wide array of data sources and streamlining regulatory tasks via advanced document analysis. Their ability to process and extract insights from unstructured text data is particularly valuable. In the financial sector, for instance, LLMs enable companies to tap into previously underutilized data sources like news reports, social media content, and publications, opening new avenues for data analysis and insight generation.

The impact of generative AI is also profoundly felt in software engineering, a critical field across all industries. The potential for productivity improvements here is especially notable in sectors like finance and high-tech. An interesting trend in 2023 is the growing adoption of AI coding tools by traditionally conservative buyers in software, such as major banks including Citibank, JPMorgan Chase, and Goldman Sachs. This shift indicates a broader acceptance and integration of AI tools in areas where they can bring about substantial efficiency and innovation.

How can businesses harness the potential of generative AI while addressing potential ethical concerns and biases?

The principles of ethical practice and safety should be at the heart of implementing and using generative AI. Our core ethos is the belief that AI must be secure, reliable, and efficient. This means ensuring that our products, including evoML and Artemis AI, which utilize generative AI, are carefully crafted, maintained, and tested to confirm that they perform as intended.

There is a pressing need for AI systems to be free of bias, including biases present in the real world. Therefore, businesses must ensure their generative AI algorithms are optimized not only for performance but also for fairness and impartiality. Code optimization plays a crucial role in identifying and mitigating biases that might be inherent in the training data and reduces the likelihood of these biases being perpetuated in the AI’s outputs.

More broadly, businesses should adopt AI governance processes that include the continuous assessment of development methods and data and provide rigorous bias mitigation frameworks. They should scrutinize development decisions and document them in detail to ensure rigor and clarity in the decision-making process. This approach enables accountability and answerability.

Finally, this approach should be complemented by transparency and explainability. At TurinTech, for example, we ensure our decisions are transparent company-wide and also provide our users with the source code of the models developed using our platform. This empowers users and everyone involved to confidently use generative AI tools.

The Need for Sustainable AI:

Sustainable AI is becoming increasingly important. What are the environmental and ethical implications of AI development, and why is sustainability crucial in this context?

More than 1.3 million UK businesses are expected to use AI by 2040, and AI itself has a high carbon footprint. A University of Massachusetts Amherst study estimates that training a single Natural Language Processing (NLP) model can generate close to 300,000 kg of carbon emissions.

According to an MIT Technology Review article, this amount is “nearly five times the lifetime emissions of the average American car (and that includes the manufacture of the car itself).” With more companies deploying AI at scale, and in the context of the ongoing energy crisis, the energy efficiency and environmental impact of AI are becoming more crucial than ever before.

Some companies are starting to optimize their existing AI and code repositories using AI-powered code optimization techniques to address energy use and carbon emission concerns before deploying a machine learning model. However, most regional government policies have yet to significantly address the profound environmental impact of AI. Governments around the world need to emphasize the need for sustainable AI practices before it causes further harm to our environment.

Can you share some insights into how businesses can achieve sustainable AI development without compromising on performance and innovation?

Sustainable AI development, where businesses maintain high performance and innovation while minimizing environmental impact, presents a multifaceted challenge. To achieve this balance, businesses can adopt several strategies.

Firstly, AI efficiency is key. By optimizing AI algorithms and code, businesses can reduce the computational power and energy required for AI operations. This not only cuts down on energy consumption and associated carbon emissions but also ensures that AI systems remain high-performing and cost-effective.

In terms of data management, employing strategies like data minimization and efficient data processing can help reduce the environmental impact. By using only the data necessary for specific AI tasks, companies can lower their storage and processing requirements.

Lastly, collaboration and knowledge sharing in the field of sustainable AI can spur innovation and performance. Businesses can find novel ways to develop AI sustainably without compromising on performance or innovation by working together, sharing best practices, and learning from each other.

What are some best practices or frameworks that you recommend for businesses aiming to integrate sustainable AI practices into their strategies?

Creating and adopting energy-efficient AI models is particularly necessary for data centers. While this is often overlooked by data centers, using code optimization means that traditional, energy-intensive software and data processing tasks will consume significantly less power.

I would then recommend using frameworks such as a carbon footprint assessment to monitor current output and implement plans for reducing these levels. Finally, overseeing the lifecycle management of AI systems is crucial, from collecting data and creating models to scaling AI throughout the business.

Final Thoughts:

In your opinion, what key takeaways should business leaders keep in mind when considering the optimization of AI code and the future of AI in their organizations?

When considering the optimization of AI code and its future role in their organizations, business leaders should focus on several key aspects. Firstly, efficient and optimized AI code leads to better performance and effectiveness in AI systems, enhancing overall business operations and decision-making.

Cost-effectiveness is another crucial factor, as optimized code can significantly reduce the need for computational resources. This lowers operational costs, which becomes increasingly important as AI models grow in complexity and data requirements. Moreover, future-proofing an organization’s AI capabilities is essential in the rapidly evolving AI landscape, with code optimization ensuring that AI systems remain efficient and up-to-date.

With increasing regulatory scrutiny on AI practices, optimized code can help ensure compliance with evolving regulations, especially in meeting ESG (Environmental, Social, and Governance) compliance goals. It is a strategic imperative for business leaders, encompassing performance, cost, ethical practices, scalability, sustainability, future-readiness, and regulatory compliance.

As we conclude this interview, could you provide a glimpse into what excites you the most about the intersection of code optimization, AI, and sustainability in business and technology?

Definitely. I’m excited about sustainable innovation, particularly leveraging AI to optimize AI and code. This approach can really accelerate innovation with minimal environmental impact, tackling complex challenges sustainably. Generative AI, especially, can be resource-intensive, leading to a higher carbon footprint. Through code optimization, businesses can make their AI systems more energy-efficient.

Secondly, there’s the aspect of cost-efficient AI. Improved code efficiency and AI processes can lead to significant cost savings, encouraging wider adoption across diverse industries. Furthermore, optimized code runs more efficiently, resulting in faster processing times and more accurate results.

Do you have any final recommendations or advice for businesses looking to leverage AI optimally while remaining ethically and environmentally conscious?

I would say the key aspect to embody is continuous learning and adaptation. It’s vital to stay informed about the latest developments in AI and sustainability. Additionally, fostering a culture of continuous learning and adaptation helps integrate new ethical and environmental standards as they evolve.

Leslie Kanthan

Chief  Executive Officer and Founder at TurinTech AI

Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform – EvoML – for building and scaling AI.

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The rise of Generative AI (GenAI) has enormous potential for the banking and finance industries. By utilizing GenAI, banks and credit unions speed applications from submission to approval, save time and effort, and deliver a desirable customer experience.

A recent report from the Society for Human Resource Management (SHRM) and The Burning Glass Institute details how GenAI will have an outsized role on the banking and finance industries. The report lists Morgan Stanley, Bank of America and Northwest Mutual as some of the organizations that are most likely to capitalize on the implementation of GenAI. Their study also measures GenAI exposure among several different professional industries; “investment banking and securities dealing and brokerage” measured third highest while “mortgage and nonmortgage loan brokers” ranked highest overall. If SHRM and The Burning Glass Institute are so convinced that GenAI will profoundly alter how financial institutions operate, what will that change look like and why does it matter?

GenAI is distinct from other forms of automation by its ability to automate what is typically considered knowledge work. This represents a sea change in how professional industries, including financial services, will implement automation technology in their workplaces. In fact, financial services are especially dependent on repetitive manual processes requiring specialized knowledge. Processes like loan underwriting and credit card applications require knowledge workers to manually input data and individually connect with customers or members, which takes up the majority of workers’ time and tasks.  GenAI excels in automating repetitive, manual tasks—such as data processing and pattern identification—streamlining operations and freeing up valuable time for knowledge workers.

The applications of GenAI within financial services manifest in both evident and nuanced ways, each offering distinct advantages to forward-thinking institutions. Many industries have begun employing GenAI solutions as chatbots for customer service, and financial services are no exception. GenAI-powered chatbots, operational around the clock, offer an immediate response to customer inquiries, significantly reducing the need for direct intervention by skilled professionals and enhancing service efficiency.  However, these solutions become even more compelling for financial institutions when embedded in the bank or credit union’s broader systems. For example, a loan applicant can interact with a GenAI-enabled chatbot and get a real-time status update on their loan status by providing a few identifying details. In this way, GenAI increases efficiency while also directly improving the customer or member experience.

As lending processes are vitally important to any bank or credit union, they are also ripe for GenAI enhancement. Much of lending involves knowledge workers reaching out to customers and acquiring all the documents and data necessary to complete the loan application. This can be a tedious process that can take days or weeks to complete. With GenAI solutions, not only can financial institutions instantly reach out to applicants requesting documents, but those same solutions can also automatically identify all necessary documents by analyzing the application status without a human ever laying eyes on it. Some tools can even go one step further by identifying the document category, extracting data from the documents and validating them against the loan application instantly. Imagine your own knowledge worker advancing loan processing overnight and over the weekend!

Additional GenAI solutions go beyond immediate revenue-generating processes for bank and credit unions. These tools can assist with marketing efforts by creating personalized offers based on account information. GenAI-enabled marketing tools can detect patterns in account behavior and automatically generate personalized offers based on customer preferences. Just like with lending processes, GenAI tools can then automatically reach out to customers and members via email, SMS or other communication channels. These marketing efforts again increase efficiency, improve the customer or member experience and create more lending opportunities.

GenAI’s distinctive ability to automate professional tasks, particularly in the realm of financial services, where repetitive manual processes dominate, is reshaping traditional workflows. The adoption of GenAI solutions, from chatbots enhancing customer service to automating lending and revolutionizing marketing efforts, signifies a paradigm shift towards efficiency, improved customer experiences and increased lending opportunities. 

GenAI technology is novel, and its implementations are sure to evolve further in the coming months and years. However, its potential for financial services is undeniable. In order for banks and credit unions to take full advantage of this nascent technology, financial institutions need to create AI policies, complete digital transitions and start exploring and investing in GenAI use cases now. 

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Hello, Joyce, please share your professional journey and how it has brought you to your current role as the Head of Generative AI at Amperity.
I’ve always been fascinated by decision-making and how we can make more informed decisions, which is what drew me to statistics and machine learning. I started my career in consulting and then joined Custora, a customer analytics platform, as a founding member of the product team. Over a five-year period, we were able to grow Custora from a minimum viable product (MVP) to a product used by seven of the top 20 brands by ecommerce revenue. Custora was acquired by Amperity in 2019. Since joining Amperity, my focus has been AI, ML, and ML Operations in the service of helping brands leverage their first-party data. When using ML models in the wild, ensuring they’re reliable, can be re-trained frequently, and are built on a robust identity-resolved data foundation is essential to success. ML Operations is particularly important here and often overlooked. Over the last year, we realized there was so much value we could provide to customers in the Gen AI space, and I stepped in to lead our Gen AI efforts. Gen AI will democratize data access, unlock more data-driven decision-making, make personalized experiences a reality, and give brands the opportunity to reduce resource constraints in the realms of creative and customer service.

What sets Amperity’s AI capabilities apart from other tools currently available on the market?
Amperity’s use of AI is two-fold: creating the world’s best unified customer profile and enabling easy access to this profile. We use our patented, AI-powered Stitch algorithm to create customer profiles that comprise of how a single customer has interacted with a brand across many touchpoints. We then augment the profile with our predictive capabilities to calculate key metrics that represent critical moments in a customer’s lifecycle.

Amperity’s second use of AI is to enable brands to access that profile. Using generative AI, we enable users to easily ask and answer questions about their customer profile without needing to write SQL or have a deep knowledge of the underlying schema. The goal is to use generative AI to make every organization more data driven and customer-centric and every individual capable of deriving actionable insights from the data.

Let us know about the benefits AmpAI provides for its users, and how do these advantages contribute to their business objectives?
With Predict, we can more accurately anticipate customer behavior than what would be possible with historical metrics. This impacts the business in a number of ways, including improved return on investment on paid media. For example, Amperity customer Brooks Running saw a 128% increase in return on ad spend (ROAS) after implementing Predict.
With Amperity’s generative AI capabilities, brands can derive actionable insights and make more informed, data-driven decisions that ultimately grow the business. Amperity customer Bobit stated, “Before Ai Assistant, we turned to our developers to write SQL, often with a turnaround time of days. With Ai Assistant, my team can write queries and provide answers to Bobit’s leadership in a matter of minutes. Ai Assistant has pushed us to be more data-driven and empowers my team.”

In light of increasingly stringent data privacy regulations, how does AmpAI ensure compliance while delivering valuable insights to its users?
AmpAi prioritizes compliance without compromising insight. All of the GPT modeling behind AmpAi is run on Azure open AI service, meaning that none of the data is used to train OpenAI’s models or improve Microsoft services and no data is stored in between application programming interface (API) calls. Additionally, all of the data used is first-party customer data, making it more reliable, accurate and consensual. The goal is to democratize data access and enable the business user to quickly ask and answer questions about their customers.

Could you elaborate on the personal strategies you employ to remain innovative and effective in the dynamic field of AI development and data analytics?
There are two ways I remain innovative and effective in the evolving AI and data space:
There is no substitute for being hands-on with technology. While reading is helpful, I try to always have a side project where I’m experimenting with a new technology. I find that I learn the most when I’m working on something.
AI is changing at a rapid speed and sometimes it can feel difficult to keep up. I always ask myself what’s likely to remain the same and what’s likely to quickly change. I invest my time in learning about technologies and principles that are relatively durable and am more high level with areas I think will quickly evolve.

As a respected leader in AI technology, what advice would you offer to individuals aspiring to pursue a career in this specialized field?
To those looking to enter the field, I recommend building a solid foundation in data science and machine learning, coupled with practical experience. Stay curious, embrace challenges, and never stop learning. The field of AI is as much about solving complex problems as it is about understanding the nuances of human behavior.

As we wrap up, are there any final reflections or key insights you would like to share regarding the significance of AI in shaping the future of business operations?
AI is not only reshaping business operations; it’s fundamentally altering how we understand and interact with customers. The key to success lies in using AI not only as a tool for automation but also as a way to deepen brands’ connections with consumers, making every interaction more personalized and meaningful.

Looking ahead, what trajectory do you envision for AmpAI as it continues to evolve and adapt to meet the evolving needs of businesses in the data analytics landscape?
AmpAi will continue to evolve and improve, introducing more products under Assist and Explore to further democratize access to data. We aim to make every user, regardless of technical expertise, capable of harnessing the power of customer data to drive innovation, efficiency and growth.
As AI evolves, the unified customer profile will be critical in making personalization resonate with target audiences downstream. There will be many tools emerge that focus on personalization across content creation, AI concierge and customer service. But it’s essential that brands can lean on a trusted customer data foundation to power personalization efforts to ensure they’re reaching the right customers with the right strategies at the right time.

Joyce Gordon

Head of Generative AI at Amperity

Joyce is the Head of Generative AI at Amperity, leading product development and strategy. Previously, Joyce led product development for many of Amperity’s ML and ML Ops investments, including launching Amperity’s predictive models and infrastructure used by many of the world’s top brands.  Joyce joined the company in 2019 following Amperity’s acquisition of Custora where she was a founding member of the product team. She earned a B.A. in Biological Mathematics from the University of Pennsylvania and is an inventor on several pending ML patents.

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Leads are gold — but ineffective management lets them slip through your fingers. Fewer than 30% of leads ever get contacted, and that’s costing you business. Speed matters, and in today’s hyper-competitive world, automation plugs the leak.

Forget tedious manual processes. Lead management automation closes the gap between leads and sales. But it’s not just about conversions. Imagine:

• No more wait times or frustrating transfers. Customers land with the right agent quickly the first time.
• Personalized service at scale. Say goodbye to repetitive information gathering.
• More ways to connect — via text, email or live chat — and shorter response times.

AI-powered software seamlessly handles lead management tasks at scale, from startups to enterprise contact centers. It’s changing the game and unlocking the potential for delivering superior customer service, improving productivity and maximizing budgets. 

Ready to convert more leads into customers? Here are six powerful tools that empower you to do more with less and unlock each department’s potential. 

Virtual agents

Don’t allow business hours to define your connections. Your customers live in a 24/7 world, and so should your service. Enter virtual agents: your always-on concierge for instant engagement.

These AI-powered bots handle everyday tasks like appointment scheduling, password resets and qualification verification, freeing human agents for high-impact tasks like contacting and closing hot leads more quickly.

Virtual agents act as the first line of defense, resolving common issues and routing complex leads. This omnichannel approach ensures customers connect with the right person fast. Seamless hand-offs between virtual and human agents also reduce repetition for customers. Other benefits?

• Hot leads get priority treatment — no more languishing in voicemail purgatory.
• Agents can focus on building relationships where the real magic happens.
• Frustrated customers become satisfied fans thanks to instant, always-open support.

Time zones and busy schedules shouldn’t hold you back. Leveraging virtual agents for low-level tasks allows companies to transform customer experiences leading to higher satisfaction and a sharper competitive edge.

AI for conversational intelligence

Forget the paper chase and endless spreadsheets. Automation enables agents to spend time following up with prospects instead of scoring them.

AI-powered tools use call recordings and transcriptions to instantly sort, classify, summarize, and score every interaction, with instant scoring triggering next steps like confirmation texts, personalized email follow-ups or callbacks. 

Companies can use AI to  automatically sift through massive data sets to uncover insights like:

• Product preferences hidden in call patterns.
• High-converting keywords driving sales.
• Channels attracting the most qualified leads.
• Customer sentiment and intent across every interaction. 

This real-time intelligence benefits every department. Sales and marketing can target the right leads with laser precision. Product development can leverage insights to build the products customers want. And customer service is empowered to deliver faster, better service.

AI-powered scoring isn’t just about efficiency — it’s about growth. With it, you can uncover trends, identify customer needs and pain points, develop targeted campaigns, improve productivity, optimize strategies and watch your business soar. Automation tools maximize the impact of every sales rep, turning their focus from manual data entry or analysis to converting high-value leads.

Modern call tracking

Rather than blind marketing in industries like behavioral healthcare, home services or healthcare where customers and prospects call you, connect those calls to the campaigns that drove them — with laser precision. 

While traditional tracking leaves gaps, call tracking reveals every touchpoint from the first online search to the final “hello” on the phone.

Unleash the power of your data to see exactly which ads, channels and keywords attract real, live leads. You don’t have to throw money at campaigns and hope something sticks. AI analysis does the heavy lifting, summarizing key points and highlighting golden nuggets.

• Instant insights trigger automated actions, like appointment reminders, agent queues and even personalized greetings based on a caller’s unique journey.
• Speed to lead skyrockets as agents leverage touchpoints, interests and searched keywords to prioritize hot leads.
• Conversations flourish, with personalized interactions helping to close deals faster because customers feel heard and understood.

Call tracking isn’t just about data. It’s about transforming your marketing into a conversational machine. Getting full attribution data is key to connecting offline activities like calls and texts back to their source, especially in industries where prospects typically engage offline.

Customizable IVR menu

Ditch the phone maze and embrace personalized paths with customizable interactive voice response (IVR) menus.

IVR is the friendly concierge for callers. Personalized routing based on requirements, past interactions and even caller ID guides customers to the exact department or agent they need rather than forcing them to navigate a confusing labyrinth of menus — making their experience smooth and satisfying. 

According to Salesforce, 89% of consumers are more likely to purchase again after a positive customer experience. Customizable IVRs offer more than just convenience. They’re a strategic investment in customer satisfaction and business growth. You can track how callers navigate an IVR to identify pain points and optimize the menu for even better experiences. 

It’s like having a real-time feedback loop to constantly improve customer service. And since one size never fits all, you can create unique menus for different customer segments, ensuring every caller feels valued and understood. 

Smart Router

Imagine stepping into a store and immediately being greeted by a friendly expert who whisks you away to the exact department you need. That’s the magic of call routing: scrapping the robotic prompts and frustrating hold times for personalized journeys that turn customers into evangelists.

Think of a smart router as a GPS for phone calls. This intelligent tool uses your defined criteria to direct each caller to the most efficient path. The technology can direct callers based on the campaign they responded to, location, and day and time. It can also recognize past interactions and connect callers to an agent they already know and trust, fostering deeper relationships.

The best part? You’re not limited to simple rules. This smart tool lets you build sophisticated “if/then” workflows:

• If a caller mentions X in their query, route them to the product specialist team.
• If it’s past business hours, direct them to our AI assistant for immediate help.
• If they’re a return customer, offer them priority call-back options.

The endless possibilities allow you to tailor the experience to everyone’s unique needs. You’ll have happier customers, empowered agents and smarter insights. 

Click-to-call form technology  

Online lead forms are great, but what happens once someone clicks the submit button? Too often, these leads languish in digital limbo and don’t reach the sales team before they’ve gone cold. 

An AI-powered lead automation tool, however, wastes zero time. Once a form is completed, it triggers an automatic call-back or text message. In other words, it’s proactive engagement rather than passive waiting, which:

• Connects prospects to sales teams within minutes when interest is fresh and excitement high.
• Drives higher prospect engagement and increases lead conversion rates.
• Closes more deals with instant nurturing via agents connecting with leads personally to build rapport and trust from the get-go.

Marketers live by the holy trinity: attract, nurture, convert. AI-powered lead management automation tools supercharge the pipeline and make conversions effortless by integrating with existing workflows to turn more prospects into customers.

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Elad, Can you please share your personal journey with robotics from childhood to founding RobotLAB. How has the perception and utility of robots changed over the years?

I’ve loved robots since I was a child. I loved building them, programming them and inventing new creations, but for many years robots were “toys for geeks.” They needed soldering, understanding of electronics and in general, not for 99% of the people, so it stayed as a hobby. Even when I started my first company (MassiveImpact, which focused on optimizing ads on text messages for mobile operators) and moved to Taiwan for 7 years, I always visited the vibrant electronics markets on weekends, looking for developments in sensors, motors, controller boards etc.

Then a bit after the invention of the iPhone in 2007, I started seeing a new wave of what we call today “connected toys” in the markets in Taiwan. It was a tsunami of cars, drones, balls, and other smart objects that are smartphone-enabled. This brought me back to my old passion and showed me the market was finally ready for a robotics company, as all of these smartphone-connected toys can actually be programmed to do many other things. After all, apps on smartphones are just a programming interface.

I sold my shares in my company and started RobotLAB, focusing on introducing innovation into classrooms and bringing to life math, science and physics for students. We created lesson plans, curricula and everything teachers needed in order to teach these abstract concepts using robots. We couldn’t find everything teachers wanted, so we had to build some of the robots ourselves!

Back in 2007, there were no service, delivery or cleaning robots. It took a few more years for these products to mature and become reliable enough to where we are today, with a vast portfolio of robots that can do everything business owners need them to do.

In the past few years alone, we’ve seen the demand for these products skyrocket. We can’t keep up with the demand, with it coming from everywhere (around the country and beyond) and all at once. That’s why we decided to implement franchising in our business model, so we can have multiple RobotLAB teams in every metro area. My vision is to make sure that whenever a business owner needs a robot, they can find a RobotLAB team in their backyard to be there same-day to assist them.

What is your vision for the future of robotics and its potential to further transform industries, education, and society as a whole?

Robotics has significant transformative potential. They will make industries more efficient and adaptable, introducing new business models and changing how sectors like healthcare and manufacturing operate. Robots can address challenges like elder care and urban efficiency.

They can also offer students hands-on learning, making education more tailored and effective. It’s not just about learning robotics; it’s about learning with them.

My vision for the future of robotics includes machines working alongside us, specifically in a more inclusive, equitable, and innovative world where technology amplifies the best of human capabilities.

Can you please provide a brief overview of RobotLAB and its significance in the field of robotics?

Since its founding in 2007, RobotLAB has provided turnkey robotics solutions to companies of all sizes in industries including foodservice, hospitality, banking, education, assisted-living, education, cleaning, delivery and hospitals. Our talented team of roboticists has effectively deployed thousands of robots that have provided businesses with a clear path to the successful and highly specialized integration of robotics solutions. 

As labor becomes increasingly expensive and scarce, we help businesses harness the power of robotics to improve bottom-line and employee retention by reallocating routine tasks to automated technologies. Our team oversees all aspects of the robotics integration process – from sales, tailored programming, on-site integration and repairs – to ensure businesses can access and understand solutions that will dramatically improve their performance. To improve the availability of robotics access nationwide, we recently launched a first-of-its-kind robotics integration franchise opportunity in 40 U.S. states, with the remainder set to clear before the end of 2023.

How did the idea of creating the world’s first robot franchise come about? What was the inspiration behind it?

As a leading robotics integration company, we recognized that the robotics market needed more local access to our services. We see an overabundance of demand for robots, but most resellers who ship direct from the manufacturer do not provide training, hands-on help and other necessary tools that make the most of the equipment. At RobotLAB, this A-to-Z service is what we do best and what sets us apart from all other robotics sellers or integrators.

We’ve seen great success on the B2B side, but we feel that RobotLAB needs to physically be everywhere to fulfill the demand we are currently facing. We’re franchising to expand not only where we operate locally, but who we operate with. Combining future-focused, aspirational talent with our successful infrastructure is a clear path to succeeding in equipping our modern society with progressive tools.  

Could you explain how robotics integration benefits both business owners and consumers? How does it contribute to the growth of the robotics ecosystem?

Robotics integration improves operations by means of efficiency, quality, flexibility and safety. Robots are able to streamline operations by reducing the time and cost associated with production or service delivery. They offer consistent output, reducing human error and ensuring standardization, while also offering the flexibility to adapt to market changes. Robots also reduce workplace injuries by taking over hazardous tasks.

On the consumer side, robots can work around the clock and ensure services or products are available when consumers need them. As robots alleviate redundant tasks from customer service workers, they are able to dedicate more time to improving the customer experience. 

Robotics integration fuels the growth of the robotics ecosystem as a result of the mutual positive feedback loop from businesses and consumers. This encourages further investment in robotic technology, leading to advancements and innovations. As more sectors adopt robotics, a vibrant ecosystem of developers, manufacturers, educators, and end-users emerges. This interconnected network accelerates the sharing of knowledge, best practices, and innovations, propelling the growth and evolution of the entire robotics industry.

How do you view the integration of Artificial Intelligence in the education sector? What potentials and challenges do you see in leveraging AI for educational purposes?

The main conversation around AI in education sectors so far is centered around how educational institutions can filter and check student work to see if AI wrote pieces of it. However, there’s no feasible way of definitively knowing if something was written by a human or a robot since humans also have a tendency to sound over-structured. Educators should instead use AI to their advantage, like enhancing existing simulative educational systems with AI to mimic human behavior.

Could you share examples of how robots have enhanced customer experiences and operational efficiency in industries like restaurants, schools, hotels, and assisted living facilities?

In restaurants like La Duni, for example, owners are struggling to maintain a staffing level that is required to keep the business afloat. With the introduction of delivery robots, they have allowed their existing staff to fill the demand of customers. Not only has it remedied their labor shortage, but servers are much happier not having to do repetitive, manual tasks like moving dishes to each table. At this restaurant, and many that we deploy to, servers always see an increase in tips. They are able to handle more tables and offer better service without the mundane tasks in their day to day.

The shortage of low-skill workers in certain industries has led to increased adoption of robotic technology. How do you see the role of robots evolving in addressing workforce challenges in these industries?

Many of the robots we have today are alleviating the current labor shortage. The hospitality industry, for example, has seen the loss of many low-skill workers. With the introduction of cleaning robots, they can pick up tasks like vacuuming, mopping and scrubbing floors without human intervention. Delivery robots help restaurant servers focus on the more skilled aspects of their job, like delivering excellent service and customer-forward conversation.

Soon, many hotels will adopt delivery technology that brings room service to your door via a robot with a personalized code to unlock. Robots are already addressing workforce challenges and RobotLAB is bringing them to businesses to help overcome those challenges.

How do you see the relationship between humans and robots evolving as technology continues to advance?

As technology advances, the relationship between humans and robots will become more collaborative. Robots are tools, designed to enhance human capabilities. As they become more integrated into our daily lives, they’ll be seen less as distant machines and more as extensions of our own capacities.

In education, for instance, robots will serve as learning aids, making educational experiences more personalized and interactive. In industries, they will work alongside humans, taking on repetitive tasks and allowing us to focus on more value-added activities.

Elad Inbar

Founder and CEO of RobotLAB

Elad Inbar is the founder and CEO of RobotLAB (www.robotlab.com), a unique company dedicated to making robots smart and useful in multiple industries, including education, hospitality, restaurants, hotels, assisted living facilities, etc. His current ventures in robotics and education have received wide publication and recognition in Time Magazine, The New Yorker, Tech Crunch, IEEE, NBC, Financial Times, Fast Company, CNET, San Francisco Chronicle and other media outlets. He shares his experience as a keynote speaker in many events such as SxSW, National Restaurant Association, Florida Lodging and Restaurant Association, and TCEA, ACTE, FETC and many others. Elad also sits on the Forbes Technology Council. With parallel careers in academia and technology, Elad is uniquely qualified to bridge the cutting-edge robotics industry and the educational and retail markets.

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AI and ML are changing the way we live and work. Many people think they’re reserved for tech giants, however. But increasingly we’re seeing SMEs harness the power of these tools. And the benefits are clear: artificial intelligence and machine learning can improve operations, boost customer satisfaction and help companies to outpace the competition – all of which are essential if you want your business to not only survive but thrive. 

Interested in knowing more? Here we look at the benefits of AI and ML in the business world as well as the perceived challenges. 

Customer service

We’ve probably all communicated with a chatbot online when searching our favourite store’s website. In fact, many might not even realise that when you use the chat box function, you’re not actually speaking to a human. This is one of the best examples of how technology can be used to help a business as it offers 24/7 customer support without breaks or vacations, and it’s likely to save money over time too. Even better, these chat boxes provide instant responses to customers, whatever time of the day, meaning customers are better served and we know that is crucial for customer retention and loyalty. That’s not to say there isn’t a place for human customer service agents, instead, your human team can be deployed to other areas of the business and can tackle more complex issues. 

Administration

Data entry, accounts, general admin – these are all essential tasks for business owners to complete but it’s not always easy to find the time to dedicate to them. That’s where AI and ML come in. In fact, AI can automate data entry making it faster and error-free. It can even take care of administrative tasks, report generation, and appointment scheduling meaning you and your team can focus on business-critical tasks. With more time freed up, you’ll likely be able to respond quicker around the business and can put more time into your overall strategy. 

Decision-making 

Another benefit of AI is that it provides deeper insights and can analyse large amounts of data much quicker than a human could. This makes it even easier to predict market demand, understand specific customer preferences and optimise resource allocation.

The challenges in implementing AI

Despite the benefits of AI and ML, we can’t ignore the challenges surrounding it. This includes the difficulties in managing vast data storage, recruiting skilled AI professionals, and not to mention the rapid changes in the AI landscape. Indeed, implementing AI isn’t a one-stop approach. Instead, companies need to continuously innovate to ensure they can keep pace with competitors. Another challenge regarding data privacy and seamless integration with existing systems can also not be ignored. 

That’s not to say AI can’t be implemented successfully or shouldn’t be. In fact, a whole host of SMEs can benefit from it. For example, clothing retailers could tailor their marketing based on customer preferences, manufacturers could predict demand and streamline their supply chain, law firms could draft documents quicker and more accurately and a restaurant could use the technology to optimise their staff during busy times. 

Of course, embracing AI might come with challenges, but the benefits far outweigh them. It just requires a little care and dedication and a strategy behind it. 

Chirag Shah, founder and CEO of Nucleus Commercial Finance and Pulse has over 20 years of experience in the financial services industry and a deep understanding of the needs of UK SMEs. 

In 2011, he founded Nucleus, a leading alternative finance provider, to offer flexible and tailored solutions for SMEs across various sectors and stages of growth. With an understanding of the challenges that UK SMEs face in the current economic climate, Chirag launched Pulse in October 2022, a free-to-use service that helps businesses and accountants gain insights into financial performance with AI-powered data visualisation and personalised dashboards. Chirag is not only committed to driving growth and innovation in the UK business ecosystem, but he’s also helping SMEs better understand their data to boost their profitability and guide them towards success. 

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Artificial intelligence (AI) has revolutionized data management, empowering organizations to leverage data for informed decision-making.This article explores the transformative impact of AI in data management, presenting three key ways it enhances insights. 

First, how AI automates critical processes, optimizing workflows and resource allocation. Second, how AI algorithms improve data quality by detecting and rectifying errors, ensuring reliable insights. Last, how AI enables businesses to make informed decisions by uncovering patterns and making accurate predictions.

Embracing AI in data management provides a competitive advantage, driving sophisticated decision-making and valuable insights across industries. This article will highlight the transformative potential of AI in data management, informing data decision-makers why it is essential to seize this opportunity for growth and success.

About the writer: With more than 20 years of experience in software engineering, Jay Mishra is an expert in product vision and development. Jay is the Chief Operating Officer for Astera Software, where he focuses on product development and strategic planning. Jay holds a Master of Science degree in Computer Science from Virginia Tech and a Bachelor of Science in Mathematics and Computing from the Indian Institute of Technology.

Data: it is the backbone of businesses, enabling informed decision-making, enhanced customer service, and innovation. However, effectively managing data presents challenges, from collection to storage and analysis. 

Integrating unstructured data is a challenging task due to its diverse formats and lack of structure. Managing this type of data has historically required extensive manual labor and complex systems to ensure the data is properly extracted. Even with a team of experts, there is still a risk of human error, from missing fields to duplications and inconsistencies. 

The rise of artificial intelligence (AI) is revolutionizing data management practices, ushering in a new era of efficiency and efficacy. Large language models such as ChatGPT, Bing, and Google Bard are transforming both the speed at which we can process data, and the way we can use and understand that data. 

Just as the advent of Excel revolutionized data processing and analysis, AI represents a new frontier in data management capabilities. While Excel brought the power of spreadsheets to the masses, large language models harness the capabilities of advanced language models to process and analyze data in a conversational manner. Unlike Excel’s structured and formula-based approach, AI’s natural language processing abilities enable users to interact with data in a more intuitive and conversational manner. 

Using AI, businesses can now query, explore, and gain insights from their data using everyday language, eliminating the need for complex formulas and technical expertise. This opens up new possibilities for users of all backgrounds to effortlessly leverage data in their decision-making processes. 

The pace at which this technology is advancing can be dizzying, and while many businesses understand that it is vital to embrace AI, it can be challenging to understand how to best apply these integrations. Fortunately, many software companies, including Astera, are integrating AI for customers, so that all you have to do is decide what to do with the tools you’ve been given. 

Here are three ways AI can help your business improve data management and insight. 

Automating Tasks

Advanced language models like ChatGPT play a pivotal role in streamlining data management by automating critical processes such as collection, cleansing, and analysis. By harnessing the power of AI, businesses can optimize their data management workflows.

For example, AI-powered data extraction can revolutionize document processing in industries such as healthcare. Medical facilities deal with a vast amount of patient records, lab reports, and insurance forms on a daily basis. With AI, these documents can be automatically scanned and processed, extracting relevant information and populating databases with speed and precision. This eliminates the need for manual data entry, reducing human error and saving valuable time for healthcare professionals.

Moreover, AI-powered data extraction can automate report model generation in less time with more accuracy. Large language models can identify data fields, perform semantic matching, and reverse-engineer layouts to automate template creation. This can easily automate template creation and streamline workflows for various processes, such as generating purchase orders, managing shipping documents, and organizing medical records. By leveraging AI in data management, businesses can achieve greater efficiency, improve accuracy, and free up human resources to focus on more strategic tasks.

Improving Data Quality

AI contributes to enhancing data quality by identifying and rectifying errors while enriching data with additional relevant information. Through AI-powered algorithms, duplicate records, missing data, and inconsistencies can be detected and rectified, resulting in more reliable and valuable data for decision-making purposes. Using AI means less room for human error as well as human fatigue, as this tedious task can be completed in a fraction of the time it would take a human to sift through hundreds – or even thousands – of documents to extract data. This improved quality drives better business outcomes across various industries such as finance, healthcare, and insurance. 

AI-powered data quality solutions can streamline data enrichment processes. Financial institutions often rely on data from various external sources, such as credit bureaus or regulatory databases, to assess creditworthiness or compliance. AI algorithms can automatically gather and integrate relevant data from these sources, ensuring the completeness and accuracy of information. This enriched data enables financial institutions to make more informed decisions and provide personalized financial services to their clients.

Incorporating AI into data quality management within fintech not only enhances the accuracy and reliability of data but also enables financial institutions to stay ahead in a fast-paced and highly regulated industry. 

Enabling Better Decisions

AI empowers businesses to make more informed decisions by uncovering patterns, generating insights, and making accurate predictions. By analyzing vast datasets, AI algorithms can identify trends, predict customer behavior, detect fraud, and optimize supply chains. Armed with these insights, businesses can take proactive measures to enhance customer satisfaction, mitigate risks, and optimize operations.

For instance, in the banking sector, financial institutions often deal with vast volumes of unstructured data contained in documents like bank statements, invoices, and financial reports. Manually extracting and analyzing this information can be time-consuming and error-prone.

With AI-powered tools, algorithms can automatically extract relevant data from unstructured documents, transforming them into structured formats for further analysis. This enables businesses to gain valuable insights into customer spending patterns, identify potential risks, and optimize financial decision-making. By automating the data extraction process, these tools eliminate the need for manual data entry, reducing errors and improving operational efficiency.

Moreover, the AI capabilities of these tools can be applied to various industries beyond banking. For example, in the healthcare sector, the tools can extract critical patient data from medical records, facilitating accurate diagnoses and personalized treatment plans. In the insurance industry, they can automate the extraction of policy information from complex insurance documents, improving underwriting processes and claims management.

It is clear that AI is reshaping the landscape of data management, automating tasks, enhancing data quality, and facilitating better decision-making. As large language models and other capabilities continue to advance, we can anticipate even more innovative applications. By tapping into these advanced algorithms and user-friendly interfaces, businesses can harness the power of AI to transform their data management processes and gain a competitive edge in today’s data-driven world.

Best Practices for AI Integration 

• Do establish clear goals and objectives: Clearly define your desired outcomes and objectives for integrating AI into data management. Identify specific areas where AI can add value and align those goals with your overall business strategy.

• Don’t solely rely on AI without human oversight: While AI can automate many data management tasks, it is crucial to maintain human oversight. Human experts can provide critical context, verify results, and ensure that AI-generated insights align with business goals and ethics.

• Do invest in quality data: Ensure that your data is clean, accurate, and properly organized before integrating it with AI. Quality data serves as the foundation for effective AI-driven data management.

• Don’t ignore security and privacy:  Implement robust security measures to protect sensitive data throughout the AI integration process. Comply with relevant data protection regulations and ensure that privacy controls are in place to maintain the confidentiality of data.

• Do continuously evaluate and improve: Regularly monitor the performance of AI algorithms and data management processes. Assess the impact of AI integration on data quality, efficiency, and decision-making. Continuously optimize and refine your AI models to adapt to evolving business needs.

• Do consider the scalability and flexibility of your data management solutions. As your data grows, ensure that your infrastructure and algorithms can handle the increased workload effectively. Also, plan for future changes and advancements in AI technology to ensure long-term success.

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Customer service will never be the same, especially in the banking industry where artificial intelligence has taken a giant leap towards understanding human behaviour.

Banks in the Middle East are now testing AI customer service agents that can detect a variety of human emotions, including anger and frustration, over the phone. 

If being able to detect emotion wasn’t remarkable enough, this AI is also capable of identifying and understanding different accents, dialects, and linguistics.

Technology like this is increasingly beneficial to the Middle East, where culture gaps between other countries and regions is pivotal in the making of personal connections. The AI is programmed with popular EQ phrases that can help it reduce tension with a customer who is angry or frustrated with the bank’s customer service.

This latest generation of AI technology can even determine which gender the customer prefers to speak with, allowing it to respond with that gender’s voice. All of this is done without the customers knowledge meaning they might not even realize they are speaking with an AI agent. This helps create a more personalized experience the customer is likely to remember and enjoy.

The AI is capable of learning this information thanks to previous customer interactions, which are stored and maintained within the bank’s customer databases, allowing continuous improvement so future interactions can be more meaningful which bridges the relationship gap between banks and their customers. 

Our fintechstartup, Flaist, developed this technology earlier this year and we have several patent claims pending. We’ve also been accepted into two different prominent accelerator programs – Techstars Hub71 and Startup Bootcamp Dubai.

The best part about this AI customer service agent is that it is plug-and-play for banks. It’s simple for banks, credit unions and even smaller community banks to install the technology on their back-end as a white label solution. The AI solution quickly integrates within enterprise applications like websites, mobile apps, and social communication platforms with ease. This technology is also customizable, allowing banks and financial organizations to rebrand it under their own name and color schemes on their website and apps such as Facebook messenger and Whatsapp.

The digital toolkit of APIs and microservices that are integrated into this technology help streamline and enhance customer service interactions which helps banks overcome the challenge of creating meaningful relationships with their customers.

The technology can also free up human customer service representatives, allowing these financial institutions to reallocate their resources to other areas. Early pilot programs have shown the AI customer service platform can cut IT costs by up to 25 percent, while providing the bank with new opportunities to sell existing services and increase customer loyalty.

The development of this new AI technology is effectively democratizing the digital transformation process. It’s allowing financial institutions regardless of size and resources to provide quality customer service interactions that compete with all levels of banking.

And the best part, this tech advancement is giving smaller financial institutions such as credit unions, community, and regional banks the resources to access top AI technology and provide better customer service. 

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