Technologies play a role in how AI is implemented. Learn how tech has influenced AI trends this summer from the experts.Â
Manufacturing Companies Are Slowly Integrating AI
AI is being investigated by many participants in the manufacturing sector, both large and small companies. However, only large companies like Siemens, FANUC, some major robotics companies, and larger automotive and aerospace firms, as well as pharmaceutical companies, can afford to implement AI meaningfully.
AI is still too early in its development cycle to have numerous ready-made applications, making it difficult to implement. AI applications need to be built on a case-by-case basis since there are no off-the-shelf manufacturing applications that use AI natively. As a result, only large companies are currently taking advantage of AI.
Despite this, there is widespread excitement about AI, with many companies starting to use it at the ChatGPT level, such as writing better marketing copy, which is an excellent use case. The challenge, however, is that AI applications are slow to develop because they require a lot of data to be effective. Manufacturing is a great industry for AI as it generates a lot of measurable data and hard facts. But most manufacturing companies are under-digitized, so medium-sized and smaller companies are rapidly trying to digitize their data and create AI-ready repositories. They know they will benefit greatly from AI once they accomplish this, but it is a big and expensive task. Consequently, adoption will be slow, except at the highest levels. – Rhonda Dibachi, CEO at HeyScottie
Hackathons Hit the Wall
2023 and early 2024 saw a raft of internal projects leveraging public AI-as-a-Service vendors for prototyping. However, the gap between prototype and productionisation will lead most of these projects to hit a wall and require working with specialized vendors who can amortize deeper R&D across many customers. – Dev Nag, CEO/Founder at QueryPal
An Influx of Point Solution Companies Implementing AI
The market is still really strong and bullish on GenAI solutions that can create new industries and categories or disrupt existing ones.
Sectors such as medicine, healthcare and financial services are seeing a massive influx of companies creating point solutions that deploy AI in new ways that create value.
For example, tons of companies in the healthcare space are using AI to create new drugs and treatments that would not exist without the massive compute and processing power available to them via the AI boom. – Matt Biringer,CEO at North
Reimaging Computing Experiences and Infrastructure
AI-assisted Linguistic Services in Healthcare
In healthcare, applying AI technology to language and interpreting services has yet to become standard practice when assisting patients with limited English proficiency (LEP) – but that is about to change. It is common knowledge that providers are bound by law to provide linguistics services support to patients in their language of choice. That means that live interpreters typically can be found in hospitals, particularly the emergency room (ER). Beyond the ER, however, most LEP patients are on their own, trying to decipher a hospital menu to order a meal or when simply asking for help. AI can and should be considered to fill patient touchpoint gaps, especially in non-emergent medical situations. Another reason to consider AI application: America is home to 46.2 million immigrants, with over three-quarters holding legal status, marking the highest population in U.S. history as of 2022. Investment in AI-assisted language solutions can help healthcare leaders successfully address three top motives to better serve their non-English-speaking patient population: Cost, efficiency, and quality and engagement. Moreover, AI-assisted enhancements help to level up the quality of the interpreting experience and vastly improve patient compliance and outcomes. – Dipak Patel, CEO at GLOBO Language Solutions
Using AI to Automate Sales
I think one area where we are starting to see the applied use of AI is in the AdTech/MarTech vertical, which is applicable to all businesses and not just travel. Those of us who live and breathe marketing [my entire career has been in technology-based marketing]have now spent the past few years dabbling with generative AI in content creation and workflows. But now, we marketers are looking at how we can automate sales and not just marketing. We have experimented with conversational agents and chat/telephony ourselves, and have seen others experiment here too, with the corporate direction to improve conversion rates and sales success. – John Lyotier, CEO and Founder at TravelAI
Hybrid Switches
As AI continues to advance, hybrid switches that support both PCIe 5.0 and CXL 2.0 will become indispensable in the next generation of AI infrastructure. These hybrid solutions will be the key to overcoming the increasingly complex demands of AI workloads, offering the flexibility to handle both high-speed data transfer and efficient memory sharing. I predict that the adoption of hybrid switches will accelerate, becoming a standard in AI systems, enabling seamless scalability, and future-proofing AI infrastructure across industries. This shift will drive significant innovation, allowing AI applications to reach new heights in performance and efficiency. – Gerry Fan, CEO at XConn Technologies
Enhance Strategic Decision Making With AI Cost Estimation
In the rapidly evolving landscape of IT and digital engineering, we’re seeing a growing demand for cost management technology that allows businesses to streamline projects with AI-driven insights and analysis. The integration of generative AI enables users to leverage sophisticated predictive analytics and machine learning enhancements, so businesses can deliver projects on time, within budget, and with optimal resource utilization. By analyzing extensive historical data, AI models can make highly accurate predictions, learning from past projects to reduce the likelihood of cost overruns.
Its ability to learn and evolve is one of AI’s most compelling features within cost estimation. With each completed project, AI systems refine their algorithms, leading to more accurate estimates in future projects. This continuous improvement is crucial for industries where precision in cost estimation is paramount. Also, AI can continuously update estimates as projects progress and conditions change, such as supply chain disruptions or labor shortages. This approach ensures that estimates remain relevant and accurate throughout the project’s lifespan.
AI has the ability to automate routine and repetitive tasks in cost estimation, which frees up human experts to focus on the more complex and strategic aspects, enhancing overall efficiency. AI also excels in taking into account the unique requirements of each project, including local labor and material costs, to tailor estimates accordingly, ensuring estimates are accurate and relevant to the specifics of each project.
While AI offers a range of advantages in cost estimation, it’s crucial to approach its adoption with a balanced perspective, acknowledging its potential benefits and limitations. Integrating AI in cost estimation is not just about adopting new technology; it’s about enhancing the strategic decision-making process in project management. – Charles Orlando, Chief Marketing Officer at Galorath Incorporated
Federated Learning
Federated Learning is an innovation that is very interesting. Instead of taking all data to one main place for processing, this method lets different devices or servers work together without sharing the raw data directly. It’s a big win for privacy and security, and businesses are loving it.
Federated Learning smartly fixes privacy concerns about data. It allows businesses to use AI capabilities while protecting personal information securely. – Erik Severinghaus, Founder and CEO at Bloomfilter
Edge AI: Smarter Devices Without the Wait
So, you know when you ask your phone how to go somewhere, and it takes a long time to answer? Edge AI is making this better by putting the smart thinking directly on your device. This means quicker replies, less information stored in the cloud, and improved privacy. Shops are using it to guess what you might wish to purchase before you even realize it yourself, making shopping easier and more tailored for each person. It feel like your phone or favorite shop know you more than you know yourself! – Ghazenfer Monsoor, Founder and CEO at Technology Rivers
Organizational Use of AI Forensics and AI Visibility
The technology to secure the generative artificial intelligence (GenAI) organizations are now leveraging has only been around since the first half of 2024. While GenAI’s adoption has become widespread and organizations are seeing its potential for business value, we’re also still learning about the negative impacts of GenAI, how to avoid them along with security risks, and how to ethically harness GenAI’s power.
As we get further into 2024, organizations are going to need to take more proactive approaches to their GenAI applications and strategies to see the full benefits. One example is ensuring both AI forensics and AI visibility capabilities are available across all internal networks. This would look like auditing capabilities of all AI prompts and applications, including traceability, transparency, compliance, and risk management.
Should the worst happen, AI forensics could be a game-changer for organizations by giving them clear visibility into potential risks, tools being used, and who used them, as well as the prompts ingested by the AI models.
Organizations are finding out they cannot manage what they can’t see, making AI forensics and AI visibility a top priority for those looking to ensure even approved GenAI applications don’t pose a potential threat to security posture. –  Arti Raman, CEO and founder at Portal26
Utilizing AI to Improved User Experience
We are now in the phase where the rubber hits the road, lots of customers are realising the promises of AI changing the way they operate was more hype than Truth. The only businesses that have benefited from the huge hype of LLM/Chat GPT etc are the ones that were selling “Shovels in the gold rush” which are Microsoft azure, AWS, Databricks, etc.
But, that said we will see some revolutionary products that are based on improving user experience become even bigger and capturing more market. To take an example perplexity.ai, it is a serious challenger to google. Perplexity with its unique combination of blending search with the power of Large Language models is an awesome win for a new age company battling the behemoths. – Shubh Chatterjee, Founding Scientist at ALgoxlab LLC
 Continued Progress in Quantum AI
I would like to highlight Quantum AI – it’s bound to be a true game-changer in computing. Although still theoretical, combining the principles of quantum mechanics with AI will allow us to process information at speeds and efficiency far beyond traditional computers. This opens the possibility of AI on the proverbial steroids. This is because quantum computers use qubits, which can exist in multiple states at once, such as one and zero, resulting in exponential computing power enabling them to solve complex problems much faster. This capability will enhance AI’s ability to analyze and predict outcomes. It might be the road that takes us to the much-discussed GAI or General Artificial Intelligence – the kind we’ve only seen in sci-fi movies not actual product demos.
The progress of quantum computing has been slow due to the specific conditions required to develop and operate qubits. However, this year has been a banner year for quantum computing, with exciting breakthroughs happening just this summer. Researchers have made significant progress in overcoming a major hurdle: creating stable qubits. One approach utilizes femtosecond lasers for precise manipulation.Â
Another breakthrough involves manipulating defects in a silicon crystal lattice, using lasers to create high-quality qubits in silicon by introducing hydrogen atoms into defects. This technique allows for not only creation but also erasure of qubits – key for a more controlled and reliable system.
While it’s still challenging to get qubits to “talk” to each other, for example, these advancements represent significant progress in building a functional quantum computer.
To give you a business case of quantum AI and computing, it could revolutionize pharmaceutical R&D. Traditionally, drug discovery has been painfully slow and expensive. This is because it involves analyzing massive datasets and simulating countless molecular interactions in different scenarios. Quantum AI could accelerate this process severalfold by performing these simulations more efficiently and accurately. Basically, this would allow us to identify promising (and potentially – much more efficient) drug candidates much faster and at a lower cost, which could revolutionize how we develop new medications leading us to genetically personalized medicine etc.- Ilia Badeev, Head of Data Science at Trevolution Group
Data Architectures
In the burgeoning era of data dominance, businesses are keenly pursuing AI integration as a competitive lever, recognizing the necessity of modernizing data architectures to harness the full potential of Generative AI (GenAI) and advanced analytics. This imperative drives a demand for vendors who can deliver foundational technologies—such as robust data management, rapid data transfer, and reliable disaster recovery. As concerns over GenAI misuse persist, the need for secure, recoverable AI data becomes paramount, necessitating advanced data migration technologies and real-time cloud replication to support near-zero recovery time objectives (RTO) and recovery point objectives (RPO). Companies like Microsoft Azure and AWS are pivotal in demystifying AI and crafting tailored AI strategies for businesses, ensuring a seamless blend of AI into their strategic and technological frameworks. Over the next five years, as firms increasingly focus on monetizing AI-driven applications, those vendors that prioritize customer monetization outcomes and can efficiently move, protect, and recover large data sets will likely emerge as leaders. This shift emphasizes not only the technical integration of AI but also strategic alignment with business goals to optimize investment and maximize returns from GenAI initiatives. – Paul Scott-Murphy, Chief Technology Officer at Cirata
Open-source Decentralized AIÂ
The current trends are 100% around developing an open-source decentralized AI model. The fact that large companies can skew the input models is leading to a full-court press to build out a totally open-source product. Many Depin platforms are a natural fit to deploy this robust decentralized AI model. The future of the people depends on unadulterated input models to ensure rock-solid output models. – Daniel Keller, CEO & Co-founder at InFlux
