AlphaSense is a market intelligence platform that uses generative artificial intelligence (genAI) and natural language processing to help organizations find and analyze insights from sources such as financial reports, news, earnings calls, and proprietary documents.
The platform aims to help business professionals access relevant insights and make data-driven decisions.
Sarah Hoffman, Director of AI Research at AlphaSense, is an IT strategist and futurist. Hoffman, former vice president of AI and machine learning research at Fidelity Investments, discusses how AI will change the future of work and how companies will grapple with rapidly evolving technology deployments in the coming years. He told Computerworld about whether he should.
In particular, she believes that the introduction of genAI tools into businesses will allow employees to move away from repetitive tasks and focus on more creative endeavors by learning how to use new tools and even learning how to collaborate. He said it would be. What will emerge is a “symbiotic” relationship with increasingly “aggressive” technology, requiring employees to constantly learn and adapt new skills.
How will AI shape the future of work, both in terms of innovation and new workforce dynamics? “AI can manage repetitive tasks and even difficult tasks that are specialized in nature. But humans can focus on innovative, strategic initiatives that drive revenue growth and improve overall performance. AI is also much faster and available 24/7. , and can scale to accommodate increasing workloads.
“As AI automates more processes, the role of workers will change. Jobs may focus less on repetitive tasks, but employees will have more responsibility for overseeing AI systems, handling exceptions, and more.” New roles will emerge that require a focus on processing, performing creative or strategic functions that AI cannot easily replicate.
“The future workforce will work more closely with AI tools. For example, marketers are already using AI to create more personalized content, and programmers are already using AI to create more personalized content. Leveraging Code Copilot: Employees must adapt to working with AI and find ways to make the most of human strengths and AI capabilities.
“AI can also become a brainstorming partner for experts, increasing creativity by generating new ideas and providing insights from vast datasets. There will be an increasing focus on AI. It acts as a tool that enhances rather than replaces human capabilities, leading to a more symbiotic relationship between workers and technology. This transformation involves continuous upskilling and the organization and execution of work. We need to rethink our methods.
Why does Gen Z’s adoption of AI signal a broader trend in business technology? “Gen Z grew up in a highly digital environment, so they are naturally comfortable with technologies such as AI. The rapid adoption of AI tools highlights a shift to a technology-first mindset. This generation is highly skilled in the workforce and is therefore AI-savvy, facilitating its integration into business processes. , companies are using AI Deploy and adapt led solutions faster.
“Gen Z’s use of AI reflects a broader understanding that AI complements, rather than replaces, human skills. We need to recognize the importance of training our employees to work with AI, so that it becomes a valuable tool to enhance their thinking.”
sarah hoffman
alpha sense
What is the role of AI in business teams? And how can companies best leverage AI to improve human skills and knowledge? It is not meant to be a complete replacement, but rather as a tool that augments human capabilities. Professionals use AI to streamline daily tasks such as analyzing data and identifying trends, making them more strategic and creative. You can spend more time working. Plus, AI can accelerate learning and innovation by integrating complex data, identifying new perspectives, and delivering personalized insights.
“To make the most of AI to enhance human skills and knowledge, companies must:
Clearly define the role of AI, establish specific tasks for AI such as data processing and generation of insights, and use AI as a tool to support human judgment and decision-making. Regularly check the accuracy and reliability of AI output to ensure that AI recommendations match human expertise. Effectively train your team to know when to trust AI recommendations and, importantly, when to rely on your own judgment and expertise. Enable effective collaboration between AI tools and humans. AI should complement human intelligence and enable teams to work more efficiently, creatively, and strategically. ”
What should companies prioritize to leverage AI for long-term success? “Before companies take advantage of this powerful technology and the business opportunities that come with it, they need to consider common pitfalls Companies can build their own systems that work best for their customers or leverage third-party partnerships to leverage AI. It can reduce the initial cost of building a system from scratch. This is a critical decision that will impact its future success and longevity. And the answer doesn’t have to be just build or buy. Depending on the use case involved, a hybrid solution may also make sense.
“Companies need to focus on long-term strategy, quality data, clear goals, and careful integration into existing systems.Start small and gradually scale up to implement, manage, and manage AI solutions. Build a dedicated team to optimize. It’s also important to invest in employee training to ensure they are prepared to use AI systems effectively.
“Business leaders also need to understand how data is organized and distributed across the business. Reorganizing existing data silos and identifying priority datasets takes time. To create or effectively implement a well-trained model, companies must ensure that their data is properly organized and prioritized.
“Coordination between teams is critical to a successful AI program, including developers, data analysts and scientists, AI architects and researchers, and others who determine overall business goals and objectives. These teams must work closely together to ensure consistency across development, product, marketing, and more.
“Another important aspect for companies to consider is the end user. For AI to be successful in the long term, companies must understand the needs and expectations of the people who will interact with or benefit from the technology. This includes gathering feedback from end users throughout the development and implementation process to ensure that the solution being built provides real value. .
“By focusing on these priorities, companies will prepare their workforces, ensure their AI programs are highly effective and ethically sound, and position themselves for long-term success.” You can.”
What are the biggest advances in AI this year? “In 2025, generative AI will move from experimental stages to mainstream, product-ready applications across industries. Creation,knowledge management is expected to lead this evolution.
“As more production-ready solutions are introduced, companies are looking beyond time savings to include metrics such as customer satisfaction, revenue growth, enhanced decision-making, and competitive advantage. These advances will refine how we quantify the impact of AI. These advances will help executives make more informed investment decisions and accelerate the adoption of generative AI across industries. Masu.
“Generative AI systems will also become significantly more proactive, evolving beyond a passive ‘question and answer’ model to intelligently anticipate user needs. By leveraging a deep understanding of a user’s habits, preferences, and context, these systems can predict and provide relevant information, assistance, or action at the right time. By acting as intelligent agents, they may begin to handle simple tasks autonomously with minimal input, further enhancing their utility and integration into daily workflows. ”
How do you think generative AI will move from pilot to production next year? “The leap from pilot project to full-scale deployment is the next important step for generative AI in 2025. In 2024. While businesses have experimented with AI for efficiencies, such as automating customer service queries and creating personalized content, these applications are maturing and providing tangible business results for businesses to use their data. Pipelines and AI As we refine our infrastructure, these tools may become integral to daily operations rather than discrete experiments.
“Beyond efficiency, there is growing interest in leveraging AI for strategic innovation. For example, companies are using generative AI to prototype new products or model market scenarios. These strategic applications have the potential to reshape industries by driving innovation, increasing competitive advantage, and driving revenue growth. there is.”
Over the past year, many organizations have struggled to clean up their data for use in AI. Why do you think it’s still necessary? “Even as models become more sophisticated, data cleaning remains essential to ensuring AI reliability. Generative AI systems must You rely on high-quality, consistent data. Inadequate data preparation can lead to biased output, poor performance, and even legal risks in sensitive applications. By standardizing, deduplicating, and enhancing, organizations can It ensures that the system is well-equipped to handle real-world complexities.”
How can companies ensure that the answers they get from genAI are accurate? “To ensure the accuracy of generative AI, companies must employ rigorous testing and validation methods. , must be evaluated against real-world datasets and specific benchmarks to confirm its reliability.
“Many companies are turning to search augmented generation (RAG), which uses domain-specific, authoritative, quotable data, to reduce the risk of misinformation. This is particularly important in applications such as medical and financial decision-making, which can result in high-stakes performance. Similarly, human oversight is essential in these high-stakes roles.
Companies deploying AI are using multiple models, but how can they create pipelines between these models and the business for strategic purposes? Rather than relying on AI, we take a multi-model approach, often deploying three or more AI models and routing to different models based on the use case. , maintain accuracy, and change business Continuous monitoring is required to be able to adapt to your needs.
Do you think small language models will predominate in 2025, or do you think a more typical large language model will predominate? And why? The choice between a large-scale language model and a large-scale language model ultimately depends on the specific use case, as SLM has use-case-specific constraints regarding security, cost, and latency. Very valuable for certain limited tasks. is faster, cheaper to operate, and highly customizable to suit your domain workflow. For example, AlphaSense uses SLM for financial reporting summaries. Another benefit of SLM is that it runs on-device. This is important for many mobile applications that utilize sensitive personal data.
“LLM, on the other hand, will have an advantage in general-purpose and complex applications that require high-level reasoning, adaptability, and creativity. Its extensive knowledge and versatility make it ideal for advanced research, multimodal content, and more. The hybrid approach combines the efficiency of SLM with the versatility of LLM, making it essential for AI in 2025. landscape and enable enterprises to optimize performance, cost, and scalability.”
