05 Mar implementing ai in business 3

How to Handle the Challenges of Implementing Generative AI in Your Business

Implementing legal AI drafting for in-house teams

S&P Global, the financial intelligence giant, has embraced AI interfaces across its product suite. This transformation has made sophisticated financial tools accessible to a broader range of users while maintaining the depth of functionality needed by experts. What’s happening is that many — companies and people alike — are struggling to put in place any real plans or strategies for AI adoption.

SAP: The Five AI Themes For Businesses to Watch in 2025 – Technology Magazine

SAP: The Five AI Themes For Businesses to Watch in 2025.

Posted: Tue, 21 Jan 2025 11:02:32 GMT [source]

“Companies have to create very clear and strong boundaries to limit the AI scope; they have to give consumers longer time to understand the technology,” Veresiu said. Veresiu points to a product such as Google Glass, launched in 2013, that arrived on the market before the public was ready to embrace it. The product was discontinued, but there has since been renewed interest in virtual reality and augmented reality eyewear by consumers, suggesting Google didn’t appropriately gauge the market or effectively warm up consumers. More than 60% of customers are willing to accept product recommendations from AI models. That lack of guardrails isn’t stopping people from using AI — it’s just meant that in some cases, they aren’t doing it in a cyber secure or company-approved way.

For example, autonomous vehicle companies could use the reams of data they’re collecting to identify new revenue streams related to insurance, while an insurance company could apply AI to its vast data stores to get into fleet management. Jonathan Weinberg is a freelance journalist and writer who specialises in technology and business, with a particular interest in the social and economic impact on the future of work and wider society. His passion is for telling stories that show how technology and digital improves our lives for the better, while keeping one eye on the emerging security and privacy dangers. A former national newspaper technology, gadgets and gaming editor for a decade, Jonathan has been bylined in national, consumer and trade publications across print and online, in the UK and the US. Generative AI has also given rise to an explosion in the number of new chatbots for uses such as customer service, while it can also provide actionable intelligence to make decisions simpler and faster.

How can Corporate Leadership Move Forward With Implementation of AI Solutions?

This broader perspective enables businesses to stay ahead of the curve and gain a competitive edge in the market. From streamlining operations to enhancing customer experiences, AI has become a cornerstone of success for enterprises worldwide. According to research by McKinsey

, businesses that have adopted AI technology have seen an increase in revenue of 10% on average and a reduction in costs by 20%. Implementing enterprise AI is a multifaceted process that demands a strategic approach, from defining clear goals to maintaining the technology post-deployment. Each stage is crucial in ensuring the AI implementation is successful, sustainable and delivers real value to the organization. Finally, explore available grants, funding programs, and incentives aimed at supporting AI adoption and innovation within your industry or region.

Using AI-driven demand forecasting, Walmart guarantees product availability, minimizes stockouts, and saves money on surplus inventory. If you’re struggling to implement AI in your internal processes, we recommend Netguru’s AI Primer workshop. This session helps identify and prioritize AI opportunities that can drive innovation and business transformation. Through a blend of strategic ideation and practical exploration, the workshop highlights AI’s potential to improve communication, efficiency, and customer experience. By working with data scientists and product experts, you’ll leave with actionable insights on how AI in business can be integrated into your existing processes to create real value.

However, the researchers noted challenges in the clinical deployment of AI tools, citing issues like integrating it into existing radiology workflows and establishing regulatory approval processes. Instead, companies should focus on using AI tools to fundamentally improve their operations and the experiences they deliver to customers and partners. In the insurance sector, Jerry Insurance, a site for comparing insurers, loans and repair costs, has revolutionized customer service by implementing generative AI for chat and text interactions. Customers get faster, better answers, and instead of 100% human processing, now 89% is done by large language models — enabling millions in savings and vastly better scalability. Instead of forcing customers to navigate complex phone trees or web forms, the AI interface handles everything from policy questions to claims processing through natural conversation.

Decision-makers understand the importance of accurate and complete data, with 86% saying that high-quality data is essential to the effective use of AI. But presently, they’re not bullish on their own data—only 43% describe the quality of their data as excellent, with 40% also giving an excellent rating to its accuracy and integrity. As fast as business moves in this digital age, AI helps it move even faster, said Seth Earley, author of The AI-Powered Enterprise and CEO of Earley Information Science.

Rolled out to U.S. users in May 2024, the feature has had its share of glitches, including an AI Overview recommendation to use nontoxic glue as pizza sauce to make the cheese adhere better. Even if such a scenario doesn’t happen with AI, Sheffi and others said organizations will need to adjust job responsibilities, as well as help employees learn to use AI tools and accept new ways of working. By adhering to the ten tips for overcoming the common mistakes, and leveraging the support of software agencies, your business can fully enjoy the power of AI implementation.

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Among AI Leaders, 72% report alignment between C-suite executives and information technology leadership on AI maturity goals, compared with 36% of Learners. The findings emerged from IBM’s ‘AI in Action’ report, based on research conducted by polling firm The Harris Poll across 2,000 firms in five major economies including the United States, United Kingdom, India, Japan and Germany. The research targeted organisations with annual revenue exceeding US$500m or more than 1,000 employees. Likewise, by establishing security guidelines and rules of engagement, leaders can empower their teams to explore and experiment with generative AI without exposing the company to risk.

It encompasses routine tasks such as data collection and analysis, plus more complex operations such as automation, customer service and risk management. The embrace of enterprise AI for its potential to drive growth, innovation and other business advantages is near universal. In a 2024 “AI in the Enterprise Survey” commissioned by digital transformation company UST, for example, 93% of 600 senior IT decision-makers at large companies said AI is essential to success. A late 2023 survey conducted for research firm Frost & Sullivan’s “Global State of AI, 2024” report found that 89% of organizations in multiple industry verticals believe AI and machine learning will help them achieve their business priorities.

Consider partnering with academic institutions, research organizations, or technology providers to access additional resources, expertise, and funding opportunities. Collaborative projects can leverage complementary strengths and capabilities to tackle larger, more ambitious AI initiatives that benefit multiple stakeholders. With a proactive approach with monitoring and optimization, you can ensure that your AI investments will continue to deliver maximum value and impact over time. By focusing on incremental improvements, you can minimize risks, manage costs, and demonstrate tangible value to stakeholders along the way. Speakers at MWC Las Vegas also struck a cautionary chord on AI, citing issues with AI, like hallucinations, and emphasizing the need for responsible adoption and development of the tech. The modern field of AI is often dated to 1956, when the term artificial intelligence was included in the proposal for an academic conference held at Dartmouth College that year.

Mapping out your company’s current processes might reveal the most problematic – or manual-intensive – tasks that could be automated or optimized with the help of AI. Before integrating AI into their workflows, organizations must ensure that the data is accurate, relevant, and well-organized. For instance, Intercom AI

and Zendesk AI

streamline support workflows by automating common queries, categorizing tickets based on urgency or topic, and escalating complex issues to human agents. Qualitative metrics, such as enhanced product quality and innovation, should also be considered.

Once use cases are identified and prioritized, business teams need to map out how these applications align with their company’s existing technology and human resources. Education and training can help bridge the technical skills gap internally, while corporate partners can facilitate on-the-job training. Accounting for these variables when objectively measuring how new generative AI tools impact productivity can be nigh impossible on an individual level.

How to Implement AI — Responsibly

In Europe, the EU Artificial Intelligence Act is poised to build on the already comprehensive data privacy legislation set forth in the GDPR. The EU AI Act categorizes AI models and their use cases by the risk they pose to society. It imposes significant penalties for companies that leverage “high-risk” AI systems and fail to comply with mandatory safety checks like regular self-reporting.

Ensure that the training data used to build AI models is diverse and representative of the population it is meant to serve. Include data inputs from various demographic groups to avoid underrepresentation or bias. Use tools and methods to identify and correct biases in the dataset before training the model.

Govern generative AI models from anywhere and deploy on cloud or on premises with IBM watsonx.governance. Diverse teams can bring different perspectives to the table, helping to identify and rectify biases that may be overlooked by homogeneous teams. These findings stand in stark contrast to recent data from Microsoft, which highlighted the positive impact of its AI tool, Copilot. The report indicates that 26% of surveyed companies are exploring AI opportunities, while 39% are unsure if AI is suitable for their business. We look at the findings from the study, part of ANS’s inaugural Business Blockers report, aimed to understand the technological landscape facing these companies.

Although businesses might consider shifting entire models to be AI-driven, it’s often more effective to start by deploying AI with specific use cases in mind, ensuring quicker value realization. Identifying specific applications where AI can have an impact allows for quicker implementation and more immediate results. For example, deploying AI in network operations can lead to significant gains in issue detection, remediation, and overall efficiency and performance. By focusing on targeted applications rather than an overarching cultural shift, businesses can achieve the benefits of AI faster, and with a greater impact. Enterprise artificial intelligence (AI) is the integration of advanced AI-enabled technologies and techniques within large organizations to enhance business functions.

In the near term, AI’s biggest impact on small businesses and large companies alike stems from its ability to automate and augment jobs that today are done by humans. Despite the challenges involved with scaling AI to meet business initiatives, companies do have some success stories to build on. In fact, according to a recent Prosper Insights & Analytics survey, nearly 60% of respondents reported that they were either extremely concerned or very concerned about their privacy being violated from AI using their organizations’ data. Of the projects implemented so far, for instance, nearly a quarter (23%) have underperformed, failing to meet expectations.

• Use your legal counsel to stay informed of pending legislation and how potential changes may have implications for your current and future business.
• The team should include a range of specialized roles, such as data scientists, machine learning engineers and software developers, each bringing expertise in their area.
• We’ll unpack issues such as hallucination, bias and risk, and share steps to adopt AI in an ethical, responsible and fair manner.
• Google Translate and DeepL enable businesses to bridge language gaps, improving accessibility and international reach.
• Watch a demo of the comparison of IBM models with other models across multiple use cases.

Responsible AI principles can help adopters harness the full potential of these tools, while minimizing unwanted outcomes. The Business Blockers report also explored attitudes towards cybersecurity, low code development, and barriers to business growth in the UK, providing a comprehensive view of the technological landscape facing UK mid-sized enterprises. The disparity between these success stories and the hesitancy of UK mid-sized enterprises suggests a need for greater education and support in AI implementation.

The development of generative AI technology has unlocked advanced capabilities inherent in enterprise AI. Generative AI technology is fundamentally altering many enterprise AI applications across business domains. This advancement is not just about handling data but about understanding and replicating patterns within data, leading to innovative solutions. To identify AI opportunities, research how other companies in and outside your industry are using AI. Select vendors and partners based on not only their financial stability, technical capabilities and scalability but also on their compatibility with your systems.

Execs also need to establish metrics, key performance indicators (KPIs) and other yardsticks to measure the value that AI initiatives are producing, experts said. Instead, executives — CIOs and AI directors working alongside functional leaders — need to link their AI strategy with the overall business strategy. “AI is more about the people than any other technology I’ve seen since the last widescale industrial automation in the 1970s and ’80s, so make sure you put people at the center of the AI revolution,” he added.

Veeam CIO Nate Kurtz: When data resilience meets AI strategy

Engaging the team early in the process and providing clear communication on how AI will benefit both the business and the staff can ease the transition. Arumugavelu laid out some of Verizon’s applications for AI, including network operations management, workforce planning, and customer service. He also touted a need for oversight of AI at work, pointing to the creation of Verizon’s AI Council, a leadership group overseeing the company’s approach to AI adoption, development, and deployment. IBM’s large portfolio of artificial intelligence products and services is mainly built on Granite foundation models and Watsonx technology and supports both DSML platforms and prebuilt modules. Collaborative robots, aka cobots, are working on assembly lines and in warehouses alongside humans, functioning as an extra set of hands.

Predictive maintenance has emerged as a game changer in the manufacturing industry, owing to the application of artificial intelligence. By leveraging advanced predictive analytics and machine learning algorithms, AI in the manufacturing industry enables companies to proactively monitor and predict equipment failures, minimizing downtime and optimizing maintenance schedules. In today’s digital age, artificial intelligence (AI) is no longer just a tool for large corporations; it’s also becoming a game-changer for small businesses. On this episode of The Small Business Show, we welcome Tami Cannizzaro, Chief Marketing Officer of Thryv, to explore how small business owners can harness the power of AI. With practical advice and real-world examples, Tami outlines actionable strategies to help entrepreneurs streamline operations, enhance customer engagement, and drive growth. In Beierschoder’s view, “Implementation of AI tools pays off for companies of all sizes.” The decision on whether to invest also depends on the company’s strategic focus.

One legal area that has been much discussed is the issue of bias and discrimination, especially in the context of tools used by corporate HR departments. Many of the new laws being proposed, including one that just passed the Colorado legislature, have specific new requirements to deter potential bias and discrimination. AI can personalize the customer experience and aid marketers by analyzing large data sets to uncover customer behavior patterns. AI models can also assist with forecasting sales trends and market demand, enabling more effective resources and personalized customer interactions. An effective AI strategy must also be agnostic, leveraging all available technologies without becoming locked into any single one. This vendor flexibility allows organizations to adapt and integrate new advancements as they emerge, a necessity given AI’s continuous evolution.

This includes regular audits to guarantee data quality and security throughout the AI lifecycle. The importance of data privacy, data quality and security should be emphasizedthroughout the AI lifecycle. The study covered five major markets – the United States, Japan, Germany, the United Kingdom and India – focusing on large enterprises with substantial revenue or employee numbers. This approach aimed to capture insights from organisations with the resources to implement significant AI initiatives.

Alignment with business objectives

Regardless, there’s still a significant skills gap in the workforce regarding the understanding and management of AI technologies, according to a 2023 study by GlobalData. Beyond the executive level, AI implementation in healthcare affects patients and healthcare professionals in terms of human acceptance and trust issues. A 2023 study found that clinical staff may struggle to accept AI due to the need to learn new skills and take on more complex tasks. For example, the appropriate use of RAG architecture can significantly reduce runtime expenses and increase the performance and quality of the output. Additionally, companies often don’t fully grasp the complexity of working with AI, seeing only the tip of the iceberg and missing the hidden costs. To do that, execs need to not only have a clear business strategy, they also need to understand how AI is changing their specific industry and how AI will present new opportunities and challenges to their sector and their individual business.

Vendor and partner selection for AI implementation is a crucial step for organizations. When selecting vendors, companies should explore those with relevant industry expertise and a proven track record in similar AI projects. Technology Magazine focuses on technology news, key technology interviews, technology videos, the ‘Technology Podcast’ series along with an ever-expanding range of focused technology white papers and webinars. SAP’s Foundation Model, designed for processing structured business data, also represents a move away from general-purpose LMs towards systems optimised for specific business applications.

This enables warehouses to optimize inventory levels, reducing carrying costs while ensuring product availability. AI for manufacturing is also revolutionizing warehouse management, enhancing efficiency with advanced automation and intelligent data insights. The advent of AI-powered manufacturing solutions and machine learning in manufacturing has transformed how warehouses operate, improving efficiency, accuracy, and cost savings.

Everything is unlocked at a new level, from manufacturing ops to supply chain management to predictive maintenance, paving the way for smarter decision-making. That’s why businesses and manufacturing owners look forward to embedding AI into their existing workflows. One impactful application of AI and ML in manufacturing is robotic process automation (RPA) for paperwork automation. Traditionally, manufacturing operations involve a lot of paperwork, such as purchase orders, invoices, and quality control reports. These manual processes are time-consuming and error-prone and can result in delays and inefficiencies. By leveraging the power of AI in manufacturing, companies are revolutionizing their approach to quality control, ensuring higher accuracy and consistency.

They are now designed to learn on the job, adapting to new tasks and improving over time. The integration of AI in the manufacturing market has brought significant advancements to warehouse management. From inventory optimization to streamlined order fulfillment, AI-powered manufacturing and ML in manufacturing solutions are transforming warehouses, making them more efficient and cost-effective. As per a study by PwC, Reinforcement Learning (a subset of AI) is capable of optimizing electronic device production by dynamically adjusting machine parameters in smart manufacturing. Through continuous learning and adaptation, the system maximizes output, minimizes defects, and enhances resource utilization, leading to heightened profitability and a competitive edge. Neglecting this step in AI integration could make you trust untrue insights, and lead to poor decision-making and potentially significant business setbacks.

Study: Using AI Chatbots Can Expose Sensitive Business Data – Tech.co

Study: Using AI Chatbots Can Expose Sensitive Business Data.

Posted: Tue, 21 Jan 2025 15:53:38 GMT [source]

By offering personalized suggestions to mothers based on their child’s gender and age, Edamama secured an impressive $20 million in funding. According to a Deloitte survey, manufacturing stands out as the foremost industry in terms of data generation. This indicates a significant volume of data being generated within the manufacturing sector, showcasing the industry’s substantial impact on the data landscape. Manufacturers must adopt AI to analyze this humongous amount of data generated in the sector. At Netguru we specialize in designing, building, shipping and scaling beautiful, usable products with blazing-fast efficiency. For example, a logistics company could discover that the order processing stage is taking too long, as a human employee needs to manually enter data from one system to another.