Developed by researchers at MIT, Tsinghua University, and Canadian startup MyShell, OpenVoice uses just seconds of audio to clone a voice and allows granular control over tone, emotion, accent, rhythm, and more.  

MyShell unveiled OpenVoice in a post this week, linking to a pre-reviewed research paper explaining the technology as well as demo sites on MyShell and HuggingFace where users can try it.

Dual AI models enable instant voice cloning  

OpenVoice comprises two AI models working together for text-to-speech conversion and voice tone cloning.

The first model handles language style, accents, emotion, and other speech patterns. It was trained on 30,000 audio samples with varying emotions from English, Chinese, and Japanese speakers. The second “tone converter” model learned from over 300,000 samples encompassing 20,000 voices.

By combining the universal speech model with a user-provided voice sample, OpenVoice can clone voices with very little data. This helps it generate cloned speech significantly faster than alternatives like Meta’s Voicebox.

Canadian startup 

OpenVoice comes from Calgary-based startup MyShell, founded in 2023. With $5.6 million in early funding and over 400,000 users already, MyShell bills itself as a decentralised platform for creating and discovering AI apps.  

In addition to pioneering instant voice cloning, MyShell offers original text-based chatbot personalities, meme generators, user-created text RPGs, and more. Some content is locked behind a subscription fee. The company also charges bot creators to promote their bots on its platform.

By open-sourcing its voice cloning capabilities through HuggingFace while monetising its broader app ecosystem, MyShell stands to increase users across both while advancing an open model of AI development.

(Photo by Claus Grünstäudl on Unsplash)

See also: AI & Big Data Expo: Maximising value from real-time data streams

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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In an interview at the recent AI & Big Data Expo, Streambased founder and CEO Tom Scott outlined the company’s approach to enabling advanced analytics on streaming data. At the foundation of Streambased’s offering is Apache Kafka, an open-source event streaming platform that has been widely adopted by Fortune 500 companies.

“Where [Kafka] falls down is in large-scale analytics,” explained Scott. While Kafka reliably transports high-volume data streams between applications and microservices, conducting complex analytical workloads directly on streaming data has historically been challenging. 

Streambased adds a proprietary acceleration technology layer on top of Kafka that makes the platform suitable for the type of demanding analytics use cases data scientists and other analysts want to perform.

Because these continuously flowing event streams power critical operational systems and core business functions, data quality must already meet high standards in terms of accuracy, timeliness, and structure. By leveraging these existing Kafka data pipelines, Streambased ensures its analytical capabilities have access to up-to-date, clean and well-organised data.

Use cases that showcase the power of Streambased’s approach include fraud detection in financial services. If an anomalous transaction occurs, analysts can quickly query similar or related transactions to investigate – which would be difficult and inefficient to accomplish with a pure streaming architecture. Streambased’s optimization for analytical interactivity enables users to rapidly gather contextual insights without disrupting their workflow.

The convergence of operational and analytical data platforms represents an impactful trend that Streambased calls the “streaming data lake” movement

“I think we are at the period of the streaming data lake movement. And by a streaming data lake, I mean a complete convergence between data systems that we use for analytical purposes and data systems that we use for operational purposes,” explains Scott.

Recent enhancements like infinite data retention in Kafka and native streaming analytics services lay the foundation for this new paradigm. For now, Streambased remains focused on empowering business analysts through frictionless self-service access to granular real-time data, without requiring changes to existing tools and processes.

You can watch our full interview with Tom Scott below:

(Photo by Robert Zunikoff on Unsplash)

See also: AI & Big Data Expo: Unlocking the potential of AI on edge devices

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI & Big Data Expo: Maximising value from real-time data streams appeared first on AI News.

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular.

At Canon, Zheng’s team focuses on ethically integrating AI into business by first mapping current and potential AI applications across areas like marketing and e-commerce. They then analyse and assess risks to ensure compliance with regulations.

Canon is actively mapping out AI applications and assessing risks, as Grace explained, “to align with regulations such as the EU legislations.”

As founder of Kosh Duo, Zheng also provides coaching to help businesses scale up through the use of AI marketing and data-driven approaches. She coaches professionals on achieving greater recognition and rewards by leveraging AI tools as well.

A key challenge she encounters is misunderstandings around what AI truly means – many conflate it solely with chatbots like ChatGPT rather than appreciating the full breadth of machine learning, neural networks, natural language processing, and more that enable today’s AI.

“There’s a lot of misconceptions, definitely. One of the biggest fears, as I touched on, is the very generic understanding that GPT equals AI,” says Zheng. “[Kosh Duo] provides coaching services to businesses to scale to the next level using AI marketing and data-driven approaches.”

When asked about trends to watch, Zheng emphasised the need for continual learning given how rapidly the field evolves. She expects that 2024 will be an “awakening year” where businesses truly grasp AI’s potential and individuals appreciate the need to evaluate their current skillsets.

The interview highlighted the transformative but often misunderstood power of AI in business and the importance of developing specialised skills to properly harness it. Zheng stressed that with the right ethical foundations and coaching, AI and machine learning can become positive forces to drive growth rather than something to fear.

Watch our full interview with Grace Zheng below:

(Photo by Benjamin Davies on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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The adoption of AI is rapidly gaining momentum, with 35% of businesses currently utilizing AI-powered solutions. This trend is mirrored in the wider world, where 77% of devices in use feature some form of AI. Recognizing the competitive advantage that AI can bring, 9 out of 10 organizations support the adoption of AI. The future looks bright for AI, with projections estimating that AI will contribute a staggering $15.7 trillion to the global economy by 2030.

How can we use AI in marketing?

Incorporating AI into marketing involves leveraging the transformative power of technology to enhance various aspects of the marketing landscape. AI redefines how businesses connect with their audiences, from predictive analytics and personalized customer experience to automated decision-making and tailored messaging. This section delves into the diverse ways AI can seamlessly integrate into marketing strategies for achieving optimal results.

Best 10 AI marketing campaign examples in 2023

As artificial intelligence (AI) revolutionizes the marketing landscape, businesses are embracing its transformative power to drive effective marketing strategies and enhance digital marketing efforts. Dive into this exclusive showcase of 10 groundbreaking AI marketing campaigns that highlight the immense potential of AI to connect with audiences, boost engagement, and achieve business objectives. These case studies demonstrate how artificial intelligence can be seamlessly integrated into various marketing channels, propelling businesses toward success.

1. Heinz: AI-Generated Ketchup

Heinz, a renowned condiment brand, embraced the generative AI craze by asking a simple question: “What does AI think ketchup looks like?” They turned this question into a viral marketing campaign, using the AI image generation tool DALLE-2 to create fun and engaging images of ketchup in various settings. To further engage their audience, Heinz encouraged social sharing, allowing users to create their own AI-generated social media posts with ketchup images.

2. Ben & Jerry’s: Breakfast Flavors Discovery

Ben & Jerry’s, a leading ice cream brand, partnered with Unilever’s AI-powered trend analysis to uncover a gap in the market for breakfast-inspired ice cream flavors. Based on this data-driven insight, Ben & Jerry’s launched their “ice cream for breakfast” campaign, introducing three new flavors that resonated with consumers.

3. Shopify: AI-Powered Shopping Assistant

Shopify, a leading e-commerce platform, introduced an AI-powered shopping assistant chatbot to enhance the user experience. This chatbot can guide users through the vast selection of stores on Shopify, offering personalized recommendations and assistance.

4. Coca-Cola: Create Real Magic

Coca-Cola, a beverage giant, launched a creative AI-powered contest called “Create Real Magic.” This contest invited digital artists to create art pieces using an AI-powered web app, with the top entries featured on Coca-Cola’s digital billboards in Time Square. This collaborative campaign showcased the power of AI to foster creativity and engagement.

5. Nutella: Unica Limited Edition Labels

Nutella, a popular hazelnut spread, sought to stand out with unique and personalized packaging. Working with Ogilvy Italia, Nutella leveraged AI to create 7 million one-of-a-kind labels, each reflecting the diverse and expressive Italian culture. This campaign demonstrated the power of using AI in marketing to enhance product differentiation and brand appeal.

6. Volkswagen: AI-Powered Ad Buying

Volkswagen, a renowned automotive manufacturer, adopted a groundbreaking approach to optimize its ad-buying decisions. By leveraging AI, Volkswagen implemented an automated system that analyzes real-time data to identify the most effective ad placements and target audiences. This data-driven approach, 

artificial intelligence in marketing enabled Volkswagen to reduce its ad spend by 20% while increasing sales.

7. Netflix: Personalized Recommendations

Netflix, a leading streaming service, revolutionized the way users discover content by employing AI-powered personalization algorithms. These algorithms analyze user behavior and preferences to recommend relevant movies, TV shows, and даже artwork that aligns with individual tastes.

7. Mastercard: Crayon AI for Competitive Intelligence

Mastercard, a global payments company, faced the challenge of staying ahead of the curve in an ever-evolving competitive landscape. To gain a deeper understanding of competitor strategies and anticipate market trends, Mastercard integrated AI into their business operations. By employing Crayon, an AI-powered competitive intelligence platform, Mastercard gained a clear picture of its competitors’ activities and market movements. This insights-driven approach empowered Mastercard to make informed business decisions and protect its market position.

8. Nike: Immersive AI-Generated Match

Nike, a sportswear brand, collaborated with Serena Williams to create an AI-generated match that juxtaposed her younger self from 1999 against her modern self from 2017. This award-winning campaign commemorated Nike’s 50th anniversary and showcased the emotional connection that AI can evoke.

9. BMW: Projecting AI Art onto Cars 

A luxury car manufacturer, BMW partnered with an advertising agency to project AI-generated art onto their 8 Series Gran Coupé. This innovative campaign aimed to connect with BMW’s target audience on an emotional level and highlight the brand’s creativity and innovation.

10. Calm App: Personalized Content Recommendations

Calm, a popular meditation app, leveraged Amazon Personalize, an AI-powered product recommendation engine, to provide personalized content recommendations to users. This dynamic approach ensures that users are presented with relevant content that aligns with their preferences, enhancing the overall user experience.

These ten examples demonstrate the transformative power of AI in marketing, highlighting its ability to drive innovation, personalization, and data-driven decision-making. As AI continues to evolve, its impact on marketing is expected to grow even more profound, paving the way for even more creative and effective marketing campaigns.

In conclusion

From Heinz’s creative use of AI-generated images to Ben & Jerry’s data-driven flavor launch, these examples showcase how AI can be applied to shape personalized customer experiences, optimize ad targeting, and personalize content recommendations. By harnessing the power of AI, business can elevate their AI marketing strategy to unprecedented heights, achieving deeper customer engagement, enhanced conversion rates, and a competitive edge in the ever-evolving digital marketplace.

To further explore the role of AI in marketing and witness groundbreaking campaigns, consider reaching out to a leading AI marketing agency. With their expertise and guidance, you can unlock the full potential of AI and transform your marketing campaigns into resounding successes.

To conclude, the era of AI in marketing presents a world of unprecedented opportunities to transform data into gold. By embracing AI-based marketing campaigns, businesses can not only stay ahead of the competition but also revolutionize how they connect with their audience, paving the way for a future where data truly becomes a valuable asset in the marketing realm.

(Editor’s note: This article is sponsored by Digital Agency Network)

The post Turning data into gold: 10 exceptional AI marketing campaign examples appeared first on AI News.

During the discussion, Grande provided insightful perspectives on the current challenges, how Edge Impulse is helping address these struggles, and the tremendous promise of on-device AI.

Key hurdles with edge AI adoption

Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

“A lot of the companies building edge devices are not very familiar with machine learning,” says Grande. “Bringing those two worlds together is the third challenge, really, around having teams communicate with each other and being able to share knowledge and work towards the same goals.”

Strategies for lean and efficient models

When asked how to optimise for edge environments, Grande emphasised first minimising required sensor data.

“We are seeing a lot of companies struggle with the dataset. What data is enough, what data should they collect, what data from which sensors should they collect the data from. And that’s a big struggle,” explains Grande.

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Always balance sensor and hardware constraints against functionality, connectivity needs, and software requirements.

Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

Transformative potential of on-device intelligence

Grande highlighted innovative products already leveraging edge intelligence to provide personalised health insights without reliance on the cloud, such as sleep tracking with Oura Ring.

“It’s sold over a billion pieces, and it’s something that everybody can experience and everybody can get a sense of really the power of edge AI,” explains Grande.

Other exciting opportunities exist around preventative industrial maintenance via anomaly detection on production lines.

Ultimately, Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life. Rather than just raw data, edge devices can interpret sensor inputs to provide actionable suggestions and responsive experiences not previously possible—heralding more useful technology and improved quality of life.

Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI.

“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.

Watch our full interview with Alessandro Grande below:

(Photo by Niranjan _ Photographs on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI & Big Data Expo: Unlocking the potential of AI on edge devices appeared first on AI News.

Available for Vertex AI customers on the allowlist, Imagen 2 enables users to craft and deploy photorealistic images using intuitive tooling and fully-managed infrastructure. 

Developed with Google DeepMind technology, Imagen 2 offers improved image quality and a range of functionalities tailored for specific use cases.

Key features of Imagen 2 include:

• Diverse image generation: Imagen 2 excels in creating high-resolution images from natural language prompts that cater to various user requirements.

• Text rendering in multiple languages: Overcoming common challenges, Imagen 2 supports accurate text rendering in multiple languages.

• Logo generation: Businesses can leverage Imagen 2 to create a variety of creative and realistic logos—with the option to overlay them on products, clothing, business cards, and more.

• Captions and question-answering: Imagen 2’s advanced image understanding capabilities facilitate the creation of descriptive captions and provide detailed answers to questions about image elements.

• Multi-language support: Imagen 2 introduces support for six additional languages in preview, with plans for more in early 2024. This includes the ability to translate between prompt and output.

• Safety measures: Imagen 2 incorporates built-in safety precautions, aligning with Google’s Responsible AI principles. It features safety filters and integrates with a digital watermarking service to ensure responsible use.

Enterprise-ready capabilities

Imagen 2 on Vertex AI is designed to meet enterprise standards, offering reliability and governance akin to its predecessor. With new features such as high-quality image rendering, improved text rendering, logo generation, and safety measures, Imagen 2 aims to provide organisations with a comprehensive tool for creative image generation.

Leading companies like Snap, Shutterstock, and Canva have already embraced Imagen for creative purposes.

Chris Loy, Director of AI Services at Shutterstock, commented: “We exist to empower the world to tell their stories by bridging the gap between idea and execution.

“Variety is critical for the creative process, which is why we continue to integrate the latest and greatest technology into our image generator and editing features—as long as it is built on responsibly sourced data,”

Danny Wu, Head of AI at Canva, added: “We’re continuing to use generative AI to innovate the design process and augment imagination.

“With Imagen, our 170M+ monthly users can benefit from the image quality improvements to uplevel their content creation at scale.”

As Imagen 2 makes waves in the creative industry, organisations are encouraged to explore its potential. Google Cloud anticipates users will harness the new features to elevate their creative endeavours and build on the success achieved with Imagen.

(Photo by G on Unsplash)

See also: Microsoft unveils 2.7B parameter language model Phi-2

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Google Cloud announces Imagen 2 text-to-image generator appeared first on AI News.

The report underscores the need for a composite AI approach. This involves combining various AI types – such as generative, predictive, and causal – along with diverse data sources like observability, security, and business events. This holistic strategy aims to provide precision, context, and meaning to AI outputs, ensuring reliable results.

Key findings:

• 83% of tech leaders emphasise the mandatory role of AI in navigating the dynamic nature of cloud environments.
• 82% anticipate AI’s critical role in security threat detection, investigation, and response.
• 88% foresee AI extending access to data analytics for non-technical employees through natural language queries.
• 88% believe AI will enhance cloud cost efficiencies through support for Financial Operations (FinOps) practices.

“AI has become central to how organisations drive efficiency, improve productivity, and accelerate innovation,” said Bernd Greifeneder, Chief Technology Officer at Dynatrace.

“The release of ChatGPT late last year triggered a significant generative AI hype cycle. Business, development, operations, and security leaders have set high expectations for generative AIs to help them deliver new services with less effort and at record speeds.”

While organisations express optimism about AI’s transformative potential, concerns linger:

• 93% of tech leaders worry about potential non-approved uses of AI as employees become more accustomed to tools like ChatGPT.
• 95% express concerns about using generative AI for code generation, fearing leakage and improper use of intellectual property.
• 98% are apprehensive about unintentional bias, errors, and misinformation in generative AI.

“Especially for use cases that involve automation and depend on data context, taking a composite approach to AI is critical. For instance, automating software services, resolving security vulnerabilities, predicting maintenance needs, and analysing business data all need a composite AI approach,” added Greifeneder.

“This approach should deliver the precision of causal AI, which determines the underlying causes and effects of systems’ behaviours, and predictive AI, which forecasts future events based on historical data.”

As organisations forge ahead with AI adoption, balancing enthusiasm with a mindful approach to challenges becomes paramount. The survey underscores the transformative potential of AI, but its effective integration requires careful consideration and a diversified AI strategy.

“Predictive AI and causal AI not only provide essential context for responses produced by generative AI but can also prompt generative AI to ensure precise, non-probabilistic answers are embedded into its response,” says Greifeneder.

“If organisations get their strategy right, combining these different types of AI with high-quality observability, security, and business events data can significantly boost the productivity of their development, operations, and security teams and deliver lasting business value.”

A full copy of the report can be found here (registration required)

(Photo by Matt Sclarandis on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Craven — with an extensive background in engineering and leadership roles at McKinsey & Company, HSBC, Nokia, among others — shared his experiences as a consultant helping C-level executives navigate the complex landscape of AI adoption. The core message revolved around understanding AI beyond the hype to make informed decisions that align with organisational goals.

Breaking down the AI hype

Craven introduced a systematic approach to demystifying AI, emphasising the need to break down the overarching concept into smaller, manageable components. He outlined key attributes of neural networks, embeddings, and transformers, focusing on large language models as a shared foundation.

• Neural networks — described as probabilistic and adaptable — form the backbone of AI, mimicking human learning processes.
• Embeddings allow computers to navigate between levels of abstraction, somewhat akin to human cognition.
• Transformers — the “attention” mechanism — are the linchpin of the AI revolution, allowing machines to understand context and meaning.

LLMs as search and research engines

Craven assesses if LLMs alone make good search engines. They understand search intent exceptionally well but don’t have access to vast data, give accurate results, or reference sources—all of which are key search requirements.

However, Craven highlighted that large language models (LLMs) are powerful summarising engines for research. He emphasised their ability to summarise data, translate between languages, and serve as research assistants:

Craven went on to caution against relying solely on LLMs for complex tasks—showcasing a study where consultants using language models underperformed in nuanced analysis.

De-hyping AI: Setting realistic expectations

The presentation concluded with practical use cases for organisations, such as documentation tools, high-level decision-making, code review tools, and multimodal decision-makers. Craven advised a thoughtful evaluation of when LLMs are useful, ensuring they align with organisational values and principles.

However, Craven warns against inflated claims about AI’s performance—citing examples where language models enhanced certain tasks but fell short in others. He urged the audience to consider the context and nuances when evaluating AI’s impact, avoiding unwarranted expectations.

Craven offered actionable insights for implementation, urging organisations to capture data for future use, create test cases for specific use cases, and apply a systematic framework to develop a strategy. The emphasis remained on seeing through the hype, saving millions by strategically incorporating AI into existing workflows.

In a world inundated with AI promises, Adam Craven’s pragmatic approach provides a roadmap for organisations to leverage the power of AI while avoiding common pitfalls.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Demis Hassabis, CEO and Co-Founder of Google DeepMind, introduced Gemini as a multimodal model that is capable of seamlessly understanding and combining various types of information, including text, code, audio, image, and video.

Gemini comes in three optimised versions: Ultra, Pro, and Nano. The Ultra model boasts state-of-the-art performance, surpassing human experts in language understanding and demonstrating unprecedented capabilities in tasks ranging from coding to multimodal benchmarks.

What sets Gemini apart is its native multimodality, eliminating the need for stitching together separate components for different modalities. This groundbreaking approach, fine-tuned through large-scale collaborative efforts across Google teams, positions Gemini as a flexible and efficient model capable of running on data centres to mobile devices.

One of Gemini’s standout features is its sophisticated multimodal reasoning, enabling it to extract insights from vast datasets with remarkable precision. The model’s prowess extends to understanding and generating high-quality code in popular programming languages.

However, as Google ventures into this new era of AI, responsibility and safety remain paramount. Gemini undergoes rigorous safety evaluations, including assessments for bias and toxicity. Google is actively collaborating with external experts to address potential blind spots and ensure the model’s ethical deployment.

Gemini 1.0 is now rolling out across various Google products – including the Bard chatbot – with plans for integration into Search, Ads, Chrome, and Duet AI. However, the Bard upgrade will not be released in Europe pending clearance from regulators.

Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI. Android developers will also be able to build with Gemini Nano via AICore, a new system capability available in Android 14.

(Image Credit: Google)

See also: AI & Big Data Expo: AI’s impact on decision-making in marketing

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Google’s next-gen AI model Gemini outperforms GPT-4 appeared first on AI News.

In the realm of Knowledge Management, we are now witnessing the emergence of the last mile problem. About a year ago, ChatGPT captivated millions of users worldwide, prompting businesses to explore the potential impact of this groundbreaking technology. Knowledge Management quickly became a focal point of interest, with vendors like OpenAI and Microsoft developing APIs and tools for developers to create their own applications. Since then, Fortune 1000 IT departments have been actively experimenting, testing, piloting, and implementing solutions to address productivity inefficiencies.

The possibilities presented by available APIs and services are truly revolutionary. We are witnessing remarkable examples of knowledge repositories being accessed through generative AI, enabling functions that were once considered nearly science fiction.

However, Knowledge Management encompasses far more than an internal dataset complemented by generative APIs that extract knowledge. It encompasses information derived from documents, dashboards, reports, and even expertise found within a company. Additionally, knowledge may be obtained from third-party systems and subscriptions. Enterprises often operate multiple systems and technologies from various providers such as Microsoft, SAP, Box, Google, ServiceNow, Salesforce, and Workday. Each system has its own access controls and data types, and none were designed to be universally searchable.

Consequently, enterprises face significant last mile challenges in Knowledge Management. Different business units or departments within the same company may have distinct needs. Use cases, data sets, and requirements can greatly vary. For example, sales may require document storage and CRM, while operations may need document storage, ticketing systems, and project tools. HR, on the other hand, may rely on document storage, learning management systems, and ERP solutions. Furthermore, groups like marketing or research & insights may require access to external tools and subscriptions, in addition to internal data. A one-size-fits-all approach to Knowledge Management cannot adequately address the diverse range of tools, data, and departmental needs found in the typical enterprise.

“Enterprises often operate multiple technologies and systems from various providers. Each system has its own access controls and data types – and none were designed to be universally searchable”

Support is another critical aspect of the last mile problem. Who is responsible for understanding the unique business requirements and data dependencies of each unit? Who provides user training, onboarding, and ongoing support? Who develops custom glossaries, maps out ontologies, and fine-tunes the AI? In this rapidly evolving technological landscape, how can features and functions be tailored to meet the specific needs of each business unit?

While dedicating internal IT resources to solve these challenges is a viable solution, it may not be the most efficient approach if Knowledge Management is not the primary focus of the business. In such cases, alternative strategies can better support the last mile for different business users, departments, or teams.

To this end, Enterprise Knowledge Management vendors specialize exclusively in KM. Their platforms and capabilities are built on years of experience working with customers, understanding their real-world needs. They offer a wide range of data connectors that seamlessly integrate with various enterprise technologies, constantly evolving to keep pace with the latest advancements. These vendors have dedicated customer success teams to assist with user onboarding, training, and support. They optimize their AI models to suit each customer’s requirements and provide ongoing assistance.

Ideally, business units and departments should either possess internal IT resources with the capacity to address these challenges or collaborate with approved vendors offering vertical market solutions.  Solutions should seamlessly integrate with existing standards, policies, access controls, and technologies within the enterprise, ensuring comprehensive support for Knowledge Management.

As we move forward into the future of Knowledge Management, the emphasis should be on collaborative efforts between internal IT resources and external vendors. By leveraging the strengths of both, enterprises can navigate the complexities of the last mile problem more effectively. In doing so, they not only enhance productivity and efficiency but also unlock the true transformative potential of Knowledge Management in the digital era. The last mile, once a formidable obstacle, becomes a pathway to seamless collaboration, innovation, and success in the knowledge-driven landscape of tomorrow.

Editor’s note: This article is in association with Lucy.

Photo by Bruno Saito

The post Overcoming the ‘last mile problem’ in knowledge management: A guide for IT teams appeared first on AI News.