The rapid development of artificial intelligence (AI) is already starting to change the world. Advances in AI have made it possible to completely transform the user experience, and the demand is only growing. With the rising popularity of virtual reality (VR) headsets, more users are being introduced to this revolutionary technology at an earlier age. Research shows there are around 171 million people currently using VR worldwide, and out of those users, the vast majority are teenagers or younger. 

Over the past year, technology companies such as Meta began lowering the age restrictions for its VR apps to reach younger audiences, and while there are some restrictions in place now to ensure the safe use of these devices, this technology still poses a major threat to these audiences. The use of Virtual Reality technology can be beneficial for children if used responsibly, however, more action needs to be taken to better protect these audiences from the dangers of this disruptive technology. 

Online Safety in the Digital Age 

VR is not new.

The idea of using VR has been studied since the 1990s. Fast forward to today, healthcare companies, schools, and households are all harnessing AI-powered technology, and younger audiences are among some of the most frequent users. 

All technology generally has both positive and negative benefits to society, and VR headsets are no different. For example, new modes of learning delivery certainly should result in more effective education and children who enjoy the process more than traditional schooling.

Rather than just reading about a subject, a child can enjoy a fully immersive and interactive experience that can be much more enjoyable and effective than traditional methods.

On the other hand, many raise concerns about the safety and privacy of these devices. Many VR apps have already taken certain precautions to prohibit the unsafe use of these devices by children. Some of these restrictions involve requiring preteen’s parental approval to set up an account or young users only seeing apps and content rated for the pre-teenager age group. However, as previously mentioned, these limitations – while a good starting point – are not going to solve all the safety concerns that parents and guardians have with children using these apps. 

Identifying Friend from Foe

The age changes being made to these devices make children fall victim to nefarious individuals. VR represents a world that requires a nuanced understanding of potential threats because the cues that exist in the physical world can be more easily masked in VR. More specifically, the time spent in these connected worlds is a largely invisible experience, which causes serious issues when identifying friends from foes.

Pre-teenagers developmentally are less equipped to detect a threat to their physical or emotional well-being which requires this more nuanced understanding. Similarly, pre-teens are simply less intellectually and emotionally developed than older children. This presents an even larger risk to those in that younger age group.

Strangers in cyberspace can more easily impersonate “friendly” actors in VR, and that, combined with the lack of sophistication required for pre-teens to detect this, means a much bigger threat to all children – especially those who are younger. Additionally, pre-teens can be exposed to inappropriate and violent content without teachers or guardians being fully aware. There are also privacy concerns associated with VR devices. Several apps can collect data on users, such as eye movement and facial recognition, which many parents or guardians may not be comfortable with. For all of these reasons, there needs to be a better way to protect children when they are actively using these devices. 

A Better Path Forward to Securing the Metaverse 

The answer to this growing problem will undoubtedly lie in the involvement of parental figures.

Very strong controls around identity and content that children interact with must be implemented to protect them. More specifically, to protect children, all persons in the “spaces” that they interact in must have strongly authenticated and verified identities that can assert their relationship to the child, as well as assert permitted attributes that parents must approve before being allowed to interact with children.

For example, the real identity of the person and relationship to the child must be approved. 

Furthermore, the VR equipment itself must have controls to ensure that the person presently wearing it is an authentic and verified individual to whom the account belongs to prevent impersonation. Concerning content, strong controls around the age-appropriateness and classification of it must be implemented. This can be aided by AI to automatically detect and classify malicious content. All of these restrictions combined can better safeguard both children and pre-teens from the dangers of these devices. 

AI poses immense challenges for user security, most of which we are only beginning to understand.

Looking ahead, running age-appropriate and safe virtual experiences will become one of the most important challenges facing the world. As the popularity of VR devices continues to grow, particularly among younger audiences, both companies and parental figures will need to consider implementing strong controls. Once the security and identity threat is under control, only then can we begin to truly protect the health and safety of younger audiences in the metaverse. 

[To share your insights with us, please write to sghosh@martechseries.com]

The post To Help or To Harm: the Potential for Virtual Reality to Shape Future Generations appeared first on AiThority.

The post AiThority Interview with Dr. Alan Baratz, President and CEO of D-Wave appeared first on AiThority.

The way businesses engage with customers is an ever-evolving practice, and depending on how it’s conducted, it can have a direct reflection on important measurables including revenue. Independent Software Vendors (ISVs) are continuously striving to enhance user experiences by leveraging today’s technologies.

Innovation, Agility, and Integration

ISVs are typically known for their ability to adapt and respond accordingly to specific needs. They can quickly respond to market demands and technological advancements, introducing new features and capabilities that can improve the overall customer experience. ISVs develop software that can seamlessly integrate with existing systems and platforms, and that integration capability is vital for businesses looking to enhance customer engagement without disrupting current workflows.

Many ISVs also specialize in Customer Relationship Management (CRM) solutions, helping businesses manage and optimize their interactions with customers. These systems centralize customer information, streamline communication, and enhance overall engagement.

Recommended: Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

Transforming User Experience Through Collaborative Innovation

Here are just a few ways in which ISVs modernize user experiences:

• User-centric design: ISVs adopt user-centric design principles to create intuitive and user-friendly interfaces. They conduct thorough user research to understand the needs, preferences, and pain points of their target audience.

• AI and machine learning: Incorporating artificial intelligence (AI) and machine learning (ML) algorithms enables them to provide personalized experiences. This includes features such as recommendation engines, predictive analytics, and intelligent automation, enhancing the overall user experience.

• APIs and integrations: Their open APIs allow seamless integration with other software and services, meaning users can connect their preferred tools and enhance functionality to suit specific needs.

• Continuous feedback: ISVs use agile development methodologies, which involve continuous user feedback and updates. This allows them to quickly respond to changing user needs and preferences, ensuring that the software remains relevant and effective.

The Future Lies in Personalized and Secure Engagement

ISVs often focus on creating niche and specialized software solutions tailored to specific industries or business processes. Those specialized tools enhance customer engagement by addressing unique challenges and providing targeted functionalities. One such example is that their solutions can be customized to meet the specific requirements of individual customers. This level of personalization enhances the customer experience and fosters a sense of ownership among customers. When customers feel that a product has been tailored to their needs, they are more likely to remain loyal to that product.

Read AI ML News: Quantasy Announces Innovative Video Feature in True Voice App to Enrich Mindfulness and Community Engagement 

It’s also important to note their relationship with security and compliance, which are increasingly critical and ISVs focus on developing solutions that prioritize data security and adhere to industry regulations – which is essential to maintaining customer trust.

Integrated Payments: A Game-Changer in the Payment Ecosystem

ISVs play a crucial role in the payment ecosystem because businesses of all sizes are increasingly drawn to software platforms that offer more than basic functions. By integrating payments directly into their software platforms, ISVs offer a seamless user experience. Integrated payments allow ISVs to grow the size of their market with roll-out processing solutions around the world via cloud-connected processing partners. Integrated payments also offer excellent flexibility in designing a preferred checkout process and make it easier to introduce new payment methods such as Buy Now, and Pay Later (BNPL).

ISVs are a key player when it comes to the future of customer engagement and they may be invaluable when it comes down to retention. The specialized, innovative, and scalable software solutions can align with the evolving needs of a business and its customers. The future of engaging with customers means forming strong relationships that blend well with the technology available today, and innovations to come.

AI ML Insights: Responsible AI Institute’s Amanda Lawson Receives Recognition from Women in AI Ethics

[To share your insights with us, please write to sghosh@martechseries.com]

The post The Future of Customer Engagement: Understanding how Independent Software Vendors (ISVs) Operate appeared first on AiThority.

Do you remember the Monopoly man having a monocle? Did you grow up reading the Berenstein Bears? Have you ever enjoyed Walker’s Salt & Vinegar chips in the green bag? 

If any of these experiences sound familiar, get ready to question your reality: The Monopoly man has no monocle, “Berenstain” is the bear family name, and Walker’s chips come in a blue bag. These gaps between reality and our shared memories have grown common enough to have a name — the Mandela Effect. It describes a phenomenon in which a human recalls something that never existed or existed differently than memory serves. 

Questioning your own recall can be an unsettling experience. But, for anyone whose livelihood has been impacted by the rise of AI, the Mandela Effect is growing even more ever-present as a similar phenomenon is showing up in generative AI outputs. 

What’s Causing AI to Have “Hallucinations” Akin to Human Misremembering?

Not unlike humans, AI recognizes patterns and uses repetition to reinforce what’s already known. Also, like humans, if an AI system is only relying on pattern repetition to learn, the technology sometimes arrives at an erroneous conclusion or belief — and an AI “hallucination” is born.

With Generative AI applications beginning to do the work of humans in a variety of roles, AI hallucinations can become extremely problematic. These errors can make us look foolish in front of clients, lead to bad business decisions, and reinforce harmful biases.

Safeguarding AI from making these mistakes is possible, but we must first understand what causes them and then apply several key principles when building AI applications. 

Understanding the Causes of AI Hallucinations

AI is a system that functions as a consensus engine. AI-powered technologies take in massive amounts of information for training and extract dependencies to answer questions or formulate text. But, like our own brains, AI technologies aren’t perfect. For example, Google’s AI Bard famously hallucinated an answer during a product demo — costing the company millions in lost share value. 

To make matters worse, AI anomalies are not always easy to spot. AI is designed to provide convincing and confident answers, so if you aren’t up to speed on a particular topic or lack time to fact-check, you could unwittingly rely on bad information. 

There are several approaches to fixing this problem — and none of them require us to abandon the further development of this amazingly useful technology. Instead, we must take more care in how we use AI technologies and accept the fact that AI can occasionally invent things out of thin air.

Top AI ML Insights:

Data Lineage Sheds Light on Generative AI’s Hallucination Problem

Why Designing Unbiased AI Is So Hard

Constructing any intelligent system poses a significant challenge because its decision-making prowess relies on the quality of the data sets employed during development, as well as the techniques used to train its AI model over time. However, an entirely flawless, impartial, and accurate data set is a fantasy — it doesn’t exist. This presents a formidable obstacle in crafting AI models that are immune to potential inaccuracies and biases.

Facebook’s parent company, Meta, is a perfect case study for understanding how data impacts AI training. The company initially made its new large language model (LLM) available to researchers studying natural language processing (NLP) applications that power virtual assistants, smart speakers, and similar tools. After some exposure to the model, researchers determined that the new system “has a high propensity to generate toxic language and reinforce harmful stereotypes, even when provided with a relatively innocuous prompt, and adversarial prompts are trivial to find.” To say this is not ideal is an understatement.

Meta hypothesized that the AI model — trained on data that included unfiltered text taken from social media conversations — is incapable of pinpointing when it “decides” to use that data to generate hate speech or racist language. This example is further proof that AI systems are not capable of reflecting on the content they are creating and should not operate independently of human decision-making processes and intervention.

Various Approaches to Solving the AI Hallucination Problem

So, if we can’t completely trust AI, how do we nurture its development while reducing its risks?

By embracing one (or more) of several pragmatic ways to address the issue:

Institute Domain-Specific Filtering.

One helpful approach to navigating AI hallucinations is to apply domain-specific data filters, which can prevent irrelevant and incorrect data from reaching the AI model while it’s being trained.

For example, imagine an automaker that wants to incorporate an AI that detects soft failures of sensors and actuators in an engine for a small, four-cylinder vehicle. The company likely has a comprehensive data set covering all of its models, from compact cars to large trucks and SUVs. But the automaker should filter out irrelevant data — say, data specific to an eight-cylinder truck — to avoid misleading the four-cylinder car’s AI model.

OpenAI’s recently announced customizations are another variation of this approach: feed custom data sources to the AI as helpful context, aiming for the technology to focus on this provided “knowledge base” most when generating answers for users. Despite good intentions, this effort is, ironically enough, heavily “biasing” the AI system with the custom information provided. Hopefully, the material provided is free of unwanted biases, but it’s also a good reminder that a single mitigation approach alone is unlikely enough as we work to reduce the risks surrounding AI tools.

Keep Humans in the Loop.

We can also establish filters that protect the world from bad AI decisions by confirming each decision will result in a good outcome — and, if not, making sure a human can prevent the technology from taking action.

To achieve this, we must implement domain-specific monitoring triggers that instill confidence in the AI’s ability to make specific decisions and take action within predefined parameters. However, decisions outside of those parameters should trigger and require human intervention and approval.

Run Parallel Systems.

A third guardrail against AI biases is to use proven systems to check newer models. In this instance, developers can run more trusted systems in parallel to newer ones, to spot discrepancies and mistakes.

Much like the other methods of preventing bias and avoiding hallucinations, this approach requires humans to make judgment calls about outputs and potential adjustments. This technique is similar to the way we guide a child to learn a new skill, such as riding a bike.

Recommended AIThority Conversations:

AiThority Interview with Keri Olson, VP at IBM IT Automation

An adult serves as a guardrail by running alongside to provide balance and guidance so the child stays on course — and avoids making rash decisions or dangerous turns, learning along the way. 

AI’s Future Depends on Human Care

Minimizing AI hallucinations is possible, but it will require a lot of human involvement. Misapplication of AI technologies will only create hallucinations, inaccuracies, and biases in these systems — everyone must be vigilant when choosing to use AI tools. But, with a thorough understanding of the stakes, we can help AI systems avoid some very human mistakes.

[To share your insights with us, please write to sghosh@martechseries.com]

The post How to Avoid AI Hallucinations From Becoming Costly Liabilities appeared first on AiThority.

Embracing Edge AI can be a game-changer for businesses, propelling them toward a future where real-time data processing and decision-making are at the forefront of innovation. Edge AI is a term that refers to the deployment of artificial intelligence (AI) models closer to users and devices, either on-premises — such as in a retail store or bank branch — or on edge computing platforms. With this approach, AI processing occurs near where data is created, on the “edge” of the network, in a decentralized manner, instead of using cloud-based solutions or centralized computing. This results in reduced latency, improved performance, and enhanced privacy and security.

Many industries, including retail, finance, manufacturing, healthcare, automotive, and telecommunications, are investing in Edge AI to increase efficiency in their operations — essentially by providing a large set of automation possibilities — and to improve their customer experiences. Demand for this technology is growing, and the overall state of Edge AI adoption is gaining traction.

According to Gartner, “More than 55% of all data analysis by deep neural networks will occur at the point of capture in an edge system by 2025, up from less than 10% in 2021.” And according to IDC, by “2023 more than 70% of organizations will run varying levels of data processing at the IoT edge.”

AIThority Insights:

The Role of AI in Super-empowering Customer Service Agents

The Benefits of an Edge AI Approach

One of the main benefits of Edge AI over running AI in centralized computing is improved efficiency. By processing data locally rather than sending it to a central server, Edge AI can drastically reduce latency and remove bandwidth constraints, leading to faster decision-making and improved operational efficiency.

This allows for real-time or near-real-time processing of data, a critical feature in many applications, such as autonomous vehicles, manufacturing processes, and healthcare monitoring. This type of processing is also a key advantage when the goal is to improve the customer experience.

Businesses also save on bandwidth and data storage costs by minimizing the need to transmit vast amounts of raw data back to the cloud or a data center; instead, only the relevant preprocessed information is sent.

This is much more scalable and sometimes can enable solutions that otherwise wouldn’t be technically or economically feasible, and it also brings increased resiliency: Edge AI systems can continue to process data even when they lose connectivity, enhancing the reliability of weak communication links and optimizing costs.

Last, but not least, the Edge AI approach can offer enhanced privacy and security: since data is processed locally, Edge AI reduces the risk of data breaches or loss during transmission. This makes it suitable for industries with stringent privacy regulations because sensitive data does not need to leave the premises.

Edge AI in Action

It has been said that “data is the new oil,” which means that any connected source of information is also a juicy target for criminals. AI is increasingly important in these scenarios as the attacks become more advanced.

Top IT Leadership Article: What Financial Services Organizations Need to Know About Zero Trust Maturity

Edge AI security measures, like access control, can be delegated to edge nodes, which can run sophisticated AI-based algorithms for inference and behavioral analysis that can detect and stop suspicious activity from cyberattacks — including the feared “zero-day” attacks — before they even enter business networks and cause any harm.

In retail, Edge AI can enable smarter customer experiences, such as personalized recommendations based on real-time in-store behavior, including monitoring self-checkouts to reduce losses or running real-time inventory management. In manufacturing, it can assist in predictive maintenance by processing data from numerous sensors to predict equipment failures and suggest timely maintenance, minimizing downtime.

In healthcare, patient monitoring devices can use Edge AI to process health data in real-time, alert healthcare providers of any immediate risks, and ensure patient privacy by keeping sensitive health data localized. Security systems equipped with Edge AI can process video footage in real-time for facial recognition, anomaly detection, or immediate threat analysis and alerting.

Telecom operators can use Edge AI to optimize network operations by analyzing network traffic in real-time and dynamically managing network resources. Similarly, Edge AI can be used for managing smart power grids, detecting anomalies, and predicting energy demand to optimize power generation and distribution.

Steps for Preparing to Adopt Edge AI

Organizations looking to adopt Edge AI will first need to identify the specific use cases or areas in their business where Edge AI could provide a considerable benefit. This should be followed by an evaluation of the technical requirements since Edge AI involves different technology than traditional cloud-based AI.

The next step is to ensure your team has the necessary skill set or that your organization has a mechanism in place for training. Teams will need to be educated on edge computing, AI model development, and edge node orchestration.

At this stage, a partnership with edge computing providers and companies that provide ready-to-use AI models is all it takes to start collecting results, as your company will be able to leverage an integrated solution and vendor expertise.

Edge AI aligns with strategic growth initiatives, seamlessly integrates with change management practices, and may even catalyze the emergence of groundbreaking business models. The adoption of Edge AI signifies a commitment to staying ahead of the curve, ensuring that your enterprise remains competitive in a rapidly evolving digital landscape.

[To share your insights with us, please write to sghosh@martechseries.com]

The post How Can Businesses Benefit from the Edge AI Boom? appeared first on AiThority.

Imagine if ‘Team Santa’ was a real entity and it’s their busiest time of the year. Failure is not an option. If logistics and supply chains fail, kids across the world will be throwing gargantuan tantrums. The stakes couldn’t be higher for Team Santa. In this article, we unravel the complexities of ‘Team Santa’s’ logistics and supply chain management and how AI could be a real ‘game-changer.’

Quantifying the Challenge: How Many Presents Does Team Santa Deliver? 

Our first task is to quantify the challenge by examining Team Santa’s first crucial metric – how many parcels is Team Santa likely to deliver with only one sleigh and eight reindeer, one with a flashing red nose, in one single night?

At SpendConsole, we take the time to analyze the logistical complexity of your business. So for such an important issue as identifying Santa’s use of AI and ML, we’d need to see quality insights. As an example:

• The number of kids across the globe under 14 is estimated by Statista to be just under 2 billion.
• 32% of the world’s population is of the Christian faith and, therefore, most likely to celebrate Christmas, giving us 640 million kids likely to receive pressies.
• Sadly, very limited up-to-date data is available to show how many kids believe in Santa, despite an information-drenched world already sapping the sheer joy and beauty out of our collective innocence.
• We have to go back to an academic study from the late 1970s (yes, it actually happened), the conclusions of which many argue still stand today:
  • 85% of four-year-olds believe in Santa
  • 65% of six-year-olds believe in Santa
  • 25% of eight-year-olds view old Saint Nic favourably.

Assuming an even distribution of kids between 0-14, this reduces the number of kids likely to receive pressies to around 238 million. The question is, how many kids have been so naughty they can’t get on Santa’s nice list?

In the absence of a formal naughty scale, our statistical assumption is that only 15.9% of kids have been naughty. With the greatest coincidence ever, this leaves us with an exact 200 million kids likely to receive pressies – oh the power, beauty, and convenience of statistics!

Finally, Statista shows an average global fertility rate of 2.27 children per female in 2021. This means Team Santa should expect to deliver to around 88.1 million unique households! Assuming three presents per well-behaved kid, that’s around 530 million parcels for same-day delivery. So many pressies to source, prepare, wrap, and deliver – and so much milk and cookies for Santa!

Benchmarking Team Santa Against Industry Best Practice

With 530 parcels, we can see how Team Santa has their work cut out. This is especially true when we consider Amazon, the largest e-commerce company globally, which delivers an estimated 1.6 million parcels daily. While Amazon’s infrastructure has been tried and tested, how would Amazon consider handling 530 million parcels for same-day delivery with one sleigh and eight reindeer? Team Santa has decided the only outcome of impossible is possible as they target delivering over 300 times more in one day than Amazon.

Clearly, such amazing performance opens up Team Santa to numerous supply chain management issues that need to be met proactively by looking at effective demand management.

AiThority.com AI ML Trends: 10 AI In Manufacturing Trends To Look Out For In 2024

Demand Management: Using AI and Big Data for Forecasting

At the heart of Team Santa’s Just-in-Time (JIT) methodology, there must be a sophisticated demand forecasting AI. To get details on the presents kids want and to meet that demand, Team Santa relies on some key channels for data.

• Their global Santa franchise – Santas in shopping malls
• Letters and emails to Santa at the North Pole

Handwritten notes are likely subject to Optical Character Recognition (OCR) software. If Team Santa has followed our lead by using proven AI to turn supplier invoices into ERP-based data, they will use AI to turn those letters directly into purchase orders and contracts in real time. The biggest challenge, however, is timing.

The marketplace data is collected from around mid-November onwards. With extended lead times and shipping times from international suppliers, Team Santa must have a very clear idea of what well-behaved kids want as presents well in advance. Team Santa must proactively use ML to look at the patterns of purchase from last year and look at real-time data models on kids’ current behaviors.

It is also likely that Team Santa has partnerships with all the major Social Media outlets to predict the toys kids will likely want proactively. Our analysts checked all the public social media companies’ annual reports, and we can’t find any confirmation of these strategic partnerships with Team Santa – perhaps Team Santa has strong NDAs!

Supplier Performance and the ‘Silly Season’

Having been through some serious economic challenges over the past three years globally, it is unlikely there will be any extra favors for Team Santa from their suppliers. As a highly seasonal business, Team Santa’s cash flow will tend to be very lumpy. Suppliers do not want to be a tail-end Charlie and get paid last. Instead, they will want to be paid before shipping their toys. It will be critical for Team Santa to have a complete overview of their suppliers, the supplier data, and supplier performances to create Dynamic Supplier Relationship Management tools. With access to this data across all suppliers, they can adjust their financial strategies and negotiate early payment settlements.

Our clients have found our supplier reporting from our AP Transformation platform effortlessly helps them plan – so no doubt Team Santa could be using something similar to help scale their efforts.

AiThority.com News: Stereolabs and Syslogic Team Up to Advance Robotic Applications that Can Withstand Outdoor Condition

Logistics and Delivery Performance

Team Santa delivers at nighttime, typically between 10 pm – 6 am across multiple time zones when kids are asleep. Therefore, Team Santa must be able to cross-match delivery times with kids’ sleep patterns. AI is very powerful for identifying patterns in data and providing matches and exceptions that would take humans far too long to calculate. Perhaps, Team Santa also has access to biometric data from kids’ smartwatches to give the necessary sleeping insights.

Fraud Prevention: Dealing with Naughty Santa Scammers

Finally, whenever there is time pressure to deliver products fast and to pay for them quickly, there will always be those trying to scam Santa. We see this in action with our clients in normal times, where scammers have used AI-generated voice clones of senior management staff and sent subsequent extremely well-worded emails, requesting fast payment for new suppliers.

Given Team Santa deals with so many international suppliers, and many new ones, they are probably using automated compliance tools to validate new suppliers against local compliance databases (if not, of course, they can always reach out to us on the Santa express line).

Just-in-Time (JIT) Inventory Management 

Team Santa’s supply chain management has to be a marvel of logistical prowess and festive efficiency. They have taken JIT inventory management to a new level, operating at a scale that shows unadulterated talent. To make this workable, undoubtedly Team Santa leverages the best-in-class, technologies, and best-in-class practices.

Best practice determines that it is vital to have preferred and qualified suppliers. Thankfully for Team Santa, in a digitized environment, inventory replenishment is automated with clear contracts, pricing, and delivery timeframes, meaning that demand for presents can be catered for in a dynamic, real-time way. Team Santa must be super adept, as we never seem to hear of any problems associated with any failed deliveries across social media.

Top AI ML News: LILT Announces Enterprise AI Controls and In-App Multimodal Translation

Team Santa and the Season of ‘Festive Funtech’ 

Team Santa’s logistical excellence is a great lesson for us all to learn about the power of AI and ML when solving direct business problems. Importantly, it’s a valuable lesson in how to use AI and ML to help review supplier management and craft the supplier performance data necessary to help manage inventory and empower that feeling of ‘Festive Funtech.’

[To share your insights with us, please write to sghosh@martechseries.com]

The post How AI Could Transform Santa’s Supply Chain Challenges appeared first on AiThority.

Hi Hendrik, welcome to AiThority.com’s Predictions Series. Please tell us about the evolution and ever-changing role of Artificial Intelligence (AI) capabilities in sales enablement.

I think Sales enablement companies are taking AI to the next level already. 

As buyers get smarter with more information at their fingertips, sellers will need to leverage AI to keep up. 2023 was undoubtedly the year in which AI entered the mainstream consciousness, but the nuances and the advantages that AI can offer in a business environment, are still being worked out.

To maximize the almost unlimited possibilities that AI can offer, sellers will need to harness it in their sales meetings to drive better buyer interactions, stay one step ahead and be ready to add meaningful value to the conversation.

Whether it’s using AI to instantaneously surface the most compelling pitch deck, talk track, or playbook, AI will enable sellers to show up as trusted advisors, earning buyer confidence and becoming indispensable to the customer journey. In addition to sales meetings, AI will also be leveraged to augment the role of the internal sales coach, making sales development programs smarter and more scalable. The latest innovations mean that AI can provide actionable insights on seller skills and behaviors, helping define a baseline for sales readiness, and deliver targeted support to the reps who need it most. And if you’re not integrating AI into your sales strategy, you will be opening yourself up to a brief creep from your competitors.

With AI, can we finally expect alignment between Sales and Marketing functions? 

Yes, we can finally see a better alignment between Sales and Marketing using AI.

For years businesses have tried and failed [epically] to align sales and marketing teams. In a recent survey, approximately 70% of organizations said their sales-marketing alignment was not very good, leading to frustration and finger-pointing. However, new enablement platforms coupled with AI will finally make this a reality. When sales and marketing teams can better align, they can sustain and nurture relationships with buyers.

Alignment will also ensure maximized revenue and growth opportunities for the business.

Bringing sales and marketing teams together will allow them to collaborate on key business goals and see how each department impacts the other.

Customers will be delighted when their needs are anticipated, understood, and managed in real time.

With alignment, there will be more rapid deal cycles as customers are engaged and ready to buy.

What avatar would the Customer Relationship Management (CRM) solutions take in 2024?

According to me, CRMs would evolve into Sales Enablement Platforms (SEPs).

Customer Relationship Management (CRM) and Sales Enablement Platforms (SEP) are both crucial tools in the modern sales and marketing landscape, but they serve distinct functions. While CRM systems were about managing customer relationships and data, the reality is they have become more about customer record management. Meanwhile, sales enablement platforms are about empowering sales teams to engage more effectively with those customers.

In today’s world, businesses need a system of engagement to complement their system of record, and this has made Sales Enablement technology a must-have. Put simply, it is where the relationships happen. CRM systems are designed to manage interactions with current and potential customers, tracking the history of customer engagements, sales opportunities, and service requests. They are invaluable for maintaining detailed records of customers and long-term relationship building.

On the other hand, SEPs focus on equipping revenue enablement teams with the tools, content, and information needed to sell more effectively. These platforms provide resources like sales training materials, content management, and analytics tools to optimize the sales process.

It’s only a matter of time until SEPs outweigh CRMs in importance and additive value.

Recommended AI ML Article: State of Implementation of Generative AI (Gen AI) in Marketing

Thank you, Hendrik! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]

• About Hendrik Isebaert
• About Showpad

About Hendrik Isebaert

Hendrik Isebaert is Showpad’s Chief Executive Officer. After joining the organization in 2016, Hendrik embarked on an extensive business immersion, working in leadership positions across Showpad’s dual headquarters in the U.S. and Belgium.

Beginning his Showpad career as Chief of Staff, Hendrik was subsequently promoted to the role of Managing Director for Europe, Middle East and Africa, and latterly to Senior Vice President of Revenue globally.

About Showpad

Showpad is a leading sales enablement platform.

The post AiThority Interview with Hendrik Isebaert, CEO at Showpad appeared first on AiThority.

Hi, Rita. Welcome to the Interview Series. Please tell us about your role at Cloudflare. How did you arrive at Cloudflare?

I joined Cloudflare seven years ago as a solutions engineer, helping our customers achieve success with our technical deployments for our performance and security services at the time. Before Cloudflare, I was working as a software engineer, so when Cloudflare Workers was released it seemed like it could present a massive opportunity to change the way applications were built, so I joined the product side to work on it, and have been helping grow our developer offering since (which now includes our AI products).

What has changed in the AI data engineering landscape since the launch of ChatGPT? How are generative tools transforming DevOps and programming techniques?

On the DevOps and programming side, we’ve started seeing generative AI transform the way developers were building before ChatGPT, with the release of Github’s Co-Pilot.

We’re already seeing massive productivity gains from developers using code-generation tools to accelerate development, especially when it comes to generating boilerplate code, tests, and tasks that have been solved many times but are time-consuming. The other big shift we’re already starting to see and I believe we’ll see even more of is generative AI opens up the door for more collaboration amongst developers, and designers, and a lower barrier to entry for beginners.

Could you highlight Cloudflare’s approach to innovating with generative AI tools? Please tell us more about Mistral 7B.

The challenges developers face with deploying AI today are very similar to the challenges we’ve been solving with our developer platform for the past 6 years — a lot of developer productivity is wasted on setup, provisioning, optimization of resources, scaling and performance. So we wanted to bring the same serverless approach we brought to generalized compute to GPUs and AI deployments and allow developers to start building applications with AI without having to think about infrastructure.

We launched Workers AI in September with support for several models in our model catalog out of the box to get started with an open-source LLM being as easy as writing a few lines of code. We’ve been expanding our model catalog since launch and recently announced support for Mistral 7B which is an exciting new open-source LLM that outperforms previous LLMs in many ways, despite being a much smaller model.

How do you think AI is going to reach critical mass for businesses?

There’s been a lot of excitement recently about really large language models, however, anyone running or deploying them in production will be faced with reality pretty quickly: it’s really expensive to run them, and hard to even find and provision infrastructure for them. The next wave of innovation we’re going to see will likely go the other way, in getting smaller models (single-digit-billion parameter models, as opposed to hundreds-billion parameter models) to perform better.

With fine-tuning, these models are more likely to provide value to businesses fast, and power better experiences without breaking the bank.

AiThority Interview with Snyk’s CMO Jonaki Egenolf

On a related note, there’s been so much great innovation going on with open-source models, and the tooling ecosystems that have sprung up around them. It will be interesting to see how open-source model adoption plays out compared to proprietary, but I would say open-source is always a good bet.

With models taking multiple seconds to execute, does performance really matter in AI?

AI is still a fairly nascent technology — so our expectations are in line with that. But if AI is to become an integral part of our daily lives, performance is going to improve significantly and become a standard.

The web started out very similarly, with users being accustomed to waiting multiple seconds if not minutes for a web page to load. Today, however, engineers are optimizing for every large millisecond of wait time because as it turns out, there’s a direct correlation between performance and conversion rates.

Especially once AI starts powering more real-time experiences that are meant to emulate humans (think ordering a coffee at a drive-through), we’re going to expect experiences that have processing and reaction times that mimic those of human interaction.

Your predictions for the AI engineering industry in 2024: 

I have two big predictions for AI development in 2024. 

The first is that surging AI bills will land on developers. As AI experimentation skyrockets, so do AI bills.

Developer teams will be required to answer for this AI spend as CFOs will not accept unbounded and unpredictable costs for much longer and there will be added pressure to prove the return on investment. Tools offering insights, guardrails, and monitoring, especially in experimentation, are going to be critical for every dev team’s AI arsenal. 

AiThority Interview with Abhishek Shrivastava, VP of Product at LinkedIn

The second is that smaller models are going to become the default.

While it’s been exciting to see what we can achieve through the hardware that’s allowed the training of extremely large hundred billion parameter models, the reality is that in production there are diminishing returns to running such large models, while the costs of running them are disproportionately higher. What we’re going to see is more fine-tuning of smaller models, and innovation around customizing them and making them smarter.

Lighter notes:

Burn the midnight candle or soak in the sun?

Neither! I’m a morning person, but I love a cozy day indoors.

Coffee, or Tea?

Coffee

Your favorite Cloudflare offering that you want everyone to know about?

I’m biased, but Cloudflare Workers! It takes just a few seconds to try and you can get an application up and running so quickly and it will continue to scale regardless of how much traffic you get.

First memorable experience in your career as a technology leader?

Shipping workers.dev and launching it at JSConf EU. It was surreal seeing apps pop up the next day built on top of the tools we had just launched.

One thing you remember about your employee (s):

There are so many different ways to be a successful PM. Some PMs are more analytical and live by dashboards, others are great speakers and presenters, some love getting down into the API design. You want a mix of these different skills on your team to both foster different perspectives and learning from each other. I feel so lucky about the team I get to work with, and learn from them myself every day.

Most useful app that you currently use:

Google Calendar – it dictates my entire day.

Thank you,  Rita! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]

• About Rita
• About Cloudflare

About Rita

Rita Kozlov is the Sr. Director of Product for Cloudflare‘s developer platform, and AI initiatives. She helped launch Cloudflare‘s initial compute offering Workers, and has since been a critical part of developing Cloudflare‘s developer strategy, including launches such as D1, Cloudflare Pages, and recently Workers AI. Rita started her career in Software Engineering, moving on to solutions engineering before finally finding her passion in product development.

About Cloudflare

Cloudflare, Inc. (NYSE: NET) is the leading connectivity cloud company. It empowers organizations to make their employees, applications and networks faster and more secure everywhere, while reducing complexity and cost. Cloudflare’s connectivity cloud delivers the most full-featured, unified platform of cloud-native products and developer tools, so any organization can gain the control they need to work, develop, and accelerate their business.

Powered by one of the world’s largest and most interconnected networks, Cloudflare blocks billions of threats online for its customers every day. It is trusted by millions of organizations – from the largest brands to entrepreneurs and small businesses to nonprofits, humanitarian groups, and governments across the globe.

The post AiThority Interview with Cloudflare’s Rita Kozlov, Senior Director of Product, Developer Platform appeared first on AiThority.

Hi Ryan, welcome to our Interview Series. Please tell us a little bit about your role and responsibilities at Salesforce.

I’m the SVP of Product Management of Service Cloud at Salesforce, where I oversee the platform and help thousands of companies utilize our trusted AI for customer service to create seamless conversational, predictive, and generative AI experiences for agents and customers.

Could you tell us more about the ideal customer profile of Salesforce Service Cloud? Which industries rely on your solutions and services?

Delivering efficient customer support is crucial to driving satisfaction, loyalty, and positive customer experiences for companies of all sizes, in every industry, around the world. Any company looking to streamline and enhance its customer service operations is a good fit for Salesforce Service Cloud. Service Cloud helps organizations of all sizes, like Heathrow Airport and AAA, deliver personalized customer service with AI-powered insights, recommendations, and productivity tools.

Salesforce Service Cloud also offers industry-specific applications for business across more than 13 industries, from IT to retail, consumer goods, and beyond. Leading solar services provider Sunnova is using Service Cloud to easily track the performance of its hardware, automatically create work orders, and resolve cases faster using predictive AI from Einstein for Service.

Recommended: 69 Percent of Accountants Said AI Has a Positive Impact on the Profession

Tell us more about your newly launched Sales and Service AI capabilities. Can you tell us about Einstein GPT’s role in these announcements?

Salesforce recently debuted new AI capabilities for both Sales Cloud and Service Cloud. The Sales Cloud innovations are designed to boost productivity and help sales teams close more deals, faster. Service Cloud boasts a broad set of AI capabilities, including Einstein Copilot for Service, which automates personalized responses based on real customer interactions to help resolve issues faster. The newly launched AI innovations for Service Cloud bring AI-powered insights directly into the flow of work for service professionals. These capabilities transform how service teams deliver value across customer touchpoints — from process and automation to operations — to boost agent productivity, cut costs, and enhance customer satisfaction.

Our latest launch is Service Intelligence for Service Cloud with Einstein Conversation Mining. This innovation uses AI to analyze customer chats and emails to uncover insights. For example, the new solution can analyze a customer’s specific challenges and then asses the likelihood of complaint escalation to proactively address the customer’s issue. It could also identify a trend in customer cases, like if there’s an increase in customers asking about a return policy. With that information, service agents then train a bot to spot similar customer cases and automatically surface a self-help article on returns to help them resolve the issue faster.

How does Service Cloud align with Marketing and Sales Cloud? What are the benefits of using Salesforce for omnichannel customer experience management?

Omnichannel is designed to combine all channels into one seamless experience, whether before the purchase, during the purchase, or after the purchase. Omnichannel helps break down the siloes that can complicate customer interactions. With an omnichannel mix, information is shared between sales, service, and marketing team members to ensure that support staff have information readily available to assist the customer.

Renowned Italian fashion designer, Boggi Milano, adopted the omnichannel mix to improve their customer experience from discovery to service. With this approach, Service Cloud enables Boggi Milano customers to begin shopping online, chat through purchasing decisions with a service rep, and transition seamlessly to a personalized in-store retail experience.

What kind of infrastructure does a financial services organization need to match the demands of modern contact center transformation?

Could you share some use cases from the financial services industry specifically mentioning your differentiated Sales and Service AI products and services?

The customer service demands of modern financial service organizations are unique. Salesforce technology is designed to help these organizations tackle constantly evolving demands by providing the tools to tailor individualized client experiences.

The AI revolution is here, but at the foundation of the contact center transformation is really a data revolution. Industries like financial services need the infrastructure in place to ensure that their customer data can be the basis of next-generation AI experiences. Building a proactive, personalized service experience based on real customer data and interactions can help these organizations solve customer challenges more efficiently.

Top News:

Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

For example, Santander UK relies on Service Cloud for automation in the customer support process, which has transformed how it manages relationships. Powered by Service Cloud’s Live Agent feature, Santander helps subscribers of its digital platform Navigator directly connect with support team members via live chat. Every new business contact and each subscriber case is automatically created in Service Cloud, helping Santander streamline other important tasks with intelligent workflows. These AI integrations enable Santander to personalize experiences for each client’s unique journey and build true partnerships with their growing businesses.

Beyond financial services, organizations across industries count on Service Cloud to help support their customer needs. Organizations spanning industries like retail, healthcare, manufacturing, and more use Service Cloud to incorporate automation into the service process and nurture customer relationships throughout every stage of the lifecycle.

Lighter notes:

Coffee, or Tea?

A Chemex Pourover is absolutely part of my morning ritual at home, but a cuppa is always welcome when traveling.

Most useful app that you currently use:

I wouldn’t even know how to answer these questions without Slack’s new Canvas feature for team collaboration.

Your favorite Salesforce product marketing initiative that you want everyone to know about:

It’s difficult to pinpoint just one initiative, but adding new AI-powered capabilities to Salesforce Service Cloud is one of my favorites. Salesforce has long been a pioneer in the AI space and the future holds even more exciting AI-driven developments for Service Cloud.

Your first memorable experience in your Salesforce career

My first time on the main Dreamforce stage was as a partner, where I was meant to silently give a demo on behalf of one of our customer Trailblazers. Marc kept asking me all these great questions… not realizing that I didn’t have a microphone to give him answers!

Thank you, Ryan! That was fun and we hope to see you back on AiThority.com soon.

[To share your insights with us, please write to sghosh@martechseries.com]

• About Ryan
• About Salesforce

About Ryan

Ryan Nichols is the EVP & GM, Service Cloud at Salesforce

About Salesforce

Salesforce empowers companies of every size and industry to connect with their customers through the power of AI + data + CRM.

The post AiThority Interview with Ryan Nichols, EVP & GM, Service Cloud at Salesforce appeared first on AiThority.

It is estimated that by 2026, over 80% of organizations will have used generative AI APIs, or deployed generative AI-enabled applications. Generative AI is a powerful tool and by integrating it alongside other technologies, such as other AI and automation functions, businesses will see valuable outcomes, but what does it mean for the customer experience (CX) landscape? While some may fear that AI systems are on the brink of replacing Customer Service Agents, a paradigm shift from agents to AI-enabled agents is not about replacement, but rather, augmentation.

Riding on the Generative AI Hype, CDP Needs a New Definition in 2024

As with other forms of AI, such as self-driving cars which so far have amounted to little more than advancements in intelligent cruise control, AI in the contact center isn’t set to remove the human element, but instead, help them to focus on it. Generative AI specifically is supercharging agents, equipping them with powerful tools that enhance their capabilities, and plays a role before, during, and after customer interactions. Despite the growth of social and digital channels, which will also be strongly supported by generative AI, voice interactions remain popular and are an area that will be heavily influenced by the integration of generative AI to support more established AI and automation tools already in use.

To realize the benefit of how generative AI can support voice interactions, organizations need to focus on how it can play a role at every stage.

Before the Call: Pre-Call Proactivity for Customer Service Agents

AI in the contact centre starts to add value as soon as the customer initiates an interaction. Rather than spending time listening to repetitive, outdated, and poor-quality music while waiting, AI-powered interactive voice responses (IVRs) can capture customer data such as intent, demographic information, and geographic location to steer the customer to the most appropriate available agent.

Biggest AI Trends Transforming the Customer Service Industry (And, How You Can Prepare for the…

The captured information can be used on its own or can be integrated into a customer data layer such as a customer data platform (CDP) to identify previous interactions. This process within the IVR streamlines the call, ensuring that the customer is connected to the most appropriate available agent. At the same time, when the customer is connected to an agent, the agent will presented with information given by the customer and curated by generative AI to reduce average handling time (AHT) as the customer’s details are already located.

During the Call: Real-time insights

During the call, natural language processing (NLP) listens to the interaction and through its speech-to-text capabilities provides a transcription of the call to the AI. This in turn generates knowledge articles to help guide the interaction. The knowledge articles will provide information on screen with suggested answers and useful sources that can help with the customer’s query in real-time reducing the need for Customer Service Agents to take time searching for the same information.

The generative AI outputs presented to the agent can draw reference from multiple pre-approved data sources from the organization, which helps to expand the agent’s own knowledge and reduces the time needed to train new agents. Automatic article sourcing and summarisation can further reduce the AHT as it eliminates the need for agents to look up information related to a customer’s query. Data transparency is vital to ensure agents are delivering the correct information. As such, it is important that agents can easily click through to find the source of the output to reduce hallucinations and increase accuracy.

Five Things You Should Do to Ace your Customer Service Strategy with Artificial Intelligence

After the Call: Write off the Wrap-up

Customer Service Agents can spend up to 60% of their time in the post-interaction wrap-up, performing tasks such as updating CDP systems and creating a summary of the interaction. Generative AI can assist agents in this stage by transcribing, word-for-word, the entire call interaction using NLP. It can then use the transcription to create a summary of the call, create post-interaction reports automatically, analyze the sentiment and intent of the interaction, and even populate complaint forms and CDPs with relevant information. The agent is still involved in the process, but it saves the agent time as they only have to check the information is correct, and can compare the AI-generated data to the generated transcript as a source.

If generative AI could remove a third of the post-call processing time for each agent, this would amount to the equivalent of a 50% increase in headcount, without any added budget constraints or the need to train new staff. The extra time would also be used where it matters: serving the public and building trust in human interactions.

Efficiency Gains and Improved Experiences

Although AI, and specifically generative AI, is in its infancy, its impact will transform several industries. Within the CX space, leaders are already looking at how it can benefit their customers, agents, and organizations. Ultimately, as we’ve explored, it will be implemented across all stages of interactions and improve efficiencies. As a result, both customer and agent experiences will improve as AI takes care of typically admin-heavy tasks, that are vital to organizations that need to audit and monitor calls.

As we move into 2024, a responsible approach to generative AI will reap lucrative benefits and will be a top priority for all CX teams.

Customer Service vs. Sales: Who Uses AI More?

[To share your insights with us, please write to sghosh@martechseries.com]

The post The Role of AI in Super-empowering Customer Service Agents appeared first on AiThority.