Category Archives: AI

AI is predicted to take 20 percent of jobs by 2050 – here’s the biggest challenges companies face when implementing it into their business. 

From automating tasks and speeding up burdensome processes, through to ground breaking advances in diagnostics and smarter decision making – the opportunities are endless.
Experts predict AI may take one fifth of jobs by 2050, and not just jobs which are often classed as ‘unskilled’.
For businesses, AI offers real opportunities in their existing field and the scope to expand into new areas by building new machine learning models as diverse as creating content to predicting customer buying habits.
But it’s not just a question of ‘adding some AI’ into your business.
One of the biggest experts in this area is Laurence Moroney, the lead Artificial Intelligence Advocate for Google.
He’s now one of the worlds best AI Speakers and travels the globe to share solutions to the biggest challenges companies face when implementing AI – and how they can navigate them
1.        Don’t get caught in the hype.
Laurence says it is important for businesses to think strategically about how AI can work in their area, instead of rushing in with the latest fad.
He says: “It’s hard to go onto social media nowadays without some tech bros out there showing you how, with a simple prompt, you can turn your business around, or you can make a whole new business, or you can become a gazillionaire using GPT. There’s also a negative hype where it’s going to destroy your business, it’s going to destroy your economy, it’s going to take over the world. And I think first and foremost is to try to figure out the signal amongst all of that noise.”
2.      Understand the technology
Using AI in your business ‘requires a deep understanding of the technology’, Laurence believes
He says:. ‘It’s about education and trial and error and working with the technology, and understanding the technology is the first part of that. It’s my belief that you really need to go deep with the technology to understand exactly how to apply it for your business and your domain specific knowledge, how you can apply that along with AI technology, to come up with new and unique solutions, or better solutions, for what you already do.’
3.      AI requires a different mindset to traditional computer engineering
Laurence says: ‘AI isn’t about writing code that’s going to execute an action directly, it’s about training a model which then makes predictions based on input data,’ Moroney explains. ‘It’s a very different mindset and your staff need to be equipped to understand that and to trust that process, but it’s not an insurmountable challenge.’
4.      Fear amongst staff that they will be replaced
Laurence says: “People are worried that their jobs will be replaced by AI. ‘But staff within your business will begin to realise that AI will actually make them more powerful.’ That understanding comes with a caveat. ‘It’s managing that process that is going to be a challenge. There’s a great opportunity there, and the advice that I would give is to invest in those skills and invest in people who understand your business as well as these skills.’
‘Artificial intelligence will shape the future of work, making our existing staff much more efficient and allowing us to drive skilled people up the value chain. If you think about job roles that you have where there are tasks that are inefficient, there’s definitely low-hanging fruit there for AI to be a part of that.’

5 Ways GenAI Powers The Travel Experiences Of The Future

Written by Matthew Biboud-Lubeck, Vice President EMEA, Amperity

The digital world has permanently changed the relationship between travel brands and their customers. Travellers are providing brands with more and more data in exchange for unique, personalised experiences. Yet despite this, the traditional loyalty program model appears to be failing to meet the demands of the next generation of travellers.

As McKinsey points out, reaching the top tier of a loyalty program, traditionally, was a facet of many travellers’ personal identities. Now, many loyalty program members now seem more inclined to play the field. According to its 2023 survey on travel loyalty, younger generations are more likely to consider and transact with multiple travel players. Gen Zers and millennials consider about 1.7 times as many brands as do baby boomers and the Silent Generation and transact with about 1.3 times as many brands.

Faced with fierce competition and a rapidly changing landscape, travel brands must innovate and win back their customers’ allegiances – or risk getting left behind at the terminal.


The game is changing for travel loyalty

For many travellers today, loyalty means much more than having a membership number or collecting points. To reap the most benefits from a loyalty program, members often feel obligated to commit to one brand. The reality of their lives and personal preferences are often very different.

Going all in with a brand makes sense for a traditional road warrior or a family whose primary vacation preference is going to Disneyland Paris four times a year. But those types of behaviours are increasingly becoming outliers. A majority of travellers want something different – different experiences for different travel occasions with different travel companions in different types of destinations.

Consumers’ expectations for loyalty are changing in a number of ways. They now expect brands to streamline reward interactions, allowing them to earn and redeem rewards quickly and easily, while also offering a variety of reward options to choose from – not to mention exclusive, differentiated benefits in exchange for their spending.


Loyalty that takes flight

For a peek into the balance travel brands are trying to strike, look no further than the recent updates to airline loyalty programs. These changes intend to recalibrate rewards so that elite members will feel truly special. But the flipside of that choice may be that lower-tier members feel devalued, and therefore, may not be as loyal to the brand.

Some experts predict that the future of loyalty might not look like an allegiance to a single brand at all. Instead, it will look more closely like a ‘choose your benefit’ or an à la carte service, offering flexibility as a perk. Loyalty members who traditionally remained loyal to one carrier in efforts to gain status and earn upgrades along with other perks may no longer see a reason to spend thousands of dollars to reach the “exclusive” next tier.

This seems to be the case with many younger travellers. A recent Morning Consult study found that just under half (46%) of Gen Z travellers said that it was “absolutely certain” or “very likely” they would patronise hotel brands in whose loyalty programs they were already enrolled. Moreover, 33 per cent said that they don’t trust these brands, despite being members of the programs.

These trends underscore the opportunity for travel brands to benefit by taking an open-minded approach to customer acquisition and retention. This would see them focus less on increasing loyalty membership for its own sake and more on earning travellers’ trust and winning their loyalty on a more personal level.

Loyalty programs are paying more attention to the less frequent leisure traveller, which requires staying top of mind through lifestyle marketing. Members still expect miles and points, but they also want recognition and experiences. This requires an intimate understanding of who they are.


Out with old and in with the AI-powered new

The old way of attempting personalisation was just “guessing” at it. Brands would make broad assumptions. For example, if guests were in one demographic, then they might like what a brand has to offer other people in that cohort. In practice today, personalisation means building a customer data strategy based on a unified, cohesive view of every traveller who comes through their purchasing funnel.

By leveraging new AI-powered technologies, travel brands will be able to create clear, holistic customer data strategies that enable them to build more direct, personal relationships with all of their customers, including those who are already in their loyalty programs and otherwise. Successful efforts to do so will lead to more bookings, additional cross-selling opportunities and higher lifetime value.

Of course, leveraging AI is relatively simple in concept but much more complex in execution. AI is only as good as the data it’s learning from. And it’s only as useful as the decision-making power that it’s given. The vast majority of consumers have accepted the fact that by virtue of being online, they are giving up personal data. The flip side is that now they expect companies to use that data to help improve their experience. The rise of AI in the public consciousness has only accelerated these preferences.


Travellers to brands: “Do whatever it takes to make my trip better”

Travellers no longer fear AI. They want companies to work faster and smarter to use it to the customer’s advantage. According to a 2022 survey of travellers worldwide, nearly 75 per cent said they were either “very” or “somewhat” interested in AI that would analyse their data as a means to provide more personalised offers and customer service.

Among those, approximately 30 per cent said they’re happy with whatever it takes to make their trip better. Perhaps more tellingly, about 45 per cent said they were interested, but with the caveat that they are given the opportunity to consent for its use with the explicit purpose of using that data to present better offers and advertisements or provide more personalised service.

Personalisation must be a holistic experience throughout the entire customer journey from online booking to customer service. However, the siloed nature of data and the lack of trust in its accuracy make it challenging to provide seamless personalisation at each touchpoint. Bridging these gaps requires a comprehensive understanding of the customer journey and the ability to infuse data into the personalisation process.


AI to replace the white-glove treatment

The introduction of AI, in general, and now generative AI, more specifically, has levelled the playing field for travel and hospitality companies. In the pre-internet days, the highest levels of service came from ultra-luxury, up-market brands that could afford to dedicate personal assistance for every individual need – the white-glove treatment, if you will.

Now, at every level of hospitality, from budget to 7-star, brands can communicate on an individual level in ways that their customers feel most comfortable. That’s not only creating better traveller satisfaction, it’s driving innovation faster across the entire industry. As our Head of Generative AI at Amperity Joyce Gordon says, “Brands with a good data foundation will be able to use generative AI and create personalised experiences that will quickly become ubiquitous, and they’ll shape customer expectations.”

She believes we’re going to see a lot of rapid innovation in the GenAI space over the next two years that is likely even faster than previous paradigm shifts we saw with internet, e-commerce and mobile adoption.

In practice, individual travellers don’t understand or experience the health of a company’s data program. They care about:

  • finding the right information at the right time as they plan their travels
  • enjoying a seamless travel experience in the moment

And they want to be appreciated by companies they’ve patronised after a stay and in between trips. By using all the data they have at their disposal, especially first-party data, brands have the opportunity to build direct relationships with a much wider base of customers.


“Brands can offer experiences that feel authentic if they use your first-party data,” says Gordon. “For instance, if a brand has purchased all of this third-party data and I’ve never been to their site and now suddenly they know all of these things about me, that feels creepy.

“It’s like the person you go on a date with who has stalked all of your social media. But if I’ve shared this information with you in the past – for bookings and for experiences where I know that information has been used – and if you use it well, it’s almost a relief.”


GenAI: Better data means better results

The conversational capabilities that GenAI enables will be an important game-changer. It’s more natural to provide preferences in a back-and-forth dialogue than checking a bunch of boxes or filling out one-way, predetermined form fields. That said, Gordon warns that the No. 1 issue holding brands back in deploying generative AI-powered agents is in fact that data foundation. A virtual agent powered by ChatGPT might seem cool, but if it doesn’t have any knowledge of the customer at the outset of a conversation, it’s going to feel robotic.

“Better data means better results in the world of generative AI,” Gordon says. “If you’re in a conversation with a chatbot, it’s actually more frustrating if it feels like you’re talking to a human, but it doesn’t have any of the personalisation that a real travel agent would be able to provide.”

In addition to customer service chatbots, which are the most common uses of generative AI today, below are several ways that generative AI will support the travel experience of the future.


5 Ways GenAI Will Support Future Travel Experiences

  1. Personalised booking: Given a generative AI-powered chatbot assistant vs. an open-ended search bar or filtering tool, travellers can react conversationally to suggestions, feeding more data back to the booking engine to create better and more specific recommendations.
  2. Ancillary offerings: Suggest the right add-ons based on real-time interactions and past preferences. Travelling by yourself? Here’s a deal including a massage. Going with your three kids? Pre-purchase those flight snacks.
  3. Automated creative generation: Of all the possible images and descriptive information a brand has on file, generative AI can respond to interactions with the user and serve up the options which will resonate best.
  4. Customer insights and recommendations: Understand patterns of actions customers typically take and give real-time recommendations for when customers want to redeem loyalty points, upgrade, etc.
  5. Simplified technology: The modes of how brands ask questions of their data itself will radically change, becoming more natural language-driven vs. code-based.


The future of travel is here and it’s personalised

Whether or not they admit it – or fully understand what’s happening – travellers are craving personalised, curated information at every turn. In the world of predictive search, social media feed algorithms, e-commerce recommendation engines and now GenAI, the bar has been set extremely high for travel companies to step up and provide similar levels of service to meet the demands of their customers.

The good news for travel brands is that their customers are already willingly sharing detailed information about themselves. No matter how much a person regularly shops at a pet food store, they’re not offering up intimate information about their day-to-day lives from work to family to individual wants and needs in the same way they will to enjoy a travel experience.

With the tools available to aggregate and activate traveller information into a CDP (customer data platform), travel brands can create truly personalised relationships with their customers today. Getting started is easier than it seems. Because there’s so much data out there, there’s a temptation to think big and try to boil the ocean. By starting with what they have, travel companies will realise they have a lot to work with.


They can use customer data to make better business decisions, such as:

  • Optimising their pricing strategies
  • Developing new products and services
  • Targeting their marketing campaigns more effectively


This can lead to increased revenue and profitability.

The most exciting part of building a strong customer data strategy today is its potential for tomorrow. The physical and digital worlds are continuing to blur, and the ability to connect data intake and analysis to reflect this reality will give travel brands a significant advantage in their ability to serve their customers of the future.


Optimise your customer data initiatives to achieve the goals below:

  • Build out first-party data collection. Expanding privacy restrictions are making it harder to use data provided by third parties, which comprise a majority of the customer data travel brands have today. Brands that evolve their data strategies and focus on first-party data will have a huge opportunity to provide better service using information they’ve received directly from the customer.
  • Reach beyond loyalty members. Combining data from loyalty programs with other sources helps to better understand each customer, effectively repositioning “loyalty” around the person instead of the program. By targeting customers with the greatest potential value – in the moment and over a lifetime, regardless of status – brands can start to formulate a more accurate foundation for their customer data and personalisation strategies.
  • Build a unified view of the traveller. Brands now have the ability to utilise platform technology that breaks down silos and collates data together into a unified view of each customer, which will enable them to develop detailed customer profiles and support personalisation efforts.
  • Drive personalisation to every customer touchpoint. There is a huge opportunity to use customer data to customise experiences for travellers throughout their journeys – and today’s consumers are clamouring for this. The next frontier will be using AI to learn what customers want and need without asking.

About the author

Matthew Biboud-Lubeck, Vice President EMEA, Amperity

Matthew is the vice president of EMEA where he is responsible for the commercial expansion of Amperity, a leading customer data platform trusted by brands like Reckitt, Under Armour and Wyndham Hotels & Resorts. Lubeck joined Amperity in 2017 to help launch the company and has served in a number of key roles building sales, customer success, and marketing functions. Matthew established Amperity’s LGBTQ employee resource group (ERG) and is a trusted advisor and customer-centricity change agent to the C-suite across leading consumer brands.

Prior to Amperity, Lubeck spent 10 years with global beauty conglomerates Estee Lauder Group and L’Oréal as Group Head of Customer Data Strategy and Analytics, leading 30 brands across luxury, mass and salon professional divisions to better use data & unlock incredible beauty experiences, establishing L’Oreal as an industry leader. He resides in London with his husband and young daughter.


About Amperity

Amperity delivers the data confidence brands need to unlock growth by truly knowing their customers. With Amperity, brands can build a first-party data foundation to fuel customer acquisition and retention, personalise experiences that build loyalty, and manage privacy compliance. Using patented AI and ML methods, Amperity stitches together all customer interactions to build a unified view that seamlessly connects to marketing and technology tools. More than 400 brands worldwide rely on Amperity to turn data into business value, including Alaska Airlines, DICK’S Sporting Goods, Endeavour Drinks, Planet Fitness, Seattle Sounders FC, Under Armour and Wyndham Hotels & Resorts. For more information, visit or follow us on Linkedin, X, Facebook and Instagram.


Gcore Unveils Inference at the Edge – Bringing AI Applications Closer to End Users for Seamless Real-Time Performance

New AI solution enables fast, secure, and cost-effective deployment of pre-trained machine learning models globally, at the edge

Gcore, the global edge AI, cloud, network, and security solutions provider, today announced the launch of Gcore Inference at the Edge, a breakthrough solution that provides ultra-low latency experiences for AI applications. This innovative solution enables the distributed deployment of pre-trained machine learning (ML) models to edge inference nodes, ensuring seamless, real-time inference.

Gcore Inference at the Edge empowers businesses across diverse industries—including automotive, manufacturing, retail, and technology—with cost-effective, scalable, and secure AI model deployment. Use cases such as generative AI, object recognition, real-time behavioural analysis, virtual assistants, and production monitoring can now be rapidly realised on a global scale.

Gcore Inference at the Edge runs on Gcore’s extensive global network of 180+ edge nodes, all interconnected by Gcore’s sophisticated low-latency smart routing technology. Each high-performance node sits at the edge of the Gcore network, strategically placing servers close to end users. Inference at the Edge runs on NVIDIA L40S GPUs, the market-leading chip designed specifically for AI inference. When a user sends a request, an edge node determines the route to the nearest available inference region with the lowest latency, achieving a typical response time of under 30 ms.

The new solution supports a wide range of fundamental ML and custom models. Available open-source foundation models in the Gcore ML Model Hub include LLaMA Pro 8B, Mistral 7B, and Stable-Diffusion XL. Models can be selected and trained agnostically to suit any use case, before distributing them globally to Gcore Inference at the Edge nodes. This addresses a significant challenge faced by development teams where AI models are typically run on the same servers they were trained on, resulting in poor performance.

Benefits of Gcore Inference at the Edge include:

  • Cost-effective deployment: A flexible pricing structure ensures customers only pay for the resources they use.
  • Inbuilt DDoS protection: ML endpoints are automatically protected from DDoS attacks through Gcore’s infrastructure.
  • Outstanding data privacy and security: The solution features built-in compliance with GDPR, PCI DSS, and ISO/IEC 27001 standards.
  • Model autoscaling: Autoscaling is available to handle load spikes, so a model is always ready to support peak demand and unexpected surges.
  • Unlimited object storage: Scalable S3-compatible cloud storage that grows with evolving model needs.

Andre Reitenbach, CEO at Gcore comments: “Gcore Inference at the Edge empowers customers to focus on getting their machine learning models trained, rather than worrying about the costs, skills, and infrastructure required to deploy AI applications globally. At Gcore, we believe the edge is where the best performance and end-user experiences are achieved, and that is why we are continuously innovating to ensure every customer receives unparalleled scale and performance. Gcore Inference at the Edge delivers all the power with none of the headache, providing a modern, effective, and efficient AI inference experience.”


86% of UK people managers believe AI tools can improve their role effectiveness, Visier research finds

New research from Visier reveals the top factors shaping managers’ approach to decision-making in the workplace—and opportunities to incorporate generative AI tools to improve outcomes and bolster manager effectiveness.

Visier, the globally recognised leader in people analytics and workforce solutions for people-powered business, today unveiled new global research that serves as a plea for help from managers who see major insight gaps in what they need to do their jobs well – and points to enthusiasm for AI as a key solution.

The research shows that AI is seen as a net-positive addition to the flow of work with an overwhelming 86% of UK (87% overall) respondents expressing excitement about the potential of generative AI tools to improve their role as people managers. Managers also expressed an overarching desire to make better, data-informed people decisions with 94% of UK respondents (96% overall) agreeing that improved access to people-related data would lend more confidence to their processes.

“Nearly half of the survey respondents said their current decision making process is greatly influenced by intuition and practical experience, rather than hard facts,” said Ben Harris, Vice President EMEA, Visier. “Many managers also said that accessing data is time consuming and difficult, so relying on instinct is an everyday reality for most.

“It’s recognised that people managers in every sector are under severe pressure, and bearing in mind organisations are sitting on vast amounts of useful data, creating access to it – especially for those who aren’t data analysts – will help them with decision-making accuracy and align to overall organisational strategy. Through this research, managers said that better people-related data could inform their decisions on hiring, promotions, engagement, productivity and efficiency.”

As part of the research, Visier surveyed over 750 people managers in the UK, U.S., Germany, Netherlands and Switzerland. The results revealed a clear belief in leveraging generative AI-powered tools to become better leaders.

Other key findings from Visier’s research include:

  • Adoption of AI is already underway with 52% of UK respondents saying they have already used generative AI tools to support their role as a people manager. This compares to 64% of overall respondents. Additionally, 59% of UK respondents already use it to help write performance reviews (in comparison to 49% globally). Likewise, 93% of UK respondents (81% overall) said if they had access to a generative AI tool that provided their desired use cases, 41% would use it multiple times a week.

  • Time savings created by AI would flow back to the team with 36% saying they’d direct their energies to team planning (UK 31%), and 30% on coaching their team. In the UK, the second highest choice was ‘working on personal learning and development’ (29%).

  • Trust is, however, a challenge, highlighting that employers and employees want to work with tools recognised as safe and secure. Thirty-three percent of UK respondents have concerns about data privacy and security. Another 33% of UK respondents said their organisations don’t allow the use of generative AI tools, while 15% said they don’t trust generative AI tools.

“Generative AI has the ability to democratise access, providing deep insights within the flow of work, and without the need to become proficient in analytics tools,” said Keith Bigelow, chief product officer, Visier. “But its benefits extend beyond the delivery of data to act as a personal coach to every employee, nudging them to ever higher performance, delivering real-time insights in a way that just hasn’t been possible or scalable to date.”

He continued: “Managers’ approaches to decision-making should always involve a balance of data-driven insights and experience. The absence of either harms the organisation’s ability to align to strategy and ultimately maintain a competitive edge.”

For more information on Visier, visit: To learn more about Vee, Visier’s AI digital assistant, visit:

About Visier

Visier is the recognised global leader in people analytics, providing on-demand answers to people-powered businesses. At the core of Visier’s innovations is a simple premise: People impact is business impact and that’s why Visier provides the tools and insights organisations need to drive productivity, performance, and business outcomes through their people. Founded in 2010 by the pioneers of business intelligence, Visier has over 50,000 customers in 75 countries—including enterprises like BASF, Panasonic, Experian, Amgen, eBay, Ford Motor Company, and more. Visier is headquartered in Vancouver, BC with offices and team members worldwide. To learn more about Visier, visit

Strategic Leadership Expanded with New Chief of Staff Role at digital learning firm

The new role sees Europe’s fastest-growing digital learning provider, GoodHabitz, further committing to its AI and technology journey

In an innovative move, digital learning provider, GoodHabitz, has created a new role for a member of its Engineering Leadership team. Israel Roldán has become the company’s Chief of Staff, cementing the firm’s long-term strategic commitment to operational effectiveness.

The new role’s purpose is to foster collaboration and drive strategic initiatives across the company, including focusing on AI and data. Roldán is an expert in engineering management, web architecture, software development, strategic communications and product development.

Key to Roldán’s new role is advising the GoodHabitz leadership team while also ‘seeing around corners’. He will leverage data and insights to spot opportunities to boost workflow, teamwork and strategic outcomes. He will also set benchmarks and identify challenges early to brainstorm solutions with internal experts. Communication is also paramount. Roldán will be responsible for ensuring the company’s 380+ strong team working across more than 20 countries understand its direction, challenges and opportunities, helping them move forward together and talk with a united voice.


Israel Roldán, Chief of Staff at GoodHabitz, explains: “In the past two years, we’ve expanded our strategic initiatives across the company, enhancing our product offerings and customer engagement. Moving from the Engineering Leadership Team to Chief of Staff my role is to ensure initiatives are implemented effectively across all departments, supporting our overarching goal of driving organisations forward by empowering their employees with personal, high-impact learning journeys.

“One of our company’s key objectives is to help our people – and our clients’ people – to develop. It’s what all our work focuses on. Pivotal to this position, and engineering roles generally, is to connect seemingly disconnected efforts and initiatives to better move towards organisational goals. Communication with the directors and facilitating collaboration across the company is central to this.”


Chief of Staff roles are more common in the United States, associated with the government, military, and increasingly, corporate organisations. According to GoodHabitz, the role needs a strategic mindset to transform strategy to execution, managing stakeholders along the way. With so many organisations’ processes relying on tech and AI, Roldán believes that many more organisations may tap into their Product Engineering teams as a pool for strategic talent development, as engineering is all about solving problems with the resources at hand.


Roldán, personally, has always wanted to move beyond engineering towards product development and strategy as a whole. “I’m genuinely excited about the opportunities ahead. Technology isn’t merely a tool for innovation; it serves as a catalyst for enhancing operations across all departments.

“As Chief of Staff, my role is to orchestrate our efforts to ensure seamless alignment with our overarching strategic goals. This coming year I’m particularly focused on advancing our adoption of AI-powered features and strengthening our data capabilities for more informed decision-making and for the benefit of our students. I look forward to reflecting on our progress and sharing insights from this journey in future collaborations.”


About GoodHabitz

GoodHabitz believes it’s people who drive companies forward. That’s why personal development is key for every organisation.

GoodHabitz offers digital learning experiences that make personal development work for everyone. Its engaging content and unique approach are designed to move people, teams and your entire organisation forward.

GoodHabitz was founded in The Netherlands and is now active in more than 20 countries worldwide, serving over 2,700 enterprises and SME clients in a wide variety of industries. Read more about us at

Over a third of businesses unprepared for AI

New analysis from Fasthosts reveals that more than a third (35 per cent) of businesses polled are not ready to use AI and other advanced technologies because of limitations in their existing IT infrastructure.

In today’s business landscape where a robust IT infrastructure is paramount, businesses must not underestimate the importance of creating an AI-ready environment. Yet, many are being held back by a lack of understanding and inadequate computing capacity within their current infrastructure.

In fact, 60 per cent of polled businesses said that their understanding of technologies such as AI is non-existent or basic. Additionally, over half (61 per cent) of respondents noted that they are yet to evaluate how AI-ready their infrastructure is.

Failing to prepare or even consider creating AI-friendly IT environments can put businesses at risk. It can leave them trailing behind those who are implementing AI quickly and efficiently, as well as missing efficiency opportunities and facing increasing long-term IT costs.

Justin Bateman, Senior Product Manager at Fasthosts, said, “It’s alarming that more than half of businesses are unsure of limitations in their current IT setup that could hinder the adoption of technologies like AI. By not evaluating this, businesses are depriving themselves of an AI-ready infrastructure that offers cost-effective scalability, data management and increased control over their IT environment.

“For businesses that understand these technologies, they need to create an AI game plan and act. And for those that are hesitant or confused, it’s time to invest in a partner and technology that bridges both the knowledge and infrastructure gap.”

New privacy research pegs AI as a rival threat to cybercrime

  • More than half of developers believe AI will almost equal Cybercrime in terms of risk to data privacy
  • Developers concerned about current regulatory frameworks, with 98% advocating for proactive measures to address future data privacy concerns


21st May 2024: New research* released today reveals the extent of concern regarding the future threat posed by AI and Machine learning to our privacy.

Cybercrime is still seen as the main threat with 55%, but AI comes in close second at 53%.  Despite AI being a relatively new menace, the research shows that developers believe the technology is a threat that is rapidly catching up with cybercrime, as it becomes more mainstream. The cost of cybercrime is projected to reach $13.82 trillion by 2028: the reality is that with increasingly sophisticated AI potentially in the hands of a new generation of cybercriminals, this cost could grow exponentially.

The study, commissioned by Zama – a Paris-based deep tech cryptography firm specialising in the world of Fully Homomorphic Encryption (FHE)* – surveyed developers across both the UK and US.

During the research, more than 1000 UK and US Developers were asked their opinions on the subject of privacy, to uncover insight from the people that build privacy protection into everyday applications.  The research revealed developers’ own perceptions and relationship with privacy, delving into subjects such as , what privacy considerations should be at the centre of evolving innovation frameworks, who holds the ultimate ownership of privacy and what their opinion is on the approach to regulation.


In addition to the findings revealing significant concerns about AI’s threat, the research also reveals that 98% of developers believe that steps need to be taken now to address future privacy and regulation framework concerns.  72% also said that regulations made to protect privacy are not built for the future with 56% believing that dynamic regulatory structures – which are meant to be adaptable to tech advancements – could pose an actual threat.

“Despite cybercrime expected to surge in the next few years to the cost of trillions, 55% of developers we surveyed in our research stated that they feel cybercrime is only ‘marginally more of an issue’ than the threat to privacy that AI will pose. We have seen from our work that many developers are the real champions of privacy in organisations and the fact that they have some legitimate concerns about the privacy of our data, in relation to the surge in AI adoption, is a real worry,” says Pascal Palier, CTO and Co-founder of of Zama.

“Zama shares the concerns expressed by developers about the privacy risks posed by AI and its potential irresponsible use. Regulators and policymakers should take this insight into consideration as they try to navigate this new world. It’s important not to underestimate the very real threat highlighted by the experts who are thinking about protecting privacy every day, and make sure upcoming regulations address the increased risks to users’ privacy,” he added.


The survey went on to reveal that 30% of developers believe that those behind making the regulations are not as knowledgeable as they could be about all the technologies that should be taken into consideration, also presents a real danger, while 17% believe this would pose a possible threat to future tech advancements.


“It’s undoubtedly an exciting time for innovation, especially with AI advancements developing as fast as they have. But with every new development, privacy must be at the centre; it’s the only way to ensure the data that powers new innovative use cases is protected. Developers know this,  embracing the vision championed by Zama in which they have the ability and responsibility of safeguarding the privacy of their users. It’s clear, in analysing their insights, that they would like to see regulators taking more responsibility for understanding how Privacy Enhancing Technologies can be used to ensure privacy of use for even the newest of innovations, including Gen AI. Advanced encryption technology such as FHE can play a positive role in ensuring innovation can still flourish, while protecting privacy at the same time,” he adds.


*FHE, Fully Homomorphic Encryption

FHE is an encryption technique that enables processing data without decrypting it. With data encrypted both in transit and during processing, everything you do online could be encrypted end-to-end, allowing companies and organisations to offer their services without ever seeing their users’ data — and users will never notice a difference in functionality.

The research was carried out by Research Without Barriers (RWB) between 9th January 2024 and 8th February 2024 with a sample comprising 1,098 Developers from the UK (571) & USA (527).

About Zama

Zama is a cryptography company building open-source homomorphic encryption solutions for blockchain and AI. Their technology enables a broad range of privacy-preserving use cases, from confidential smart contracts to encrypted machine learning and privacy-preserving cloud applications. Zama was founded by Pascal Paillier and Rand Hindi, and currently has the largest research team in homomorphic encryption.

Since it was founded in 2020, Zama has established itself as the main actor shaping the FHE market, having already made significant contributions to the field of data privacy and encryption, including 17+ filed patent families, $100 million in secured deals and the successful delivery of four innovative products/solutions to the market.



Amperity Redefines Composability with the World’s First Lakehouse CDP

Data teams can now stop integrating and start sharing data across their tech stack to save time and lower costs.

Tuesday, May 21, 2024Amperity, the leading AI-powered enterprise customer data platform (CDP) for consumer brands, today announces a new composable approach for customer data management: the Lakehouse CDP. Now, brands can seamlessly share live data sets between a CDP and a lakehouse without maintaining ETLs or copying data. IT teams can optimize how data is stored and processed with any platform that uses lakehouses’ open table formats to save time and lower costs. This composable and more secure flow of data ensures brands can fuel the data-intensive demands of Generative AI and 1:1 personalization with high-quality data.


Amperity’s Lakehouse CDP addresses two critical concerns that composable CDPs often overlook: data quality and governance.

Other composable solutions fall short because they are built as ‘reverse ETL’ tools that activate data from a big data environment. Unfortunately, lakehouse data still needs to be cleaned, unified and aggregated into insights for business users. The CDP components available today lack an AI-driven approach to resolving identities and governing the data operations of constantly changing profiles. Reverse ETLs also require complex query logic to access lakehouse data, resulting in hidden compute costs and ongoing integration work.


“The shift to composability in the marketing technology landscape continues to gain momentum as brands become more sophisticated and strategic in how they manage customer data,” said Juan Mendoza, CEO at The Martech Weekly. “Amperity’s Lakehouse CDP reflects this shift perfectly by enabling the cleaning, enriching and harmonizing of data from often complex and hard-to-use data clouds, while also providing the capabilities to utilize that data to drive growth outcomes for marketers. The worlds of data engineering and marketing operations can come closer than ever thanks to Amperity’s release of the Lakehouse CDP.”


With the Lakehouse CDP, Amperity breaks down silos between SaaS tools and teams to fuel AI and engagement with better data.

Since Amperity’s capabilities are composable, brands can plug it directly into a data lakehouse and use any combination of its offerings to optimize customer data operations. Data teams can improve data quality across Amperity and a lakehouse with identity resolution through AmpID, generative AI with AmpAi, and pre-built data assets with Amp360. Business users can use AmpIQ to securely activate lakehouse data, enabling deep personalization. All components come with data validations and checks to govern end-to-end data workflows across platforms.

“We use Databricks for its unparalleled data tools and ability to seamlessly share data with Amperity,” said Tom Barber, head of data at Virgin Atlantic. “This powerful combination allows us to quickly unify and enrich vast amounts of customer data. By democratizing data access, we empower our non-technical users to easily make data-driven decisions. Amperity has enabled us to maximize the value of our data, enabling us to focus on delivering exceptional travel experiences.”


Introducing Amperity Bridge

To enable the Lakehouse CDP’s core benefits, Amperity is adding a key new feature: Bridge. Amperity Bridge allows users to point and share data to and from a lakehouse rather than using the slower, less secure method of reverse ETL. It uses each lakehouse’s open, industry-standard data formats so that data is available across the tech stack through a shared catalog. This provides the benefits of zero-copy for greater control and compliance without unnecessary network calls and processing. Amperity Bridge is currently available for Databricks and Snowflake.


“In today’s data-driven landscape, brands are struggling to unlock the true potential of their customer data due to the siloed nature of traditional data management tools,” said Barry Padgett, CEO of Amperity. “Amperity’s Lakehouse CDP rides the wave of open data sharing, enabling brands to build cross-platform data workflows. Our goal is to ensure high-quality customer data is available across all platforms that use lakehouse architecture without replication. With Amperity, businesses can meet the data demands of Generative AI and personalization at scale with unparalleled data governance.”


Why Brands Win with a Lakehouse Approach

Amperity’s Lakehouse CDP accelerates time-to-value by enriching data in a lakehouse. With Amperity, brands can:


  • Automate First-Party Identity Resolution. Easily maintain high data quality in your lakehouse. Unify raw customer data and produce a stable, universal identifier with AI-powered ID resolution.
  • Build Data Assets Quickly. Quickly shape data for activation. Leverage pre-built industry and use case data assets that can be easily shared with and enriched in a lakehouse.
  • Sync Enriched Data to Any Tool. Easily access and activate high-quality data from a lakehouse with Amperity’s marketer-friendly tool that’s more secure than reverse ETL.
  • Keep Data Secure and Flowing. Improve data governance by securely sharing data without replication, tracking every transformation and automating consent management workflows.


“The future of customer data management lies in enriching and activating data from a shared lakehouse catalog. This approach enables businesses to securely manage customer data in their preferred platforms and tools without the need for costly and time-consuming data migrations,” said Gerry Murray, research director at IDC. “The result accelerates and expands the use case roadmap, enhances data-driven decision-making and improves downstream application optimization.”

To learn more about Amperity Bridge, check out the demo here.


About Amperity

Amperity, the first Lakehouse CDP, delivers the data confidence brands need to unlock growth by truly knowing their customers. With Amperity, brands can build a first-party data foundation to fuel customer acquisition and retention, personalize experiences that build loyalty, and manage privacy compliance. Using patented AI and ML methods, Amperity stitches together all customer interactions to build a unified view that seamlessly connects to marketing and technology tools. More than 400 brands worldwide rely on Amperity to turn data into business value, including Alaska Airlines, DICK’S Sporting Goods, Endeavour Drinks, Planet Fitness, Seattle Sounders FC, Under Armour and Wyndham Hotels & Resorts. For more information, visit or follow us on Linkedin, X, Facebook and Instagram.

Small Language Models and Vectors Will Bring AI into Reach of Mid-Range Businesses, says Chief Scientist, Aerospike

Naren Narendran says the shift from LLMs to SLMs and vectors is introducing focused AI models that demand less compute power and are more available to users

The cost-prohibitive nature of large language models (LLMs) is prompting a progression towards small language models (SLMs), resulting in solutions that decentralise and democratise their use, says Naren Narendran, Chief Scientist at Aerospike Inc., a real-time database leader. This propelling of the adoption of AI across a broader range of industries makes it accessible to mid-size organisations and enables data-driven hyper-personalised experiences.

While LLMs are exceptional at handling general purpose queries, they require huge amounts of compute and storage and are unaffordable to most companies whose pockets are simply not deep enough. However, the market is shifting more quickly than expected towards SLMs for specific domains with fewer parameters, requiring less processing power.

“LLMs are too large and unfocused for most business applications and are expensive to run, so companies are switching their attention to more economical and specific SLMs,” Narendran says. “In addition, vectors, which came to prominence because of LLMs, are beginning to be used for more classical applications, including predictions, fraud detection and personalisation. It’s no longer necessary to manually collect a set of features from vast amounts of data and feed them into a custom model. Instead a vector can be used to encode features past and present and provide hyper-personalised recommendations. This is expanding the horizons for semantic search in a way we could not have predicted just a few years ago.”

“Vectors have become part of the conversation in 2024 in the same way that LLMs did in 2023, and this is because LLMs can’t do the AI and ML job alone. There are other critical segments, including SLMs and vector databases, that bring together the full complement of tools. This evolution is allowing a new wave of companies, particularly in real-time industries, to adopt technology for split-second, automated decisions. Now they can more fully leverage AI for the business.”


Narendran thinks that this year will see more companies honing their AI strategies and adapting so they can use data in a more personalised way.

“As organisations bump up against the constraints of LLMs, they will try other options. If running ChatGPT is too expensive, they’ll turn to a SLM. Rather than depending on platforms to serve content based on behaviour collected from a user over the last year, they will turn to vectors that can dynamically encode and search on activity from an hour ago,” he said. “The landscape is changing quickly, and the search is on to use AI tools that can deliver business value. We anticipate an innovative year ahead.”


Putting on the AI guard rails: Experts reveal how to minimise risk

 In the ever-evolving marketing landscape, one technology emerges as the potential linchpin – Gen AI.  Joyce Gordon, Head of Generative AI, Amperity, recently joined forces with industry leaders, Rio Longacre, Managing Director at Slalom, and Jon Williams, Global Head of Agency Business Development at AWS. They revealed the key risks and the importance of setting boundaries when implementing a successful AI strategy.

Joyce Gordon, Amperity

When it comes to AI, it’s fair to say that we’re in a paradigm shift that’s similar in magnitude to the evolution from desktop to mobile. As a result, over the next couple years, we’re poised to see new types of products. We’re going to see new business models emerge as costs and cost structures change. And we’re going to see new companies enter into the market. But along with these developments, many regulatory questions across privacy and legal compliance arise.


Generative AI: Risky business?

There’s obviously a lot of excitement and promise surrounding Generative AI, but it’s not without its challenges and risks. Longacre echoes this sentiment, saying, “Nothing is without risk. And Gen AI is no exception.”

He advises all brands to consider the following risks, rules and considerations associated with Gen AI and its usage:

  1. Generative AI needs a lot of content to be trained on. So if any of that content is copyrighted, then that copyright still holds. This means you have to be careful that anything you create is significantly different.
  2. Content created by Gen AI cannot be copyrighted.
  3. Under the new EU Generative AI act, any content needs to be watermarked, so it can be identified as created by Gen AI.
  4. Without keeping a human in the loop, you could open your brand up to reputational risk.
  5. Have the right partners, processes and data foundation to position yourself strongly in this era. If you hold your own customer data and creative assets in one place, you can use them to train your Gen AI on, so you’re not reliant on someone else’s copyrighted content.


“What’s going to be important are the tools you use and the partners you have. Make sure you’re using the right tools – don’t use the free ones. Spend a little more money, do your due diligence and pick ones that have digital watermarking capabilities,” Longacre advises.

“And remember, Gen AI is definitely not without legal risk. However, this is not an insurmountable problem. Partners like AWS have some great tools to help you.”


Williams chimes in, pointing out, “One of the most important things to start from a consideration perspective is making sure that your company-owned content is not being used to improve the base models or being shared with third-party model providers because, otherwise, you become a part of their model. And then, whatever information you provided access to is actually integrated into their capabilities.

“The way we think about that at Amazon is that with Amazon Bedrock, your content is never used to improve the base models and is never shared with third-party models – it’s encrypted at rest and you can actually encrypt the data with your own keys.


AI and reputational safety

When it comes to safety, he cautions that brands should be implementing guard rails. “In terms of your reputational safety, make sure that you’re putting guard rails around the use of Generative AI, making sure your marketing team has the opportunity to define a set of policies to help safeguard Generative AI applications. With Bedrock guard rails, you can configure those policies. You can set a list of denied topics that are undesirable in the context of your application.

“For example, an online banking assistant can be designed to make sure that it refrains from providing investment advice to people that log into that banking assistant. Content filters can make sure you’re filtering harmful content across hate insults etc etc and even coming soon. actually down to the specificity of words.


The other thing to be really careful about, Williams cautions, is PII (personally identifiable information) redaction. “So you can make sure you select a set of PIIs that can be redacted in your generated responses that are coming from your foundational models. In a customer environment, that’s incredibly important.

“The last thing you want to do is have your customers talking to something and it’s providing them with information that it shouldn’t have shared with them. Then, indemnification. So we actually offer uncapped intellectual property indemnity for copyright claims or raising from generative output from Amazon Titan image generator and all of the images generated by it,” he says.

“The Titan image generator also has an invisible watermark that can’t be cropped or pressed out. You can look at the use of the images or the models that you’ve created for the future and make sure that you can track those things accordingly. Those are some of the things that we’re putting into place to help with the security of company’s data but also sort of the reputational risk guard rails that you need to be making sure that you have a strategy for and the tools to be able to implement.”


AI and the human touch

Longacre points out that every use case he shared has a human in the loop. Since we’re in the early days of AI, that’s not surprising as most brands are starting with ‘human in the loop’ use cases. This is where AI generates outputs that a person then approves and potentially refines. ‘Human in the loop’ use cases enable productivity gains while minimising risks arising from hallucinations or unexpected outputs.

“Maybe the copy is being written by Gen AI, but a human reviews it,” Longacre says. “The image might be generated, but it’s not being pushed out into the wild.

“We’re starting to see a little bit of that, but generally, there’s human oversight. Even with chatbots. I mean chatbots have been around forever. Most of them were machine learning based. You need that knowing of, ‘OK, when do you have the escalation? Where do you pass from the chatbot to a live person for certain use cases?’ Identifying that is still super critical.”


Gen AI cost and customer risks

Beyond the legal and reputational risks that Gen AI poses, there’s another risk to consider: customer retention and satisfaction and cost. For example, a couple of months ago, I was trying to book a flight and hotel for a trip. I went through this whole conversation with a chatbot on the booking website. Then, at the end, it wasn’t able to complete the booking.

It had asked me a lot of questions like my preferences, who I was traveling with and all of these other things. These were things it should have already known as I’ve made many bookings with the site before. So, I left feeling frustrated because I wasn’t able to make the booking at all through this experience. It didn’t enhance my discoverability because it didn’t pull in any first-party data.

And back to the cost risk. This is often overlooked. But if you think about something like conversational AI, each time it has to ask the user a question, that’s another request that needs to be made to the LLM API. If this happens once or twice, then no big deal. It costs a fraction of a cent. But at the scale of hundreds of millions of users, this becomes a huge business expense. To avoid this, brands must think about other ways to integrate more first-party data to both create a better customer experience and reduce costs.


Is your company making this common AI mistake?

According to Williams, one major oversight companies often commit during the implementation of AI is neglecting to consider the “what” aspect – specifically, the identification of relevant use cases. Technology is a brilliant enabler, but it’s just one of the tools you can apply to help with real-world complications. So as an organisation, have the executive team work with their teams to identify what the time-consuming, difficult or impossible problems that Generative AI could help solve. Then think small with the day-to-day irritations of either your employees or your customers. What are their ‘paper cuts’ on an everyday basis, and how can you then develop those use cases to address those challenges?


Get very specific with exactly what it is that you are trying to do and how you track that. Also, make sure that you have alignment given. A lot of the way that Generative AI is going to be used effectively is predicated on your technology stack and the data that you have in your organisation. Therefore, making sure that your marketing organisation is talking to your IT organisation is also a critical step to take as a company.


Watch the full webinar here.


About the Author

Joyce Gordon is the Head of Generative AI at Amperity, leading product development and strategy. Previously, Joyce led product development for many of Amperity’s ML and ML Ops investments, including launching Amperity’s predictive models and infrastructure used by many of the world’s top brands.  Joyce joined the company in 2019 following Amperity’s acquisition of Custora where she was a founding member of the product team. She earned a B.A. in Biological Mathematics from the University of Pennsylvania and is an inventor on several pending ML patents.


About Amperity

Amperity delivers the data confidence brands need to unlock growth by truly knowing their customers. With Amperity, brands can build a unified customer profile foundation powered by first-party data to fuel customer acquisition and retention, personalize experiences that build loyalty, and manage privacy compliance. Using patented AI and machine learning methods, Amperity stitches together all customer interactions to build a unified view that seamlessly connects to marketing and technology tools. More than 400 brands worldwide rely on Amperity to turn data into business value, including Alaska Airlines, Brooks Running, Endeavour Drinks, Planet Fitness, Seattle Sounders FC, Under Armour and Wyndham Hotels & Resorts. For more information, please visit or follow us on LinkedIn, Twitter, Facebook and Instagram.