• Artificial intelligence without ethics should be tackled like climate change.
• International co-operation may well be necessary.
• Individual nations may not have what it takes to get the job done.

There has never been a technology that needs good ethics as much as artificial intelligence does. The consequences of getting this moment in techno-history wrong are disastrous. Not, as has been widely publicized, world-ending disastrous, but disastrous in terms of our ongoing understanding of – and striving towards – a more equitable world than we inherited.

In Part 1 of this article, we talked with Richard Foster-Fletcher, Chair of MKAI, and Simon Bain, CEO of web platform OmniIndex, who aim to establish ethics for artificial intelligence, about what “good ethics” might look like for artificial intelligence. 

In Part 2, Richard and Simon explained the potential consequences of failing to give artificial intelligence some ethics, irrespective of the complexity of the process of establishing absolute harms as a basis for what those ethics should look like.

That left us with one fairly massive question.

THQ:

So how do we give our artificial intelligence ethics? Genuine question – it’s out there already, doing a thousand different jobs. How do we teach it to be progressive technology (without unnecessarily overstepping boundaries of ethical difference)?

RF-F:
By next year.

THQ:

Excuse us?

RF-F:

We’ve got to do that by next year, otherwise it’s going to screw with the elections in the US.

THQ:
Oh. Yeah. Everybody that knows about this technology seems to be deeply worried about exactly that. “The AI election,” as they call it.

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Can artificial intelligence have ethics?

SB:
And they’re right to worry. If you look at human nature, it has always been tribal. We’re a tribe group. We like our own tribe. Anybody comes near our tribe, we’ll throw something at them to try and get rid of them.

The problem the internet has caused over the last 20 years is that it’s made us more tribal, with people only reading and viewing within their own grouping. When you have AI pushing more and more of that information across, that gets exacerbated. And this is where I think generative AI can be very damaging.

The only way you can stop that is to make sure that people within politics don’t use the tools to push themselves. But that’s an impossible ask.

AI. Ethics. National politicians. No, that can’t posibly work.

THQ:
Yeah, what’s that line? A lie goes around the world while the truth is still getting its shoes on? In terms of the election and generative AI, if you have entirely believable video footage of something that is still in reality a lie, broadcast to an echo-chambered public operating on concentrated confirmation bias, then you have no chance of fighting against that. You’re relying on, as you say, the people who are highly invested in a specific result to be moral enough human beings to not misuse the technology.

And at that point you’re putting a hell of a lot of faith in human beings.

SB:
You are. But I think that’ll be quite a short term effect – one or two elections – before the majority of people who are sane and who do listen to both sides will suddenly react and come up. That middle ground has always been there, they’ve just been hidden and have been pushed away.

And I think for the next couple of elections that may be the case. Then all of a sudden when people realize that the people on either extreme are using these tools, that centrist ground will say “Hold on a minute, this is wrong, let’s actually go out and do something about it.”

The how of ethics and artificial intelligence.

THQ:

So, while we wait for the great centrist revolution, we come back to the question of how we give our artificial intelligence ethics.

RF-F:
Well, we have great depth of understanding. We don’t have the breadth of ability to combat this yet. The breadth is where you need the different perspectives from around the world and different people to be able to say “Have you asked this question? What would happen if…?”

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What are the risks of artificial intelligence without ethics?

That’s very much doable from the implementation perspective. The deployment perspective of AI is a little more complex in terms of the model building the data, but we can start there. And that means you put together external AI audits, you have multi-stakeholder feedback in there. And I would venture from two sides, one from appointed groups that are very well educated but have diversity across the group, say 10-20 people looking at this.

And then I think you’ve got to go out to hundreds, if not thousands of people and have them look at this and share thoughts that companies will never otherwise access in a million years because of the culture and the way that organizations have to be created.

THQ:
How realistic do we think that is, given companies’ perceived need to not be “unnecessarily” audited or unnecessarily criticized.

The who of ethics and artificial intelligence.

SB:
My own view is that this has to come out of the UN. We have a whole range of agreements in the UN which are bilateral across the world. Whether it be to do with embryonic research, whether it’s to do with nuclear disarmament. There’s a whole range of things that governments agree to. It’s very difficult to get the agreement, but governments do agree to them.

An international power to tackle an international problem? Call the UN!

If we have a similar understanding about AI, and if we put it in the same bracket as other tools such as embryonic research, which then takes it away from a single organization or handful of organizations (because organizations have to make money, first and foremost), we can say “Right, you can’t go beyond this line,” and then make sure that all other organizations audit that as well.

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I think it can be self-managing within guidelines from the UN. It has been done before and I think it can be done again. What I think is really bad are policymakers being driven by individuals and organizations who have their own fixed agenda. We need to make sure that they do not become the sole voice here.

THQ:
So what are the practicalities of how we get it done? If we get it done via the UN, that’s great. But that depends on political buy-in more than anything else to raise that agenda, yes? Meanwhile, the polling shows a majority of Americans are preparing to vote for Donald Trump, who was famous for pulling out of international accords and threatened to pull the US out of NATO, and the UK government has pulled itself out of the EU, is exploring ways to pull itself out of the European Convention on Human Rights, so it can behave in ways that are currently against that convention.

So why do they think they’d be enthusiastic about adding any power to the UN to make rules over anybody operating generative AI?

RF-F:
It’s like smoking really, isn’t it? It’s not clear what the harms are to anybody in the short term by doing this. And technology companies are brilliant at providing this ease and convenience that we love and now sort of depend on.

I think there’s definitely a role, as Simon says, for the UN, because I think there is so much bias from each country and each corporation, too. We aren’t going to see wholesale change until we see a wholesale change in business, because this linear economy can’t carry on. So we know that talking about this in isolation, we do get a bit stumped, but when we talk about it alongside climate change and equality and diversity, you start to see a picture emerging that needs to pull this in for sure, and hopefully will be understood as part of it. 

No artificial intelligence without ethics allowed.

THQ:

Just a gentle reminder – there are plenty of people I both the US and Europe who believe climate change is a hoax, and Net Zero is an unnecessary con on the working class.

RF-F:
One of the key things to recognize is that mental harms and physical harms need to be put on the same page in the same place. We would never have allowed something with the power of ChatGPT be deployed in pharmaceuticals or food or anything like that, but because with generative AI, all the potential harms are mental, we don’t take it anything like as seriously. But the harms are immense. We need to take mental health more seriously and then you start to see the ramifications and you can legislate against them.

THQ:
That’s the point, isn’t it? It’s all intertwined and interfolded with other things and other elements of real life. The idea that we can solve x-part of this, while the rest of it is still a swirling mass of weirdness, suggests we need to go right back to basics and build up from there.

You’re going to tell us that’s too dystopian a view, aren’t you?

SB:
Yeah, it is dystopian. I think we have to go back to history and have a look through the many things that were going to kill the world, whether it be the printing press, the television, the VCR, whatever it happens to be. They’ve all had their time of notoriety, but society has a tendency in the end to manage those things.

Where I would be concerned is to have a national government try to manage it, because a national government is… just not very good at managing things. So I come back to the UN.

It has some power, and then it’s down to individual governments to try and make the system work. This is an international issue, so it needs an international solution. Right now, the best hope we have is the UN.

How the UN works #101.

The post How to mobilize to deliver ethics to artificial intelligence appeared first on TechHQ.

• Unless it’s trained, the ethics of artificial intelligence are a synthesis of the internet.
• The internet was never designed to teach ethics to artificial intelligence.
• Without ethics, the worldwide take-up of artificial intelligence will make inequality stronger and more divisive.

It’s becoming increasingly clear the more we add generative AI into our business systems that artificial intelligence, an entirely machine-based system, needs some code of ethics.

But adding ethics to artificial intelligence is not in any sense as easy as it might sound.

In Part 1 of this article, we talked with Richard Foster-Fletcher, Chair of MKAI, and Simon Bain, CEO of web platform OmniIndex, both of whom are fighting to get this done, about the complexity of thinking we understand what “good ethics” – and especially “bias-free ethics” – might look like.

That’s especially difficult, given that we have to solve for the naturally-grown ethical biases of the people training the AI (most of whom will probably be white, westernized men), as well as the ethics of the company, and a country, and arrive at an ethical model that can be applied worldwide to artificial intelligence technology, which has nothing beyond the internet as a whole, and what we teach it of our concept of ethics, to guide it.

THQ:
We were just talking about the tricky business of moralistic data. As you say, whose morals are we talking about? How certain are we of the ground on which we’re playing here?

RF-F:
Yeah. There are some absolute morals that we can all share, and others we need to be very careful of.

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SB:
I think we need to break it down, too. We have, say, Chat GPT Global, which has all the internet data in it, and as Richard said, that’s probably 60, 70% US-based. But then you’ve got ChatGPT as a private sandbox system for an organization. Now their data is purely for them. There are going to be biases in that data, obviously, but it is a much smaller dataset and therefore is much less likely to have some of those ethical problems, being that it’s based on an organization’s business model.

If they start bringing in external data, then we’ve got problems. I think we need to differentiate what we’re talking about here. Are we talking about AI leading the world and every answer coming specifically from the internet? God forbid, we all know how good Wikipedia is. Or are we talking about artificial intelligence, generative AI in this instance, being a subset of data for an industry to get answers for a specific industry within their organizational data? 

I think we need to be careful to differentiate the cases.

Can artifical intelligence systems cope with ethics?

THQ:

Oh, definitely. But is there also a degree to which the number and the extent of the harms that can be done are lowered simply by the scale crunching down to individual industries, individual companies? Or do we just know there’s something wrong within the system?

SB:
I think the headlines are just saying there’s something wrong within the system.

My own view is that a lot of the hype we’ve been having recently that “AI is going to blow up the world” and all the rest of it is the best marketing and PR pitch I’ve ever seen.

The IT industry is pretty good at PR and marketing, but both OpenAI and Google, through Bard and Microsoft played a blinder when they said AI could blow up the world. Brilliant. That’s got everybody talking about it.

It’s the biggest load of BS I’ve ever heard, but it does have an awful lot of people talking about it, which is exactly what they wanted. Because what you’ve got to remember about the reason Microsoft put so much money into OpenAI and the reason Google has got Bard, is not for the greater good.

It’s to sell you more advertising. And the reason Google came out with Bard so quickly, and I’m pretty certain they didn’t want to, was because Microsoft had a lead on them. When it comes to advertising inside their browser, we have to remember what the uses of those particular tools were.

I mean, Google’s just announced that the Gemini Project, which is actually powered by DeepMind, a true AI application, is used in the National Health Service in the UK as well as elsewhere. But we have to be careful again of what it is we’re actually looking at and why these systems came about in the first place. Because it wasn’t to help us, it was to make revenue.

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THQ:
That’s all absolutely true. But the point is that whatever their initial purpose was, they’ve been taken up and taken across the board and very soon they’re going to be in more or less everything. So they quickly outgrow that initial purpose while still fulfilling it. And so it becomes a bigger thing to deal with.

Also, of course, they’ve got at least 100 years of science fiction to help them in the idea that “the machines are going to kill us.”

So what is the scale of this issue, Richard?

RF-F:
What ChatGPT and the others have done is answer the question, “What would it be like if I could chat with the internet?”

That means the scale of the problem is significant because no matter what the data is doing, there has to be this layer of interpretation attached to that. So you’ve got Google, for example, and somebody types in “CEO” and then presses “Image Search.” What do you want them to show?

Do you want them to represent the data, which maybe looks like 90% white men over here in this part of the world? Or do you want them not to be representative of the data as it exists, and show a variety of people?

That’s just an example of the choice they have to make. What do you want to show? An unpleasant but accurate picture, or an aspiration but inaccurate one?

That’s a tough one, right?

The ethical issues of using the internet to train artificial intelligence.

Say the three of us developed a dating app and were selling it to users for $20 a month. And then we have a meeting and say “Hey, look at all this data we’ve got. We should sell the data and make even more money!”

But now we’re selling data into aggregators that were never intended for that purpose. So we’ve got GPS data, we’ve got timestamps of data of when people sent messages and how many times and so on. And yes, that’s extremely powerful and useful. That’s why Meta bought WhatsApp: it doesn’t read the messages, but the metadata is worth a fortune. But it wasn’t intended for that purpose.

Our dating app now is producing data that we never intended to, and that’s the situation we’ve got with the entire internet. 

It was never intended to be training data for this kind of artificial intelligence, let alone to try to teach it ethics.

So the problem exists on a global scale.

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THQ:
That’s almost as scary as the “Artificial intelligence will burn the world” headlines. Only slightly more intellectual and real.

So what happens if we don’t address this? As you say, it’s not going to blow up the world, but in what ways will it negatively affect the nature of society as we understand it now? 

RF-F:
It’s cat and mouse, isn’t it? That’s the problem – you start to lose track of what’s reaction to the world and what’s been created in the world and you can no longer really understand sources or truth or where things have come from and who’s written them and why they’ve been written.

For instance, we’ve always had very clever marketing people and campaigns, but we’ve known the purpose. You look up at the billboard and there’s a bunch of young, beautiful people drinking Diet Coke. It’s obvious – you’ll be more popular if you drink Diet Coke. It’s a dumb message, but we get it. They want us to go and buy Diet Coke.

THQ:

*Pops can.* Sorry, do continue.

RF-F:

Then we get into the world of social media algorithms, AI, and large language models, and we have no idea what’s what – no idea of motivation, or response, or outcome. So there is a complete inequality of understanding between the user and what they’re putting in, what’s done with that, what the implications are of that. 

The calls for ethics in artificial intelligence are growing.

SB:
History is written by the victors. It’s never written by those who supposedly lost. For instance, take Charles Babbage. Absolutely brilliant, man. But did he invent the engine? Or did his supposed sidekick, Ada Lovelace? Well, probably she did, but he was the one who wrote the paper.

What we’re doing or what we might end up doing with generative AI and artificial intelligence of this nature is rebuilding all of those prejudices and enhancing them, which means that in 20 years’ time, 30 years’ time, instead of having equality in the marketplace and the workplace, we’ll have an even larger amount of inequality. Of patriarchy. Of white privilege. Of heteronormativity.

We’ve spent 20 to 30 years trying to make the world a slightly less prejudicial place.

We haven’t done a very good job of it, from what I can see. But we are in deep danger of knocking it backwards because of the inbuilt prejudices and everything that’s been written on the internet. If we’re using these tools to make decisions, they’re going to make those decisions based on what they know.

And what they know may not be the truth.

THQ:

In fact, it’s vastly unlikely to be the truth.

SB:

Exactly. Look at England in 1066. Did King Harold get an arrow through his eye? It’s more likely that he disappeared and went hiding for a little bit before going across to France or wherever. But he didn’t create the tapestry, the accepted record of events.

THQ:
Dear gods, we’ve just realized. As far as artificial intelligence is concerned, the internet is the tapestry of record, and the tapestry of accepted ethics. Come to that, the “Bayeux Tapestry”… isn’t even, actually, a tapestry. It’s an embroidery.

But of course, the arrow in the eye makes for a much better human narrative. The fact that it’s almost certainly not true has always been seen as somehow less relevant than the quality of the story.

History – it’s all fun and games till someone “loses an eye.” #WinkyFace

SB:
Yeah, exactly. And that’s what we’re now taught.

THQ:
So it won’t destroy the world, but it might make the world we think we know unrecognizable to future generations?

SB:
That might be a little bit too exaggerated, but it’s going to make it harder for equality to take place, because we’re building on a state of inequality to begin with, and we’re teaching those models with an unequal dataset. And as soon as you have that, then it gets built in and propagated out. As Rich said earlier, it’s so much more data now, so many more decisions.

What will our newest magic mirror show us about our society? That depends on whether we teach it ethics.

THQ:
The big idea in science fact and science fiction both is that technology is a mirror of the society that creates it. So the question is how do we solve this problem of teaching bias and flawed ethics to artificial intelligence, without tearing down and fixing the society we know?

In Part 3 of this article… we’ll find out.

Frankenstein taught us that created systems will emulate their creators, so leaving them without ethics is probably an enormously bad idea.

The post What are the risks of artificial intelligence without ethics? appeared first on TechHQ.

• Artificial intelligence has no inherent ethics.
• The task of defining ethics for artificial intelligence is complex.
• Even within one culture, there are many ethical standards.

Generative AI has been both brilliant and controversial since it exploded across the world late in 2022. But one of the main concerns around its use is that artificial intelligence is a system inherently devoid of ethics.

There are plenty of people who argue that artificial intelligence is just a tool, and no-one has ever suffered by using a hammer against a nail or a spoon to eat their dessert – and no-one’s ever argued the need for an ethical hammer.

But that of course reduces, beyond the point of useful comparison, the kinds of uses to which artificial intelligence is being put, already, less than a year after its release.

In particular, artificial intelligence is being deployed in ways that make ethics not just a necessary part of its make-up, but a crucial one.

Data security, recruitment, resource allocation and more are areas in which the new iterations of generative AI are being deployed – and in which, were the jobs being done by human beings, we would want to be sure that those humans had ethical compasses in line with both company aspirations and norms of societal positivity and progressiveness.

Does artificial intelligence have electric ethics?

Artificial intelligence doesn’t, in any native way, have those compasses. Large language models are trained on the screaming ethical void that is the internet. More bespoke, open-source versions can be trained more easily in company-specific data-pools, but even that leads to uncomfortable questions.

The case of Amazon is a key example. When using artificial intelligence in its initial recruitment process, it famously started weeding out women and people of color when looking for managerial candidates – because the historically accurate data it was fed on what qualities successful Amazon managers had strongly suggested that such managers were both white, and male.

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Artificial intelligence has the inherent ethics of a mirror. If your company has had historically poor representation, you can be sure you’ll be teaching that poverty of diversity to your AI.

And you really need to do better than that.

That’s why a UK-based artificial intelligence ethics body, MKAI (Morality and Knowledge in Artificial Intelligence) and secure data platform OmniIndex have come together in an attempt to eliminate the bias inherent in an AI created in our society, and provide a pathway that includes – and indeed insists on – good, 21^st century ethics in AI projects.

We met with Richard Foster-Fletcher, Chair of MKAI, and Simon Bain, CEO of OmniIndex, to see how it’s possible to teach artificial intelligence to have good ethics – and to some extent, how we can be sure we knew what good ethics look like.

The scale of the ethics question in artificial intelligence.

THQ:
What’s the scale of the problem that we’re tackling when it comes to artificial intelligence bias and ethics? What happens if we just don’t tackle it, or if tackle it in the wrong way?

RF-F:
People in the industry are more concerned about this than people outside the industry, because we’re biased and we spend our time thinking about it – which outsiders probably don’t. With that proviso in place, I think it’s an absolutely global problem.

Artificial intelligence ethics will need to be applicable around the world.

I don’t think it’s a problem in terms of extinction threat, as has been said, but I do think it’s a problem in terms of the underlying structure of our society, particularly in societies that we know here in Europe, which we have to believe are built on fairness and just principles.

I think we’re in danger of damaging those significantly. Would you like the Artificial Intelligence Ethics Issues 101 version?

THQ:
We love a 101 version – then at least we can build on a solid foundation of understanding – which seems necessary in questions of ethics.

RF-F:
OK, well the three things that can go wrong with artificial intelligence are 1) that there’s bias in the data inherently, 2) that as we move forward, the selections that we make are biased, and 3) the way that we interpret the selections we make and the results we get are biased.

The strange thing perhaps is that in some industries, you can get away with it sometimes. In other industries, you absolutely can’t at all, ever.

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ChatGPT and the downside of digital democracy

The very nature of AI is that it scales, it amplifies, it accelerates what you’re doing. And that’s why things go wrong so quickly. If you take something like ChatGPT, it was trained on the internet.

Well, the internet’s largely written in English – certainly the bit of it used to train ChatGPT. We know there are a lot of North American websites making up a substantial percentage of the internet as a whole. And we know that tribes in Papua New Guinea are not represented at all.We know this.

It’s obvious. So then, we scale out a model, and I think they’ve done a reasonable job in trying to produce unbiased results. But ultimately, it’s difficult when the dataset you’ve got is so inherently biased. Now, within AI, it scales right down so that individuals get penalized when they shouldn’t. And if you think about financial decisions, legal decisions, even recommendations in entertainment system, the results won’t be what the person needs or wants, it won’t be representing them as an individual.

The ethics of a cellphone plan.

There was an example in the US where they were using whether somebody had a cell phone plan as an indicator of whether they would reoffend or not. When you scale that out from a data-point to a decision-making paradigm, it makes perfect sense. When you scale it down, you find individuals who absolutely should have been bailed, who weren’t because they didn’t have a cell phone. Which is just nuts, right?

THQ:

Huh. Who knew AT&T could save you from jail time?

RF-F:
Obviously, that’s not correct on individual basis, because it harms individuals coming from certain minorities, certain parts of society, certain ages, and certain genders.

Which is a bad indicator, because even if, right now, “we” don’t get caught in decisions like that, the fact that it can be means that one day it will be, because we’re all going in the same direction. It means there’s the potential for bias in the system.

And then if you scale that problem up, you get policy decisions and news generation that’s based on this data. And now we have laws and governance and public affairs and so on that also don’t represent the society within them. And you can see how we’re just going off on a trajectory that’s going further and further away from large portions of society.

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And I know in the UK, we don’t want that. That’s not the society we want here.

We want an inclusive society.

SB:
That leads to one other point on bias. I was chairing a meeting a number of years ago now with Chief Data Officers, and one of the guest speakers got up and started talking about how bad some of the data was out there, and how it should all be classed as good moralistic data.

And that’s brilliant. Until I asked him whose morals he wanted to use, because my morals are going to be different to yours, and the morals of the West are going to be different to the morals of the East, and the morals of the Far East. And when you come to data, especially within AI, as Richard says, you’ve got this massive amount of data and it’s being churned over very quickly. You’ve got to be very careful of where those choices come from and how those choices are made.

White, middle class artificial intelligence ethics?

And it’s people like ourselves who are writing the rules engines. But if we are all nice, middle class, Western-thinking white male people, then those rules engines are going to be wrong for the other 60%-70% of the world. And we have to be very careful on that.

THQ:

That seems fairly important. After all, even among demographic groups, like middle-class Western-thinking white male people, you get different interpretations of ethics – that’s why political parties still exist. Expand that out to other demographics even within one society, and you’re looking at a multiplicity of ethical standards to incorporate within “good ethics” for artificial intelligence.

Bad things have a tendency to happen when Western powers enforce their ethics on other countries…

RF-F:

Exactly. So what’s the point in Simon and I coming into, say, Uganda, where 70% of people are really below the poverty line and bringing our ethics to their artificial intelligence? It’s not relevant. They have ethics in the country that we have to respect and understand, while still having an understanding of absolute harms. Everything that’s not an absolute harm, we need to be very respectful of.

In Part 2 of this article, we’ll explore more of the complexities of how we establish appropriate ethics for worldwide systems like artificial intelligence.

Our ethics, your ethics? To some extent, they’re down to the clueless luck of geography, wealth, religion, and other such random factors. So how do we teach an artificial intelligence?

The post Can artificial intelligence have ethics? appeared first on TechHQ.

From chatbots and virtual assistants that offer instant, personalized assistance to predictive analytics that anticipate customer needs, AI is redefining the very essence of customer interactions.

Automated systems are so intuitive that they can sort out simple queries without anyone having to wait in a tedious call queue. Human agents can now concentrate on customer issues that could only be solved with their skills, with AI-powered tools allowing them to do so more efficiently than ever.

TechHQ spoke to Colin Crowley, the Senior Director of Customer Engagement at contact center systems specialist Freshworks, about some of the challenges facing businesses and their CX improvement challenges.

“On a general basis, a lot of these AI-powered technologies help to resolve one of the age-old battles in the customer support world between efficiency and quality,” he said.

The typical AI chatbot can help to increase a company’s efficiency but automated, smart systems can’t handle every inquiry. However, forgoing them entirely results in human agents dealing with a high volume of menial issues, leading to unnecessarily long wait times.

“Typically, in the past, efficiency and quality were at loggerheads, where you would have to sacrifice one for the other. And, typically, you were sacrificing the former for the latter because it’s easier to quantify and monetize efficiency.”

In a contact center, efficiency is measured, traditionally, with metrics like the number of contacts an agent makes each hour or the average issue-handling time. However, an assessment of quality would involve evaluating a myriad of factors, including the agent’s communication skills, problem-solving abilities, and customer satisfaction.

Mr Crowley said: “I think what AI has done is really change that up entirely, so we now live in a world where you’re capable of increasing quality and efficiency at the same time; it’s no longer a choice between one or the other.”

Quality can now be metricized through AI-powered sentiment analysis, which gives a reading of how a customer is feeling about a conversation, and scoring based on specific criteria or keywords, allowing agents’ performance to be easily monitored at scale.

The best way to strike a balance is to allow chatbots to solely deal with high-volume, straightforward queries, said Mr Crowley.

He added: “That enables customer support teams increasingly to be, I guess you could say, ‘human’ at scale. It empowers them to be more personalized and empathetic and, at the same time, they can handle more contacts simultaneously.”

Companies can also implement A/B testing to achieve that balance, identifying whether a bot or a human agent would work better for ‘grey area’ customer inquiries. For example, early in the sales process, questions from prospective customers are likely to be simple ones that a chatbot could manage, but also the human touch could help boost brand image and be effective towards conversions.

“AI-powered technology can help you look at the qualitative difference in those conversations […] It makes the chatbots able to handle a lot more in a way that doesn’t make you feel like you’re in an endless loop.”

Similarly, an agent-facing bot could flag issues that a customer may have with the service when they reach out with an unrelated query. The agent can then offer to solve this issue too.

AI tools can further benefit human support agents with real-time sentiment analysis. This is particularly beneficial when they must quickly move between multiple conversations requiring different sensitivity and empathy levels.

Mr Crowley said: “Agent-facing bots can be there to help correct your tonality when you’re communicating back and forth with customers to make sure that, based on the mood and the sentiment of the customer, you’re communicating in such a way that is most acclimated to what the customer needs to hear at that particular time.”

For managers, more advanced, AI-powered data analytics can give them a unified view of agent performance, helping to identify areas of improvement. It is common for customer support organizations to sit on top of swathes of unused data, largely because they do not know how to gather it or turn it into actionable insights.

“That’s a huge area where AI can help because it can analyze all of this […] data coming in from customer contacts on the fly and help to funnel that back into the operations, product, or web team to improve your service or product,” said Mr Crowley.

The technology can also help agents offer benefits to customers while remaining mindful of company finances.

Mr Crowley said: “It’s hard to know what’s the best discount or amount of credits to give someone, and when you’re giving too much or too little.

“Based on certain criteria of the customer, which could be their loyalty tier, prior contact history, or how many orders they’ve had previously, that agent-facing bot could recommend the perfect accommodation that satisfies the customer and, at the same time, is conscientious about the company’s pocketbook.”

While there are many benefits to applying AI solutions, doing so effectively is challenging. Often the data necessary for them to work comes from disparate company systems, and it is time-consuming to collate it, especially in real-time and at scale.

The solutions can also involve a significant upfront financial investment without an easily measurable ROI, which has traditionally held companies back. However, Mr Crowley says that this is likely to decrease in the near future.

“Like with ChatGPT, we are seeing widely accessible technology that’s democratizing access to AI,” he said. “At Freshworks, we’re trying to build AI into the product from the get-go. This means you don’t have to balance three separate vendors for this and that AI technology and deal with the data siloing that takes place and extra price tags.”

Advancements in AI solutions are coming thick and fast, and customers now have hyper-personalized experiences in their dealings with all businesses. Their expectations for support are constantly being raised, therefore.

One way Mr Crowley said meeting increased demands could be achieved is by moving some agents, who have a reduced workload thanks to lower-level issues being taken over by chatbots, into an “innovation team.”“Agents can move into more specialized roles where they help to be pioneers in company technology by just keeping up with that technology curve and helping to pilot programs,” he said.

Other roles could be created for agents which involve performing quality assurance on the chatbots and managing their conversational flow. “That’s a great way to utilize agents who are really skilled,” said Mr Crowley.

“The technology can actually create more career opportunities for agents within a customer support organization.”

The role of the support agent itself will also evolve with the integration of AI. Mr Crowley said: “The average customer support agent is going to be handling specialty tasks more and more because most of the lower-level issues are going to be taken care of.

“That entirely changes the customer support role from being […] this lower-level role, where you’re just a reactive receptacle of people’s complaints, to a role for people who are more skilled, more specialized, and paid better.“You could say that it actually improves the agent experience too, because typically agents don’t like handling very mundane issues because it’s not very satisfying.”

However, it is necessary to pair the launch of AI tools with a clear plan about how the support agent role will change and remain a part of the company structure.

Any investment in customer-facing AI technologies should also be matched by investment into projects which support agents. After all, by having a bot at their fingertips which helps them manage four customer interactions simultaneously, they will reap benefits from their faster response time in the form of higher satisfaction scores.

“That’s a great way to condition customer support agents to see AI technology as something that enhances their experience and empowers them to do better without being burnt out, rather than seeing AI as something that takes their jobs away,” said Mr Crowley.

He added that integrating AI technologies into customer support organizations will influence their strategies more broadly. The success of chatbots could see more of a focus on conversational engagement through channels like Facebook Messenger and Direct Messages on Twitter.

Mr Crowley said: “These are all channels that are relatively real-time but with an asynchronous back and forth and are more immediately attached to everyone’s social life. AI is making conversational engagement disproportionately more efficient and higher quality than other contact channels, and that will push more companies to go towards those channels in higher degrees.”

They will also level the playing field between smaller and larger companies, as readily available AI solutions mean they can compete on efficiency in a way that was not previously possible.

He said: “That’s going to force, I think, a lot of these bigger companies to have better customer service. The smaller companies would traditionally tend to have better customer service because they are spending more on that as a differentiator. So AI will help increase competition on the customer service side with the bigger companies.”

The post The synergy of AI and human agents: A new era in Customer Support appeared first on TechHQ.

• Dining in at fast food restaurants is becoming a thing of the past.
• Chains are removing seating to optimize for takeaway pick-up and delivery.
• The future consists of robot chefs, drone delivery, and anti-sog packaging.

When you imagine a fast food restaurant, what do you see? A sparkling, plastic-and-tile establishment echoing with the squeals of children’s birthday parties and sniggering teenagers? Or queues of silent adults standing behind freestanding touchscreens or an unattended kiosk?

The answer probably differs depending on the last time you set foot in a McDonald’s as, over the past few years, the latter has become a more accurate representation. Technology is turning the sound down, as customers order over the phone or through a touchscreen, stand and wait to pick up their order before grabbing their food and racing outside – all without saying a word.

There has been a notable change in the number of visitors eating at fast food chains, which was accelerated by COVID-19. According to data from the NPD Group,  just 14 percent of US quick-service restaurant traffic is now dine-in – just half of what it was pre-pandemic. The following year, 85 percent of all fast food orders were taken to go.

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This is working towards a change in fast food restaurant culture. While the so-called ‘Golden Arches’ was once the destination, it is now just a quick stop along a pre-existing journey, if that. Many who want to enjoy some salt, fat, acid, and heat need only open their phones to have it delivered to their door within minutes.

As a result, chains are reducing the number of tables available for their customers, optimizing the space for on-premises orders, takeaways, and drive-thrus instead. This includes more drive-thru lanes and windows specifically for third-party delivery pickup.

Chipotle already offers designated drive-thru lanes for mobile-order pickups only, and other establishments, like McDonald’s, Burger King, Taco Bell, and KFC, are eager to roll them out too. Source: Chipotle

Fast food – standing room only

TGI Fridays introduced ‘Fridays on the Fly,’ a 2,500-square-foot store format that focuses on delivery and takeaway orders, early last year. Chipotle already offers designated drive-thru lanes for mobile-order pickups only, and other establishments, like McDonald’s, Burger King, Taco Bell, and KFC, are eager to roll them out too.

In fact, McDonald’s has already set up an ‘Order Ahead Lane’ at a branch in Fort Worth, Texas, that is nearly 100 percent automated. The restaurant was opened in December last year and has no indoor seating at all, instead containing special kiosks and digital screens where customers can place their orders to-go.

It also has a special pick-up shelf and a room dedicated to serving delivery drivers. Finally, it has parking spaces available for curbside pick-up, meaning anyone can have a warm meal within minutes of arrival.

McDonald’s in Fort Worth, Texas has a special pick-up shelf for mobile orders and a room dedicated to serving delivery drivers. Source: McDonald’s

The restaurant was opened in December last year and has no indoor seating at all. Source: McDonald’s

Four months after the Fort Worth branch was opened, it was reported by the Wall Street Journal that McDonald’s would be laying off hundreds of employees as part of a company-wide restructure. A memo sent to staff said that the layoffs were intended to make McDonald’s more efficient.

While most of those impacted worked at the corporate offices rather than in branches, the restructuring was said, at least in part, to “accelerate the pace of… restaurant openings” and “modernize ways of working.” Who knows what changes will come next to help achieve these goals?

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It is clear that most fast food chains are making efficiency improvements a top priority. Wendy’s is piloting “Wendy’s FreshAI” to take orders at drive-thrus and an “underground autonomous robot system” which will see bots delivering orders from kitchens to parking spots. Starbucks plans to open 400 new takeaway or delivery-only branches in the next three years, according to the Wall Street Journal, after removing all of the seating in select cafes.

McDonald’s is also the latest restaurant to utilize ‘geofencing’ – where back-of-house staff are alerted when a customer is approaching the restaurant to pick up their order with their location data, ensuring the food can be ready and warm upon their arrival.

If this trend of shifting towards delivery service is anything to go by, it seems that fast food fans are willing to accept the 30 percent price hike on food ordered through a third-party app, like Deliveroo or Uber Eats, for the comfort and convenience of having a meal in on the sofa.

But if you take into account travel costs, dine-in taxes, and the temptation to go and spend more money at other establishments after eating, does it really end up the more expensive option? Besides, your ludicrous energy bill needs to be paid regardless, so you may as well feel the benefits of it by staying in.

No pickles, no people

The battle for automation rages on, and there are countless technologies that are just waiting to be rolled out more widely. A robot chef called Flippy, from Miso Robotics, can reportedly flip burgers faster than a human while maintaining consistent quality. The bot is being used by White Castle, CaliBurger, and Inspire Brands, the parent company of Buffalo Wild Wings, Arby’s, and Sonic.

Starbucks has already spent millions on AI-powered espresso makers, which can mix brews more quickly than a human barista, and plans to invest even more in the area. The Blendid autonomous smoothie kiosk, which allows customers to order custom fresh drinks via an app before a robot arm gets to work with fruits and vegetables, provides a glimpse into the future of food stalls.

Short, Tall, Grande, Venti… and Robot?

Special packaging is being developed that prevents food from getting soggy over longer periods of time, allowing delivery drivers to take on more orders during their routes.

But those doing the delivery may not be human either. Starship Technologies’ fleet of autonomous ground vehicles currently deliver groceries in cities in the UK and US. They each have ten cameras, GPS, and inertial measurement units, as well as microphones and speakers to interact with customers. Their LIDAR systems provide a 360-degree view of their surroundings, enabling them to navigate pavements and objects to reach their destination.

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In Shenzhen and Shanghai, China, food delivery giant Meituan has been using drones to fly meals between skyscrapers for over a year.

Autonomous food delivery robot from Starship Technologies. Source: Starship Technologies

A food-delivery drone from Meituan. Source: Meituan

Where to now?

The poignant atmospheric difference between today’s fast food joints and those of, say, 2002 could have more consequences than just a slightly less happy Happy Meal experience. Many of these branches, McDonald’s particularly, tend to be places favored by vulnerable people.

They are often open all night, are well-lit, have sockets to charge your phone and free Wi-Fi, are cleaned regularly, and serve affordable food. Some have been designated ‘Safe Spaces’ in UK cities, where help is offered to those that need it.

It is unlikely that the ‘virtual restaurants’ of the future – McDonald’s has filed a patent for joints accessible only via a virtual reality headset – will provide similar sanctuary.

The post D’you want humans with that? How the fast food industry is turning to AI appeared first on TechHQ.

• A cognitive robot – a robot with senses – could bring huge advantages to market.
• Neura Robotics has just secured $55million to expand its cognitive robots into the US and Japan.
• Could the dawn of a new technological age follow shortly?

The notion of a robot with senses has always been a key factor in successful science fiction. A robot that can see, hear, and respond appropriately to touch is a robot which, while not exactly “free” from its programming, can certainly massively expand the range of its functions in a workplace alongside humans.

Congratulations – you lived long enough to become part of science fiction.

The reality of a robot with senses.

The cognitive robot – the robot with senses – are a reality now. Leader of the pack in terms of developing these advanced robots is Neura Robotics, which recently closed a funding round to the tune of $55 million from European investment management company Lingotto.

The technologies of robotics and AI have both suffered a degree of sci-fi-inspired paranoia in the wider world – Robots will rise up and kill us, AI will take over the world. Fill in the specifics according to what frightens you most.

Unfazed by all such creative but unfounded fear, Neura Robotics became the first company in the industry to blend AI with robotics, to leverage the advantages of both.

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Just a short while after the company launched, it introduced MAiRA, the world’s first market-ready cognitive robot. MAiRA, short for Multi-sensing Intelligent Robotic Assistant, is capable of attaining full environmental and social perception, and demonstrating autonomous behaviour.

Neura provides a platform to its partners, where the development of applications can be shared. Such applications cover various sectors, from heavy industry to the service sector, even through to a household application. Eat your heart out, George Jetson.

This variation of function is achieved by integrating all essential sensors and components, that are then incorporated with artificial intelligence using a single device. While we’ve all heard about the Metaverse, the rapidly evolving Neuraverse (Yes, really) exists to offer cost-effective automation services, with a flexibility that has previously been unattainable in multi-functional robotics.

David Reger (Founder and CEO of Neura Robotics) says the company has been “working to push the boundaries of innovation in robotics by rethinking the subject with artificial intelligence and a platform approach.” In doing that, the company could be justifiably said to have taken us into a new age of robotics. The age of cognitive robots. Robots with senses.

The point of Neura’s robots is that, while for instance some of Amazon’s robots can receive and respond to some sensory inputs, and some mass manufacturing robots can respond with a touch-judgment to levels of pressure, cognitive robots take the whole state of the art to a new level.

How to give a robot senses.

The Neura robots have sensors that give them the robotic equivalent of “robot senses” – the ability to see, hear, and sense touch. Then those sensors are paired with reflexive sensory processing, giving them an autonomous and predictive capability.

That combination means the Neura robots are particularly well suited to working alongside humans in a range of different societal areas, as well as human-designed settings.

That could lead to significant changes worldwide. In particular, in a world which is waking up to the reality of an extensive shortage of skilled workers, cognitive robots could offer a cost-effective solution.

Yes, we know – robots could be taking our jobs. But arguably, only the jobs of people who currently lack either the skills, the geographic proximity, or the existence to do those jobs.

The robots will steal the jobs…we…don’t have the skills to do…

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The dystopian nightmare of course would kick in if and when employers reclassified a whole “type” of work as “robot work” rather than “human work.” That said, all industrial revolutions do something similar, replacing whole categories of human labor with robot labor.

From the Spinning Jenny to the automotive manufacturing robot, to the Amazon warehouses full of various categories of robot with only a little human supervision, fundamental waves of change will put significant classes of human labor extinct by virtue of relative inefficiency.

The great-grandaddy of the cognitive robot…

The robot with senses is likely to be fundamentally no different – it’s just that having “robot senses” makes the probable displacement of human workers feel somehow more personal, an intellectual “uncanny valley” of anthropomorphic resentment.

The arms race of robots with senses.

While we will now probably begin an arms race of cognitive robots that are capable of working alongside human beings, the question will be whether we as human beings will ever be comfortable working alongside cognitive robots.

Right now, Neura Robotics is at the heart of the convergence between hardware development and AI, with Europe and Germany in particular holding a significant advantage in those fields. The recent funding round for Neura only promotes both its own, and Europe’s, strength in the field of cognitive robotics.

In recent years, the introduction of cognitive capabilities and innovative robotic automation into the industrial and services world has been delayed. But with a proven model from which to work, and to expand its lead over some potential competitors, and with a hefty financial boost thanks to the latest funding round, Neura looks set to drive progress in the field around the world.

As with OpenAI and its sudden launch of ChatGPT on the world back in October, 2022, it’s entirely possible that Neura will soon become the must-know name in cognitive robotics, simply by virtue of getting products out there ahead of the competition.

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In particular, the company has its sights set on the US and Japan, and certainly, Japan has a significantly higher cultural acceptance of the role of robots than the US or Europe has traditionally had. With a brand new $55 million in its pocket, and an order book already in excess of $450 million, Neura is in a good place to bring about the dawn of the age of cognitive robots.

Japan has always been more culturally comfortable with robots. Source: AFP Photo/Philippe Lopez

Doubtless the Googlebot and the Microbot will follow closely in Neura’s wake.

Cognitive robots – an idea whose time has come?

The post “Robot with senses” set to expand into US and Japanese markets appeared first on TechHQ.

La tecnología de consumo está cada vez más avanzada, en parte gracias a los gigantescos avances de la inteligencia artificial (IA). Si bien los avances en esta tecnología llevan años gestándose, en la actualidad son muchas las personas que están tomando conciencia de su poder, sobre todo desde el lanzamiento de ChatGPT y otras IA generativas.

A medida que interactuamos con dispositivos y software más sofisticados, crece la expectativa de los clientes en relación con sus experiencias digitales con las marcas. Y ello incluye las interacciones con los operadores de atención al cliente.

Pero satisfacer estas expectativas cada vez más exigentes no siempre es algo tan sencillo.

Soluciones de IA Fuente: Net2Phone

Los servicios digitales de los centros de contacto se actualizan constantemente para ayudar a las empresas a adaptarse a las crecientes exigencias de sus clientes. Sin embargo, si no se realiza con un proveedor adecuado y de forma correcta, intentar integrar todas las funciones y los canales de comunicación puede dar lugar a un sistema desarticulado, que repercute en la eficacia de las interacciones y en la experiencia general del cliente (CX).

Si se ofrecen, sin una estrategia y plataforma adecuada,  distintos canales para chats, correos electrónicos, redes sociales y llamadas de voz, los clientes corren el riesgo de verse expuestos a distintos métodos y niveles de contexto por parte de los operadores. Es posible también que sean redireccionados de un canal a otro, con la consiguiente pérdida del historial de interacciones durante el proceso. Los clientes pueden tener que repetir sus datos y frustrarse, llevándose una experiencia negativa.

Soluciones de centros de contacto en la nube para empresas, Fuente: Net2Phone

Un número excesivo de canales también dificulta que los operadores con los que hablan les ofrezcan una experiencia personalizada. Esto debería ser una prioridad, ya que el 71 % de los consumidores esperan interacciones individualizadas por parte de las empresas. Tener que navegar por múltiples plataformas para hilvanar el recorrido del cliente dificultará la capacidad del operador para ofrecer una asistencia eficiente y personalizada, lo que reducirá las tasas de conversión.

El disponer de todos estos canales y funcionalidades puede plantear una serie de problemas diferentes a la gerencia. Cada flujo de trabajo complementario —que no deja de perfeccionarse gracias a las actualizaciones de software— se traduce en un mayor volumen de datos que hay que analizar al momento de tomar decisiones fundamentadas. Esta sobrecarga de datos también plantea retos a la hora de almacenar, procesar y extraer información procesable si no se dispone de estrategias y herramientas integrales.

¿Es la IA la respuesta?

Para atender las crecientes expectativas de los clientes de recibir experiencias personalizadas, así como la cantidad cada vez mayor de datos que recopilan los servicios de los centros de contacto, los proveedores deben aprovechar el poder de la IA.

Una forma popular de hacerlo es incorporar incialmente un chatbot de IA de texto generativo al sitio web, pero esta puede resultar una estrategia arriesgada. Si no se selecciona el proveedor La tecnología sigue expuesta a imprecisiones y sesgoslo que puede perjudicar a la marca responsable de implementarla.

En lugar de confiar solo a un chatbot la atención de los clientes, TechHQ busca las formas más sofisticadas en que las empresas pueden utilizar la IA para mejorar la experiencia de sus clientes y empleados y, en consecuencia, hacer crecer su negocio.

• Los resúmenes de llamadas mejoran la eficacia de los operadores

En lugar de que el operador tenga que tomar notas manualmente durante o después de una llamada, la IA puede autogenerar un resumen en cuanto el cliente finaliza la conversación y cuelga el teléfono. Convierte los datos recogidos durante la llamada en puntos de acción y pasos a seguir, que pueden utilizarse para redactar correos electrónicos de seguimiento. Esto no solo permitirá a los operadores ser más eficientes, sino que también podrán ofrecer una atención al cliente personalizada con más facilidad. Esto se debe a que los clientes únicamente recibirán la información que precisen y mantendrán una conversación fluida a través de todos los canales.

• El análisis detallado de las llamadas mejora la experiencia del cliente

Los nuevos algoritmos pueden registrar métricas que una persona no podría, como el número de palabras que se hablan por minuto, los momentos en los que los clientes y los operadores se interrumpen entre sí y la proporción entre hablar y escuchar en una conversación. Analizando estos datos, la IA puede identificar tendencias sutiles que contribuyen al éxito de los operadores. Esta valiosa información puede utilizarse para optimizar la capacitación, mejorar los procesos de gestión de llamadas y, en última instancia, mejorar la CX en general.

• Sugerencias para reducir la brecha de habilidades

Según un informe de Cresta Insights, la tasa de rotación de empleados en los centros de atención telefónica enfocados al servicio de asistencia es aproximadamente 1,3 veces superior a la media estadounidense. Esta elevada tasa de rotación provoca un déficit de habilidades, ya que el nuevo personal necesita tener la capacitación o la experiencia adecuada para atender las complejas consultas de los clientes. Sin embargo, la integración de la IA en su centro de contacto puede ofrecer una solución.

Existen algoritmos que pueden generar automáticamente sugerencias para la mejora a partir de un análisis exhaustivo de las llamadas, capacitando al operador sin necesidad de una intervención constante de los gerentes. De este modo, el personal recién contratado puede mejorar rápidamente, cerrando la brecha de habilidades y reduciendo el índice de deserción a largo plazo.

• El análisis del estado emocional permite una resolución rápida de los problemas

El análisis del estado emocional identifica con precisión el tono de voz y el contexto, ofreciendo información crucial para las interacciones con los clientes. Permite a los operadores adaptar su enfoque para que puedan abordar las inquietudes con mayor rapidez, al tiempo que permite a los gerentes identificar áreas de mejora en el servicio al cliente. Por otra parte, el análisis del estado emocional sirve como herramienta para que los gestores y los operadores prioricen las interacciones de alta prioridad o de riesgo, lo que les permitirá asignar los recursos de forma eficaz y garantizar una resolución proactiva de los problemas.

• Una transcripción precisa acelera la evaluación de las llamadas

La tecnología aplicada a la IA puede permitir una transcripción y una grabación más precisas de las llamadas. Esto facilita el proceso de revisión posterior a la llamada, lo que propicia la rápida identificación de oportunidades de mejora para los operadores. Los supervisores también se benefician de ello, ya que pueden llegar a comprender mejor las llamadas y los incidentes que tienen lugar en la empresa sin necesidad de escuchar cada conversación. Esta mejora de la eficacia en la revisión y el análisis de las llamadas supone un ahorro de tiempo, a la vez que ofrece a los supervisores la posibilidad de transmitir comentarios específicos a los operadores. Su rendimiento mejorará en última instancia, al igual que la calidad general de las interacciones con los clientes.

Para mantenerse al día con las crecientes demandas digitales de los clientes, este es el momento ideal para incorporar la IA a su arsenal de herramientas de asistencia. Obtenga más información sobre cómo net2phone puede aprovechar las soluciones de voz sobre protocolo de Internet (VoIP) gestionadas por la IA.

The post El agente secreto de la IA: cómo la automatización puede resolver los problemas en su centro de contacto appeared first on TechHQ.

Consumer technology is growing increasingly advanced, partially thanks to the huge leaps forward made in artificial intelligence (AI). While developments in this technology have been brewing under the surface for years, many are now becoming more aware of its power, particularly since the recent release of ChatGPT and other generative AI.

As we interact with more sophisticated devices and software, it raises the bar for what customers come to expect from their digital experiences with brands. That includes interactions with support agents.

But meeting these heightened expectations is not always straightforward.

Digital contact center services are constantly being updated to help businesses keep up with their customers’ rising standards. However, trying to integrate all the latest must-have features and communication channels can lead to a disjointed system, which impacts the effectiveness of interactions and overall customer experience (CX).

Cloud Contact Centre Solutions for Businesses, Source: Net2Phone

If you offer different channels for chats, emails, social media, and voice calls, customers are at risk of being exposed to varying approaches and levels of context from agents. They can also be deflected from one channel to another, losing the interaction history in the process. Customers may have to repeat their details and get frustrated, leaving them with a negative experience.

An excessive number of channels also makes it difficult for the agent they are speaking with to provide a personalized experience. This should be a priority, as 71 percent of consumers expect tailored interactions from companies. Having to navigate multiple platforms to piece together the customer’s journey will hinder the agent’s ability to deliver efficient and customized support, lowering their conversion rates.

Having all these channels and features can pose a different set of issues for managers. Each additional workflow – which is continuously becoming more advanced through software updates – results in more, and larger, pools of data to sift through when needing to make informed decisions. This data overload also poses challenges in storage, processing, and extracting actionable intelligence without comprehensive strategies and tools in place.

Cloud Contact Centre Solutions for Businesses, Source: Net2Phone

Is AI the Answer?

To cater to customers’ heightened expectations for personalized experiences, as well as the increasing amount of data that contact center services collect, providers must harness the power of AI.

A popular way of doing so is to hastily add a generative AI chatbot to the website, but this is a risky strategy. The technology is still prone to inaccuracies and bias, which can reflect poorly on the brand responsible for implementing it.

So, rather than letting a chatbot loose with customers, TechHQ looks at more sophisticated ways companies can utilize AI to improve their customer and employee experience and grow their business as a result.

• Call summaries improve agent efficiency

Rather than the agent manually having to take notes during or after a call, AI can autogenerate a call summary as soon as the customer puts the phone down. It turns data collected during the call into action items and next steps, which can be used to compose follow-up emails. Not only does this allow agents to be more efficient, but they can also more easily provide a personalized CX. This is because customers will only receive the necessary information, and will maintain a seamless conversation across channels.

• Deep call analytics enhance customer experience

New algorithms can take note of metrics that a person could not, such as words spoken per minute, instances of callers and agents talking over each other, and talking-to-listening ratio. By analyzing this data, an AI can identify subtle trends that contribute to agent success. This valuable insight can be used to optimize training, improve call-handling processes, and ultimately enhance the overall CX.

• Suggestions for improvement close the skills gap

According to a report from Cresta Insights, the employee attrition rate at customer call centers that focus on support is about 1.3 times higher than the US average. This high turnover rate leads to a skills gap, as the new staff need to have the appropriate training or experience to deal with complex customer inquiries. However, integrating AI into your contact center can offer a solution.

Algorithms can automatically generate suggestions for improvement based on deep call analysis, coaching the agent without the need for constant managerial input. The newer staff can then improve quickly, closing the skills gap and reducing the attrition rate in the long run.

• Sentiment analysis allows for fast issue resolution

Sentiment analysis accurately identifies voice tone and context, providing crucial insights into customer interactions. It allows agents to adapt their approach so they can address concerns more promptly, while also enabling managers to identify areas for improvement in customer service. Moreover, sentiment analysis helps managers and agents prioritize high-priority or at-risk interactions, allowing them to allocate resources effectively and ensure proactive issue resolution.

• Accurate transcription speeds up call reviews

AI technology can allow for more accurate transcription and call recording. This makes for an easier post-call reviewing process, leading to the swift identification of opportunities for agent improvement. Supervisors benefit from this too, as they can gain a deeper understanding of calls and incidents taking place within the business without needing to listen to every conversation. This enhanced efficiency in reviewing and analyzing calls saves time while empowering supervisors to provide targeted feedback to agents. Their performance will ultimately be improved, as will the overall quality of customer interactions.

To keep up with the increasing digital demands of customers, there has never been a more important time to add AI into your support arsenal. Learn more about how net2phone can leverage AI-driven Voice over Internet Protocol (VoIP) solutions.

The post The Secret AI-gent: How automation can solve your contact center problems appeared first on TechHQ.

• Nestlé is using AI to provide optimized marketing campaigns.
• But the brand remains tainted by the ‘baby milk scandal.’
• AI tools can work to both improve and destroy a brand’s reputation.

Don’t panic, your chocolate isn’t going to be turned into a virtual experience just yet, but Nestlé has officially joined the ranks of companies that are integrating AI into their businesses in one way or another.

In February, the food and drinks titan announced that its 15,000-person-strong marketing division would utilize AI tech to help run campaigns across social platforms while maintaining brand consistency. This meant that creative assets produced for any of its 200 brands must be run through an AI system trained on its strict marketing do’s and don’t’s.

The system, named Cortex, comes from creative consultancy CreativeX, which states on its website that “Brand consistency has been shown time and time again to drive lift across both brand and sales KPIs.” An attractive prospect for Nestlé, which hasn’t had the smoothest ride regarding company reputation. In short, it needs to improve its image, consistently.

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It took its largest and most notorious blow about 50 years ago when campaign groups started highlighting what’s now known as the ‘Nestlé baby milk scandal’. In the 1960s, many infant formula companies shifted their campaigning to developing countries to depict it as “scientific, modern, prestigious, and (falsely) nutritionally superior to breastmilk.” Nestlé sent salespeople out to countries in Latin America, Asia, and Africa to promote their products dressed as so-called “milk nurses”.

Nestlé Milk nurses in South Africa. Source: Crowther S M et al. (2009)

They pushed the line that this expensive formula would be more beneficial to children than what their mothers’ bodies produced, which is untrue. Nestlé was later accused of providing incentives to doctors to promote their products.

Many mothers in these developing countries diluted the formula to make it last, often with dirty water, which reduced its nutritional value. As a result, many infants suffered from malnutrition, dehydration, and various illnesses, leading to a higher-than-necessary infant mortality rate.

The aggressive marketing tactics that Nestlé and associated formula companies employed are said to have contributed to a decline in breastfeeding rates in these regions, where it was once the norm and a crucial source of nutrition and protection against diseases.

After health organizations began raising concerns about these unethical marketing practices, Nestlé’s reputation took a turn, and it received significant backlash and boycotts from consumers.

Over time, the company has attempted to address the criticism and improve its image by adopting changes in its marketing strategies. Nestlé now adheres to the International Code of Marketing of Breast-milk Substitutes, created by the World Health Organization and UNICEF in 1974, but activists and critics continue to closely monitor its practices to ensure compliance.

Nestlé now claims to adhere to the International Code of Marketing of Breast-milk Substitutes. Source: Shutterstock

The AI tool Nestlé marketers are using does not claim to be a reputational tonic. Aude Gandon, Nestlé’s senior vice-president and global chief marketing and digital officer, hopes instead that it will change the brand’s advertising focus from TV to digital-first.

When CreativeX was first recruited in 2021, its team trained an AI on thousands of previous Nestlé campaigns and their results. This meant it could learn the elements of these ads that drove the best ROI. Once the learning period was over, Cortex was able to analyze any creative asset and give it a ‘Creative Quality Score’ based on how many elements it contains which prove successful on a given social platform, like YouTube or Facebook. These include aspect ratio, video length, subtitles, and calls to action.

Staff must now put any asset through the AI before it goes live to ensure it is optimized for their chosen platform. According to Ms Gandon, this is to stop time-wasting during marketing meetings where attendees may disproportionately fret about whether minute details, like logo placement, impacted the campaign’s failure.

While the AI-determined rules may sound “unsexy”, as CreativeX’s chief executive Anastasia Leng told The Drum, they are not intended to replace the creative input of the marketers. “These are basic things that create the canvas on which an idea can be successful,” she said.

Cortex is not the only way Nestlé has integrated AI into its business. In 2021 it launched a ‘Cookie Coach’ named Ruth, a virtual avatar that could answer basic questions about its Toll House chocolate chip cookie recipe. Ruth was born from the number of questions on how to recreate these cookies that Nestlé received during the COVID-19 pandemic.

In 2021, Nestlé launched a ‘Cookie Coach’ named Ruth, a virtual avatar that could answer basic questions about its Toll House chocolate chip cookie recipe. Source: Nestlé

Research from Greyb also revealed that the company has filed patents for AI technologies that could recommend personalized dietary requirements. Nestle Health Science offers various nutritional supplements, like Boost Women, and provides data for screening and assessment tools that can recommend these products. These tools currently work simply by making recommendations based on the user’s responses to a questionnaire, but the patents suggest these could soon be further optimized with AI.

But could marketing teams use AI to repair a brand’s reputation? These days, many companies make use of AI-driven sentiment analysis to monitor online discussions, social media platforms, and customer reviews for negative opinions. These ensure that representatives can address any concerns in a timely manner and demonstrate that customer satisfaction is a priority.

Similarly, AI-driven moderation tools can be employed to identify and remove harmful or misleading online content and help preserve company reputation. It can also help prevent future scandals by detecting the initial indicators of an escalating situation, allowing brands to take immediate action. Many PR companies now offer AI tools as part of their packages.

Creatively integrating the latest AI-powered innovations could also provide the opportunity for a reputational clean slate. By introducing a never-before-seen feature or service that genuinely addresses customer needs and enhances their experience, a brand can shift the narrative away from past scandals and redefine the company’s image as an AI trailblazer.

Although AI can help with marketing campaigns and safeguarding brand reputation, it cannot fully replace human judgment and creativity. Source: Shutterstock

An example of this is how OpenAI itself, the creator of ChatGPT, was outed as paying workers in Kenya a maximum of $2 an hour to label violent, sexist, and racist text data as such so that it could be used to train an AI-powered safety mechanism for ChatGPT. Not long after, OpenAI released GPT-4, its most advanced system yet, to the public.

As much as advances in AI can help a brand’s reputation, they can also work against it. An example of this is the Cambridge Analytica scandal from 2018, where the data analytics firm misused advanced AI-driven techniques to harvest and exploit the personal data of millions of Facebook users without their consent.

This data mining operation aimed to build detailed psychographic profiles of individuals, which were then used to deliver targeted political advertisements and influence the outcome of elections, including the 2016 US Presidential election and the Brexit referendum.

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While an increasing number of companies embrace AI to enhance the efficiency and effectiveness of their products and services, they also open themselves up to a host of potential controversies related to its use. While these may not be as high profile as what happened with Cambridge Analytica, a wayward chatbot or biased algorithm can inflict significant reputational damage.

The majority of AI tools can’t fact-check information, so companies that rely on generated content put themselves at risk of associating inaccurate information with their brand. Gartner predicts that 80 percent of marketers will deal with content authenticity issues by 2027.

What’s more, AI-derived copy can lack originality and personality, as it is produced by an algorithm that only works by re-assembling oft-assembled words and sentences. That tends to produce results that are safely generic. Chatbots are also open to manipulation and to proliferate bias from their training data, so aligning one with a company comes with a certain degree of danger.

As Ms Leng put it, many creatives are concerned that AI tools can only produce “cookie cutter” content. It is true that, although AI can help with marketing campaigns and safeguarding brand reputation, it cannot fully replace human judgment and creativity. Therefore it is crucial for companies like Nestlé to tread carefully, ensuring a balance between harnessing AI’s potential and preserving the essence of the human touch, if it wants to remain a household name.

The post Sweetening things up: Can AI help repair Nestlé’s reputation? appeared first on TechHQ.

From the early stages of the internet’s widespread adoption to the present era of hyperconnectivity, the volume of data we generate has grown exponentially. The proliferation of smartphones, the Internet of Things (IoT), and the seamless integration of technology into everyday life have all contributed to this increase. By 2025, we are expected to generate 463 exabytes of data each day.

For businesses, this surging digitalization has resulted in new customer expectations and necessitated innovative approaches to meet them. The largest industries, like financial services, retail, telco, and healthcare, face the challenge of effectively processing and utilizing the vast amounts of data they have access to. Traditional data processing methods are being challenged to keep up with the sheer scale, velocity, and variety of information businesses generate like never before.

Enterprises are turning to cutting-edge technologies such as GPU-based cloud computing, artificial intelligence (AI), and distributed edge computing to revolutionize their operations. Cloud computing provides scalable storage and computing resources, and machine learning (ML) techniques can be used to extract meaningful insights from complex datasets. By leveraging ML, businesses can automate processes, optimize decision-making, and deliver personalized customer experiences.

Edge computing enables data processing and computation to occur as close as possible to the data source, reducing the need for data to be transmitted to a distant data center. By bringing computing capabilities closer, an edge-focused solution can enable real-time processing with reduced latency and enhanced responsiveness for end-users.

The financial sector is already starting to embrace edge devices, as businesses can process data faster and use real-time analytics to offer personalised services. Processing data on these (typically) smaller devices also reduces transmission and infrastructure costs and allows the company to more easily meet local regulatory requirements.

However, other industries can also reap these same benefits. In retail, for example, edge computing can improve in-store customer experiences through faster data processing for personalized offers and real-time inventory management. In the healthcare sector, it can facilitate real-time diagnostics and decision-making for doctors, as well as support remote patient monitoring and telemedicine applications.

Source: Shutterstock

It has been predicted that over 50 percent of enterprise-managed data will be created and processed outside the data center or cloud by 2025. This will be partly thanks to several developments emerging from edge computing. One of these is the use of edge in combination with 5G. Together they reduce latency even further and support real-time processing capabilities creating massive opportunities for telco providers. Edge computing will also likely extend to complement cloud computing for large processing jobs rather than replace it altogether, providing burst capability, for example, at peak demand times. Finally, the edge will be more closely integrated with IoT hardware and local ML-powered applications to improve performance through more efficient data processing close to where IoT and IioT provide critical services.

However, embracing edge computing is not as straightforward as plugging in a new device. It is critical to protect the IT equipment within the devices. There are specific challenges associated with distributing compute in places where IT is not meant to reside – heat, humidity, dust and other environmental debris that can damage the equipment. Exposure to contaminents can cause failure of edge devices and result in reduced performance, shorter hardware lifespans, and costly device damage. Fluctuations in temperature can also making cooling the equipment challenging and can throttle performance. Having purpose built equipment that is sealed, reinforced and ruggedized is critical to distributing compute in new locations.

As in a centralized data centre facility, edge solutions also need to contend with heat generation and energy consumption during data processing, so cooling becomes critical to ensure the devices’ optimal performance, longevity, and reliability. Cooling technology from data centers may not be appropriate for edge installations, and, as many businesses have their own sustainability goals to meet, having an efficient solution that cools and protects IT equipment is crucial to minimize the environmental impact of edge computing operations.

Tank immersion, where the edge device is completely submerged in a non-conductive dielectric liquid, can address some of these concerns. However, this often requires a significant amount of space, large initial set-up costs, and more complex device servicing. Precision Liquid Cooling, where  a small amount of dielectric coolant is precisely targeted to remove heat from the hottest components of the server, has been developed to address all of these concerns. Through these systems, the heat from the hotspots, like the CPU and memory components, is captured and removed with an in-built heat exchanger.

Precision Liquid Cooling is more scalable than tank immersion. Offered in a standard vertical rack form factor, it offers a finer level of control over cooling distribution and can be tailored to specific components, reducing the risk of thermal bottlenecks. Similarly, it is also a more serviceable solution, as individual units can be easily accessed and maintained without disrupting the entire cooling infrastructure. This serviceability not only reduces downtime during maintenance but also lowers operational costs by enabling hotswapping of devices and allowing targeted repairs or upgrades to specific components as needed. According to Iceotope Technologies, a leading provider of Precision Liquid Cooling systems, the technology reduces component failures by 30 percent and extends the edge server’s lifespan.

The cooling solution is significantly more sustainable than many cooling alternatives. While Precision Liquid Cooling removes nearly 100 percent of server heat, it requires 40 percent less energy and reduces carbon emissions by the same amount. In fact, Precision Liquid Cooling offers approximately 6x power density improvement per square meter resulting in significantly reduced carbon footprint. Water consumption is also minimal due to the absence of mechanical chilling requirements, and significantly less dielectric fluid is required than in-tank immersion solutions.

A new solution is necessary for this new era of heightened data demands. Discover how Iceotope’s Precision Liquid Cooling technologies can enable your business to harness the power of edge computing for enhanced business growth and productivity today, while reducing costs, maximising revenue and accelerating sustainability objectives.

The post Staying cool under data pressure: The role of Precision Liquid Cooling in edge computing appeared first on TechHQ.