Category: Z General – Business Schools

It has never been easier for businesses to leverage generative artificial intelligence (AI) to accelerate go-to-market programmes, create stickier engagements with prospects and optimise operational efficiencies.

By making full use of generative AI innovations, businesses can increase content production significantly, double their inbound SEO traffic, focus sales development representatives on more valuable prospect-facing activities (as opposed to their spending hours in sales-enablement training) and lay the groundwork to shrink the time to localise a website into multiple languages.

Despite the hype surrounding the technology since OpenAI’s much-heralded release of ChatGPT, a recent study by Gartner revealed that fewer than half of business executives are currently investing in generative AI and, of those, only 30 per cent have moved beyond the initial, exploratory phase. This needs to change. Here are some of many ways in which businesses can use generative AI to realise impactful results almost immediately:

• Product marketing: Using ChatGPT4, a marketing team can increase its rate of content creation – from ideation to delivery – by several hundred per cent. Assets can be leveraged directly by prospects and customers via your website, as well as by your sales teams when they engage with prospects and customers.

• Sales enablement: A ChatGPT4 chatbot can be trained on existing content to enable sales development representatives (SDRs) to personalise conversations with prospects and customers and generate real-time answers to questions regarding your company’s capabilities from prospects and customers. Using the chatbot, SDRs can save countless hours of time in sales-enablement training.

• Ask-me-anything chatbots: You can also use ChatGPT4-enabled chatbots trained on your existing content to engage with prospects for real-time, personalised conversations. You can even split the bot you use into different personas. For example, our company has Stu the Security Squirrel to engage with prospects on security-focused conversations and questions, while Calvin the Compliance Cow engages with prospects on compliance-focused conversations and questions.

• Organic intent-based search: Businesses can use data-driven and AI-enabled SEO platforms, such as Quattr, to develop intent-based content that can significantly increase inbound organic search traffic by turbocharging content generation. Such tools can be used to produce high-quality, authoritative content that are published as ‘blog’ and ‘glossary’ resource articles relevant to your prospects.

• Streamlined web content localisation: Traditional approaches to localising website content can be tedious, time-consuming and expensive. Yet, building a translation application programming interface (API) powered by ChatGPT4 can allow a marketing team to translate and publish individual webpages quickly and easily into multiple languages.

• Multimedia to social media: Businesses can dramatically shrink the amount of time and cost it takes to create videos based on new blog posts, solution briefs and other content assets by ingesting them into an AI-enabled tool that automatically creates videos used for social media posts and captions for podcast episodes.

Driving brand awareness and engagement in today’s business landscape requires significant marketing resources just to break through the noise. Using generative AI, businesses can achieve astonishing increases in marketing outputs and exponential efficiency gains. Make sure that generative AI is a non-negotiable part of your business strategy now.

Far from being the killer of all creative thought, AI can empower your innovative processes into tomorrow and beyond. AI is transformative, but its impact will depend on the model of innovation that you are using.

AI as an innovation accelerator

Some innovative processes require surveying a dataset so vast that innovation gets stifled. The pharmaceutical industry offers a good case in point. It has experienced a ‘discovery void’ since 1987 – almost 40 years – in the field of antibiotic drug discovery. Working through millions of potential compounds for one that is successful has resulted in a high failure rate that has historically made the entire drug research industry prohibitively expensive.

Today, Hoffman-La Roche has passed mammal testing with the first AI-identified antibiotic and is currently entering human clinical trials. The technology was used to produce a shortlist of candidates among the millions that appeared worthy of further investigation. Any time you are looking for a one in a thousand event, AI can bring competitive advantages in the early stages.

AI as a catalyst for change

When the rate of internal innovation falls behind the rate of external change, companies will get into trouble rapidly. The banking sector has quickly responded to a new, younger generation of customers following the introduction of banking data interoperability laws that have freed up this conservative sector for innovation. This might include omni-channel services, with a choice of webchat, email, human-to-human phone call, video call, or face-to-face service.

The younger generation are also said to be impatient and perceptions abound that putting them through multiple processes to reach a service will lose them quickly. AI can offer personalisation through an analysis of extensive customer records, reducing the steps needed to access a service. 

In fields such as construction or manufacturing, people want fast, efficient results and the power of AI to reduce error can be a gamechanger. This means rethinking your relationship with customers and suppliers from a one-time sale to an ongoing partnership.

If you agree to share performance data on machinery delivered through sensors to a central data store, it’s also possible to track performance. Customers get early identification of problems, reducing downtime when there’s a fault and the supplier gets valuable feedback on the performance of their machine after the product has left the factory.

AI as the solution

As new AI tools leave laboratories and enter offices, a new innovation cycle begins. The generic generative AI large language model platforms, such as Llama or ChatGPT, are now being fine-tuned with domain-specific datasets. This means that they learn what matters in, say, the insurance field, allowing them to come up with more tailored responses.

We can use therefore these machines to help source datasets we don’t have or sift through relevant research. Provided that any staff involved are well trained in critical thinking, they will be able to look through the technology’s suggestions, remove the junk and focus on potential treasures. Using these tools creates efficiencies in the innovation stage and brings in outside voices. After all, knowledge of your business is a valuable thing, but sometimes a fresh idea from an external voice is essential.

We can also redirect old technologies to new problems now that they are more accessible to smaller businesses. Image recognition, for example, has been used at airports to reduce queues for almost a decade and that same technology is now starting to transform farming. Tractor manufacturers have trained their vehicles to scan fields of mixed crops and identify the crops from the weeds. The machines can then deliver a single drop of pesticide or fertiliser to the weeds and crops, as appropriate, when they are just one cm high. The result is far less chemical use, healthier crops and a move away from environmentally ruinous mono-crop farming – all by re-purposing a technology that was working reliably back in 2016.

Recommendations

However you choose to integrate AI in your innovation cycle, make sure you consider a couple of potential problems. It can be hard to wean your staff off legacy systems, so the earlier you involve your team in discussions, the better. It’s also essential to remember that error is almost always unavoidable and you need a critical approach and plan to deal with it, such as robust feedback loops. These problems are no different from human-based innovation, but a machine can only ever show you ‘what’ – you’re going to need a human to answer questions around ‘why’.  

Artificial intelligence (AI) is transforming higher education at speed. However, integrating AI into the student journey from induction, teaching and learning to student outcomes, requires careful consideration of the pedagogical, ethical and disciplinary implications.

Academics who are positive towards AI are generally seeking to harness it for enhancing teaching and learning experiences, or evolve their discipline. Administrative and professional services colleagues, meanwhile, aim to understand the broader implications of AI in academic settings.

The expansion of generative AI’s capabilities was a big and widely discussed theme within higher education in 2023 that precipitated a year of fact-finding and planning. We now need 2024 to become a year of action.

Changing expectations

As complex institutions, universities and business schools acknowledge that speed of adoption can be an issue – and something that is exacerbated by student expectations. Increasingly, students from all demographics are proficient consumers of technology and bring assumptions over its use in day-to-day life into their study experience. More and more students are also engaged in employment while studying and the changing student experience, including that of international students in particular, is well documented and discussed.

So what do we know about student expectations for their institutions, focusing on AI-based support as a critical area of change and innovation?

Studiosity commissioned YouGov, as part of its Global Student Wellbeing Survey, to conduct research among students across the UK, Australia, New Zealand, Canada, US, Singapore and UAE. The survey ran over five weeks in November and December and gained 10,189 responses, with 149 higher education institutions represented. Business students accounted for eight per cent of responses. Here are three key emerging themes from the study.

Students expect AI study support tools

In the UK, 39 per cent of students across all demographics now expect their institution to offer AI support tools (ie a trained, digital helper that gives personal feedback and other 24/7 study help) and this is consistent for all age groups.

Intriguingly, this proportion is significantly higher among international students in the UK (57 per cent) compared to their domestic counterparts (37 per cent). Business students are also more likely than those from other disciplines to want their university to innovate and provide AI support (57 per cent), compared to 29 per cent among students enrolled on subjects in the humanities and social sciences. Lastly, men are more likely (47 per cent) than women (35 per cent) to expect their university to provide AI-based support.

Worldwide, 57 per cent of students expect their university to offer AI support tools. Students in UAE and Saudi Arabia have some of the highest expectations in the world for this kind of learning assistance (84 per cent and 79 per cent, respectively).

Speed of feedback and improving confidence

Speed of feedback – ie “only waiting minutes, rather than a day or several days” – is the main reason that UK students would use their university’s AI support or feedback (26 per cent), followed by improving confidence, specifically to check they are “on the right track with their assignment’” (17 per cent).

Feedback speed and confidence-building reassurance are universally popular across all demographics of the students surveyed. The exception is that international students in the UK are slightly more likely to choose confidence over speed (22 per cent vs 21 per cent). Postgraduates have the highest amount of need for speed of feedback and support. As a large proportion of postgraduates are in full-time employment (38 per cent in this survey), this is perhaps unsurprising.

Feedback speed topped the list of desired use in most countries surveyed, apart from New Zealand, Saudi Arabia and UAE, where checking to see if they are on the right track was more prevalent. Further analysis shows that students who feel stressed, ranging from “more than a few times a year” all the way through to “constantly” (ie more than twice a day), also say speed of feedback would be the biggest driver for them to use AI support or feedback (26 per cent), followed by building confidence from being able to check they are on the right track (17 per cent).

Universities are not adapting quickly enough

Among UK students, 64 per cent say their university is not adapting quickly enough to integrate AI tools into their learning experience. This is also the picture outside the UK – 55 per cent of students in Australia and 60 per cent of those in Canada strongly perceive that their university is not adapting fast enough here.

UK respondents also revealed that a large number of students remain confused about what AI is and how it can be used in their educational setting. Many expressed concerns about the ethical use of AI, its reliability and the idea of AI potentially replacing human support. There were also reports of university-wide bans. However, while many students believe AI can play an ethical and valuable role in supporting their studies, they also felt that their university did not have this understanding yet and are being slow to react.

The launch of Studiosity’s new AI learning technology, Studiosity+, is designed to address these perceptions, with a focus on giving students formative feedback on their written work in minutes. Universities can see students’ progress, critical thinking development, pinpoint challenges and take next steps.

AI technology doesn’t just have the power to support all an institution’s students, it can also provide the kind of actionable insight that can instigate whole-institution change.