With so much AI news forthcoming every day, where does one start? What should an authoritative publication about CX distill from the various AI developments happening around the world, so that readers can derive insight and initiate action?

Here and now, we will say this: AI and generative AI is going to be foundationally embedded into as many tools and apps we can think of, and this looks set to be a trend for many years to come. Shadow AI and devising Bring-Your-Own-AI (BYOAI) policies are a few of the topics that organizations have to seriously think about and act upon in 2024.

IDC notes that the Asia Pacific region is leading the way in genAI, with two-thirds of companies already investing in genAI or exploring potential use cases. Forrester said 60% of workers will use their own AI to perform tasks, but a stubborn 5% of organizations are still resisting generative AI and its usage in their environments.

AI whitewashing will carry on, but organizations (especially those embarking on an automation or AI journey) must at least be able to tell the difference between AI and generative AI, differentiate the smart, autonomous assistants from the tools that ideate and generate text or visuals, and pickup the basics of Large Language Models (LLMs), as well as act upon improving data management.

With so much AI-driven ‘automation’ anticipated in our future, trust in AI and being able to explain the decisions made by AI is going to be important – how could we totally remove the human from the loop?

How will all these pan out? Only Time (and intensely close monitoring) can tell, eh?

Profit versus Social Benefit

Open AI’s ChatGPT kickstarted a majority of the hype and furore around AI and generative AI in 2023. Co-founder Sam Altman’s recent run in with his board members also shone a harsh spotlight upon the commercial realities of harnessing a breakthrough tech like gen AI – profits and revenue are natural tendencies for any business, but democratization of technologies like AI is also a sexy idea that aligns well with organizational CSR ambitions and has potential to yield real good.

Sectors with high levels of technology dependence and complexity like technology services, retail, financial services, and business services like marketing, fared much better on Cisco’s AI readiness index

You could say, Open AI tools’ wonderfully low barrier to entry and easily demonstrable capability to generate visuals and ideas/text, was what fuelled its hype and adoption since last year till this day.

Moving forward, it’s time to hunker down and think about ethics, frameworks, and guard rails that reduce risks of AI hallucinations, algorithmic bias, worker displacement, and even the appearance of Loab. 

Even governments have done so, and APAC is quick to produce AI governance and strategy frameworks.

This is especially critical if AGI or Artificial General Intelligence is one of the end goals.

Whether for profit or social benefit, Capgemini confirms that  AI is being highlighted as a key focus when it comes to technology investment. Almost 9 in 10 organizations plan to focus on AI, including generative AI, within the next 12–18 months. 

Revisit and reuse

We have actually been down this AI path before. This time around, there is an opportunity to see how fast the landscape has evolved with more data and increased availability of large datasets that enable AI models to be refined further.

Besides the promise of more seamless workflows in areas like customer operations, content creation, sales, and software development, Dell APJ’s Peter Marrs also thinks organizations are relooking how they deploy AI and with privacy and security this time.

Technology players the likes of networking provider Cisco to hybrid cloud player Nutanix, agree that AI is a fundamental driver of enterprise evolution. Cisco’s Hana Raja says, “Every business needs to think about how they can fully leverage it to unlock future business opportunities and benefits,” while Nutanix cautions that AI adoption is a marathon, not a sprint.

“At the heart of this trend is a fundamental understanding among most enterprises regarding the need to explore AI technologies and solutions to maintain a competitive advantage,” said Aaron White, its VP.

Besides the enterprise, AI is demonstrating commendable benefits in the healthcare sector, and when we spoke to Siemens’ Healthineers’s Virginia Chan, the edge it offered in terms of shorter time to diagnose and report was clear. The use case for AI to remotely monitor patients after surgery is also exciting because of the potential to drastically reduce costs incurred upon time, dollars, and care resources. 

AI-driven healthcare robots like Grace can also take temperature, measure the patient’s responsiveness, and diagnose patients.

Unity Technologies’ Marc Whitten had pointed out that AI has played a role in game development way before ChatGPT came onto the scene, and is widely used to accelerate elements of graphics productions, train simulations though machine learning, and automate testing and repetitive tasks. Future use cases like content creation to runtime inference are “…already taking shape” and he alluded to generative AI being used to create game content like characters, terrain, lighting, and audio, automatically.

“These tools can help accelerate productivity and enhance and supplement existing workflows, as well as enable previously unthinkable permutations during game play,” said Marc adding that Generative AI may soon be able to power more lifelike NPCs (non-player characters) that behave with more adaptability and intelligence.

AI Readiness: culture, talent, data

Interestingly, Cisco’s AI Readiness Index study, discovered that sectors heavily reliant on personalized care, or services delivery, were lagging. These include Media and Communications, Education, Healthcare, Restaurant Services, Travel Services, and Transportation.

Sectors with high levels of technology dependence and complexity like technology services, retail, financial services, and business services like marketing, fared much better.

Using artificial intelligence for IT operations (AIOps), more specifically to manage infrastructure, is still the domain of only a handful of organizations, says IDC, due to “underlying issues in basic data management to feed the AI models.”

When speaking to Dell Technologies’ Director of Partner System Engineering in APJ, Sidharth Joshi, he shared that of the 28 solutions workshops they conducted for their partner ecosystem in the past few quarters, AI has been the most popular topic of engagement.

Sidharth opined, “Organizations interested in AI success should focus on fostering expertise in data literacy, understanding algorithmic principles, and promoting interdisciplinary collaboration to build a comprehensive understanding of AI.”

Is there currently an overfocus on machine learning models, CXpose had asked Sidharth.

Companies must also prioritize data quality, infrastructure, security, and ethical considerations, after which they need to conduct ongoing data engineering and MLOps when the model is deployed into production, he shared.

“As part of our AI strategy, we actively develop and maintain an ecosystem of AI solution partners, ranging from silicon providers to model/data set curators and relevant software stack component providers in AIOps, MLOps and data management.”

As of 2020, five countries in the Southeast Asia region have developed national AI strategies, or announced their intention to do so. More countries are expected to follow suit

AI ethics and governance are global best practices to ensure responsible use of AI. They also establish accountability frameworks to promote much needed trust in using AI systems.

Hana Raja stated that companies building AI applications could also leverage existing frameworks as they too think about embedding security, privacy, and trust-by-design processes throughout their innovation lifecycle and its application in products, services, and enterprise operations.  

A team that is not afraid to provide honest feedback has a greater chance of voicing out and surfacing ways for it to co-exist in harmony and productivity with technologies like AI and generative AI.

Shadow AI in organizations also needs to be dealt with to promote further trust in AI. Wise AI’s David Lim said, ‘Tackling Shadow AI is also a solid initiative to educate employees on how to use these tools best and where not to use them.”

Shadow AI and a safe space to unleash innovation 

A large number of MNCs, including tech companies, have announced job cuts since last year. The most recent round came with an unexpected revelation – four in 10 respondents surveyed cited AI as the reason for layoffs. Major tech companies like Dropbox, Google, and IBM have already announced job cuts as part of a new focus on AI, even as a PwC survey uncovered that one in four CEOs think generative AI will lead to displacement of at least 5% of their workforce this year.

IMF chief, Kristalina Georgieva, shared during the recent World Economic Forum in Davos that almost 40% of jobs globally are exposed to AI.

Amidst all this gloomy news, Kristalina also weighed in with the view that, “AI will affect almost 40 percent of jobs around the world, replacing some and complementing others. We need a careful balance of policies to tap its potential.”

Being proactive in adoption of technologies like AI can actually enable the evaluation and enhancement of existing approaches to work. David Lim said, “Examining why employees experiment with specific tools can surface unarticulated technology needs. It can become an excellent opportunity for IT to explore and adopt new tools and services that allow them to complete their work.”

Without a change management plan and investing in employees to get onboard with the changes, “…you will find yourself in a place where employees are hesitant to use and deploy AI tools,” according to Cisco.

Last year, CXpose.tech tapped into psychological safety expert, Peter Brace, to learn how fast moving innovation can impact the overall wellbeing and productivity of employees. An environment where teams feel safe to speak up about what works and doesn’t, is fundamental to their resilience to change and challenges.

A team that is not afraid to provide honest feedback has a greater chance of voicing out and surfacing ways for it to co-exist in harmony and productivity with technologies like AI and generative AI.

The existence of Shadow AI in organizations seems at odds with the picture painted by various reports of AI displacing workers. Ironically, it isn’t a battlefield where AI is being pitted against humans.

The ones to thrive alongside technology, are the ones who realize it the soonest.