What does this mean in terms of the global economic impact of AI and GenAI? In a 2023 report, McKinsey estimated GenAI could add the equivalent of $2.6 trillion to $4.4 trillion annually across a variety of use cases. That’s comparable to the entire GDP of the United Kingdom in 2021. This impact would increase the overall influence of artificial intelligence by 15% to 40%.

Considering these economic implications, SAS and Coleman Parkes targeted 1,600 decision makers across key global markets. Respondents work in a range of industries including banking, insurance, the public sector, life sciences, health care, telecommunications, manufacturing, retail, energy and utilities, and professional services. The smallest organizations surveyed employed a workforce of 500 – 999 people, and the largest employed more than 10,000.

Learn more in the full research report and an interactive data dashboard.

“While China may lead in GenAI adoption rates, higher adoption doesn’t necessarily equate to effective implementation or better returns,” said Stephen Saw, Managing Director at Coleman Parkes. “In fact, the US nudges ahead in the race with 24% of organizations having fully implemented GenAI compared to 19% in China.”

Global regions charge ahead with GenAI

Highlights from the global survey results include indicators that signal different regions are on board and starting to adopt GenAI in meaningful ways but at different rates.

“With any new technology, organizations must navigate a discovery phase, separating hype from reality, to understand the complexity of real-world implementations in the enterprise. We have reached this moment with generative AI,” said Bryan Harris, Executive Vice President and CTO at SAS. “As we exit the hype cycle, it is now about purposefully implementing and delivering repeatable and trusted business results from GenAI.”

Where do regions rank in fully using and implementing generative AI into their organization’s processes?

• North America: 20%
• APAC: 10%
• LATAM: 8%
• Northern Europe: 7%
• South West and Eastern Europe: 7%

 Which regions have implemented GenAI use policies?

• APAC: 71%
• North America: 63%
• South West and Eastern Europe: 60%
• Northern Europe: 58%
• LATAM: 52%

To what extent do those planning to invest in GenAI in the next financial year have a dedicated budget?

• APAC: 94%
• Northern Europe: 91%
• South West and Eastern Europe: 91%
• North America: 89%
• LATAM: 84%

Note: North America comprise the United States and Canada; LATAM includes Brazil and Mexico; Northern Europe includes United Kingdom/Ireland, Sweden, Norway, Finland, Denmark; South West and Eastern Europe is France, Germany, Italy, Benelux, Spain and Poland; and APAC encompasses Australia, China, Japan and the United Arab Emirates/Saudi Arabia.

Industries and functional divisions embrace GenAI at varying rates

Sabine VanderLinden, CEO and Venture Partner, Alchemy Crew, sees much potential for industries investing in GenAI. “The future of business is being reshaped by generative AI,” she said. “Indeed, the integration of GenAI into business processes – from dynamic profiling in marketing to precision claims insurance – offers unparalleled opportunities for efficiency, personalization, and strategic foresight. Embracing this technology is essential for staying ahead in a highly uncertain and unpredictable competitive market.”

When split into industry segments, the data shows banking and insurance leading other industries in terms of incorporating GenAI AI into daily business operations across a variety of metrics. Highlights from those findings are below.

How do specific industries rank in terms of fully implementing GenAI and fully implementing it into regular business processes?

• Banking: 17%
• Telco: 15%
• Insurance: 11%
• Life sciences: 11%
• Professional services: 11%
• Retail: 10%
• Public sector: 9%
• Health: 9%
• Manufacturing: 7%
• Energy and utilities: 6%

Which industries indicate they already use GenAI daily to some extent?

• Telco: 29%
• Retail: 27%
• Banking: 23%
• Professional services: 23%
• Insurance: 22%
• Life sciences: 19%
• Health care: 17%
• Energy and utilities: 17%
• Manufacturing: 16%
• Public sector: 13%

Which departments inside organizations are using or planning to use GenAI?

• Sales: 86%
• Marketing: 85%
• IT: 81%
• Finance: 75%
• Production: 75%

Early adopters are finding plenty of obstacles in using and implementing GenAI

No. 1 on the list of challenges organizations face in putting GenAI to routine use is the lack of a clear GenAI strategy.

Only 9% of leaders responding to the survey indicate they are extremely familiar with their organization’s adoption of GenAI. Of respondents whose organizations that have fully implemented GenAI, only 25% say they are extremely familiar with their organization’s GenAI adoption strategy. Even those decision makers responsible for technology investment decisions aren’t familiar with AI – including those at organizations that are ahead of the adoption curve.

Nine out of 10 senior technology decision makers overall admit they don’t fully understand GenAI and its potential to affect business processes. At 45%, CIOs lead the way with executives who understand their organization’s AI adoption strategy. But only 36% of Chief Technology Officers (CTOs) say they’re fully in the know.

Yet despite this understanding gap, most organizations (75%) say they have set aside budgets to invest in GenAI in the next financial year.

Other challenges organizations face include:

• Data

As organizations adopt GenAI, they realize they have insufficient data to fine tune large language models (LLMs). They also realize – once they’re deep into deployment – they lack the appropriate tools to successfully implement AI. Organizations’ IT leaders are mostly concerned about data privacy (76%) and data security (75%).

• Regulation

Only a tenth of organizations say they are fully prepared to comply with coming AI regulations. One third of organizations that have fully implemented believe they can comply with regulations. Only 7% are providing a high level of training on GenAI governance. And only 5% have a reliable system in place to measure bias and privacy risks in LLMs.

Although there are obstacles, some early adopters have experienced meaningful benefits already: 89% report improved employee experience and satisfaction; 82% say they’re saving operational costs; and 82% state customer retention is higher.

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“Our customers are enthusiastic about the potential of AI but remain cautious about when and how to use it,” said Reggie Townsend, Vice President, SAS Data Ethics Practice. “They’re asking good questions about responsible and ethical AI. Our goal is to give them the tools and guidance, based on decades of experience, to integrate AI in ways that boost profitability while reducing unintended harm.”

Model cards: Trustworthy AI’s ‘nutrition labels’

It can be difficult to take something as complex and sophisticated as an AI model and convert it into something easily digestible for everyone involved in the AI life cycle. And as new rules and regulations are passed around the world, the ability to understand and share with regulators how a model is performing will be crucial. Model cards, an upcoming feature in SAS^® Viya^®, will serve stakeholders across the AI life cycle. From developers to board directors, all stakeholders will find value in a curated tool that supports proprietary and open source models.

Releasing mid-2024, model cards are best described as “nutrition labels” for AI models. The SAS approach is to autogenerate model cards for registered models with content directly from SAS products, removing the burden from individual users to create them. Additionally, because SAS Viya already has an existing architecture for managing open source, model cards will also be available for open source models, starting with Python models.

Model cards will highlight indicators like accuracy, fairness and model drift, which is the decay of model performance as conditions change. They include governance details like when the model was last modified, who contributed to it, and who is responsible for the model, allowing organizations to address abnormal model performance internally. The model usage section addresses intended use, out-of-scope use cases, and limitations, which will be key factors as transparency and model auditing likely become regulated business operations. Model cards were showcased earlier this year at SAS Insight, a conference for analysts.

“SAS is taking a thoughtful approach to how it helps customers embrace AI, focusing on the practical realities and challenges of deploying AI in real industry settings,” said Eric Gao, Research Director at analyst firm IDC. “Model cards will be valuable for monitoring AI projects and promoting transparency.”

New AI governance group led by ethical AI veteran

With the proliferation of AI, SAS customers have become increasingly concerned with how to use their data in ways that are both productive and safe. To help them on their data and AI journeys, SAS is launching AI Governance Advisory, a value-added service for current customers.

Beginning with a short meeting, SAS AI Governance Advisory will help customers think through what AI governancemeans in the context of their organizations. SAS has piloted this service, and customers have noted several benefits:

• Increased productivity from trusted and distributed decision making.
• Improved trust from better accountability in data usage.
• The ability to win and keep top talent who demand responsible innovation practices.
• Increased competitive advantage and market agility from being “forward compliant.”
• Greater brand value for confronting the potential impacts to society and the environment.

PZU Insurance of Poland is one of the largest financial institutions in central and eastern Europe. A longtime SAScustomer, PZU deploys AI in areas such as claims, sales, fraud detection and customer care.

“Our AI governance conversations with SAS helped us consider potential unseen factors that could cause problems for customers and our business,” said Marek Wilczewski, Managing Director of Information, Data and Analytics Management (Chief Data Officer/Chief Analytics Officer) at PZU. “We better understand the importance of having more perspectives as we embark on AI projects.”

Industry veteran and ethical AI expert Steven Tiell has been hired as SAS Global Head of AI Governance. Tiell, who led Accenture’s global data ethics and responsible innovation practice, is also the former Vice President of AI Strategy at DataStax.

Building on emerging government standards 

Last year, the US National Institute of Standards and Technology (NIST) launched an AI Risk Management Framework. It has become a valuable tool for organizations to design and manage trustworthy and responsible AI in the absence of official regulations.

SAS has created a Trustworthy AI Life Cycle workflow, which makes NIST’s recommendations easier to adopt for organizations by specifying individual roles and expectations, gathering required documentation, outlining factors for consideration, and leveraging automation to ease adoption. Organizations end up with a production model with documentation showing that they did due diligence to help ensure that the model is fair, and their processes do not cause harm.

The workflow allows organizations to document their considerations of AI systems’ impacts on human lives. It includes steps to ensure that the training data is representative of the impacted population and that the model predictions and performance are similar across protected classes. These steps help ensure that the model is not causing disparate impact or harm to specific groups. Furthermore, users can ensure that a model remains accurate over time by creating human-in-the-loop tasks to act when additional attention is needed.

The SAS Trustworthy AI Life Cycle workflow can be downloaded from the SAS Model Manager Resources Githuband will soon be available through the NIST AI Resource Center.

Today’s announcement was made at SAS Innovate, the data and AI experience for business leaders, technical users and SAS Partners. Keep up with the latest news from SAS by following @SASsoftwareNews on X/Twitter.

About SAS

SAS is a global leader in data and AI. With SAS software and industry-specific solutions, organizations transform data into trusted decisions. SAS gives you THE POWER TO KNOW®.

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While not the first to offer models to the market, AI software provider SAS is uniquely positioned to flex its decades of expertise in applying scalable and trustworthy AI models for prominent global financial, health care, and manufacturing brands, as well as government institutions.

“SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,” said Chandana Gopal, Research Director, Future of Intelligence, IDC. “An area that is ripe for SAS is productizing models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

With the new offering, organizations can rapidly deploy the models – which are distinct from foundational large language models – across the entirety of their environment to address specific industry challenges for fraud detection and prevention, supply chain optimization, entity management, document conversion, health care payment integrity, and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organizations to operationalize trustworthy AI technology and accelerate the realization of tangible benefits and trusted results.

Also read: Cheil’s Experience Commerce Bags Digital Agency Mandate for SAVSOL, India’s Leading Engine Oil Brand

Expanding market footprint

Organizations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organizations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AI-powered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decadeslong focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

“Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem,” said Udo Sglavo, Vice President for AI and Analytics, SAS. “By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive environments.”

Bringing AI to the masses

SAS is democratizing AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimizationeveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimized workflows seamlessly and aiding in faster planning decisions.

“SAS Models provide organizations with flexible, timely and accessible AI that aligns with industry challenges,” said Sglavo. “Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year. Learn more about SAS Applied AI and Modeling here: https://blogs.sas.com/content/tag/applied-ai-modeling/.

About SAS

SAS is a global leader in data and AI. With SAS software and industry-specific solutions, organizations transform data into trusted decisions. SAS gives you THE POWER TO KNOW®.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2024 SAS Institute Inc. All rights reserved.

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With 2023 drawing to a close, the leaps in martech and adtech – not least, in artificial intelligence (AI) – give professionals in the field food for thought. While the prospect of expanding reach and growing customer base is inviting, businesses must grapple with the proliferation of online platforms, larger and more varied data sources, as well as multichannel customer interactions. But one thing is clear; as the martech landscape shifts, content strategy must be adaptable – and these five trends will shape the martech and adtech verticals in the coming year. 

1) Differentiation Through Hyper-Personalisation 

The days of generic campaigns have long been resigned to the dustbin of history. In fact, a McKinsey survey found that 78% of respondents were more likely to repurchase from a brand that delivered personalised content.  

And with Google set to gradually disable cookies starting in 2024, harnessing the power of first-party data to truly understand customers is a no-brainer. This can be further augmented with AI – which will further boost customer loyalty. 

At the same time, businesses in the Asia Pacific need to remember that committing to strategically putting the customer first must be supplemented with adequately formalised and efficient measurements to gauge performance over time. 

2) The AI Floodgates Have Well and Truly Opened 

The past 12 months saw AI free itself from the confines of labs and hit the mainstream – with call centres and chatbots among the first use cases to emerge. And 2024 will only see AI expanding into more areas, with headway in the technology’s language and sentiment capabilities set to increase customer satisfaction. 

Also keep an eye out for chatbots gathering user preferences – a.k.a zero-party data – to make recommendations, crossing the divide from service support to conversational marketing applications. The second wave of AI usage will see the technology cement its mainstream credentials, with techniques encompassing personalised shopping experiences on websites and embedded AI on platforms such as mobile phones and wearable devices. 

However, AI will also be experienced differently across the region. The Asia Pacific is highly diverse, and while poised for growth, maturity varies widely. The likes of Singapore and China are by all accounts leading the pack, but this could also be a source of strength as the region explores and experiments. 

3) It’s An Exciting Time For Productivity 

The remarkable feats of generative AI and its subsequent widespread adoption have been quite revolutionary. This explains why it took just two months for OpenAI’s ChatGPT to gain 100 million users. 

Expect this momentum to carry over into 2024, with marketers on the brink of experiencing real and tangible benefits from this technology, particularly in terms of productivity and creativity enhancement. In no small part, this is because the incoming wave of AI-powered assistants is poised to automate repetitive marketing tasks, facilitate data analysis and utilisation, and streamline campaign and customer journey management.  

With professionals and experts given unprecedented freedom to focus on tasks that truly matter, we’re on the cusp of a new era of efficiency and creativity. 

Also read: Experience the Pinnacle of Dessert Luxury at Gold by Ice Cream Works, Now Open in Bandra

4) Governance To Be Put to the test 

Many organisations are in need of a re-consideration of their data governance, security, and privacy practices. Fuelling this is data deprecation and the new emphasis on zero- and first-party sources, as well as second-party sources such as data clean rooms, networks, and exchanges. Here’s where the wheat separates from the chaff, because some brands have instituted comprehensive data compliance practices, while others lag behind.  

It’s critical to remember the double-edged quality of emerging technology like AI – and there is considerable regulatory zeal. Malaysia, for one, is developing an AI code of ethics and governance, which is slated to be ready next year. Over in Singapore, authorities have launched AI Verify to tether AI testing to 11 internationally recognised principles. This will seriously test organisational data governance practices. Responsible marketing, particularly vis-a-vis data, will be a major factor for brands in 2024. Avoiding the speed bumps to innovation will hinge on using customer and marketing data responsibly, which will hopefully impel brands to take a strong, fresh look at their data processes. 

5) CMO’s Zero In on Tech Aggregation and Complexity 

53% of marketing respondents told Gartner they are too overwhelmed by existing technologies to explore emerging ones. Meanwhile, CMO Council found that integration – between systems and data – as well as technical agility dominated the top concerns in a recent report. Clearly, tech aggregation and complexity are keeping CMOs up at night. 

Now is absolutely the time to take charge and overcome the inability to bring together point solutions into a martech platform solution. What is needed is a pivot towards a multi-channel marketing solution. Ideally, this will be architected atop a cloud-based data store with direct access to data from within its own user interface (UI) and has native capabilities to deliver marketing communications. At the same time, this solution should also offer Out-of-Band (OOB) capabilities that orchestrate journeys to a myriad of other vendors for delivery, and can accommodate third-party decisioning engines for real-time analytically driven decisions and model scoring. While tech stack aggregation may not reduce the number of players in the martech landscape, it will be a much needed boon for technical agility, returns on investments, and, crucially, help CMO’s increase utilisation of existing applications. 

The coming 12 months is shaping up to be a year of exciting trends and advancements that will reshape how businesses engage with audiences. And in the competitive landscape of today, cohesion is key – which is why thriving in 2024 will rest on aligning martech with overall company strategy. The onus will be on marketers to clarify objectives, assess current capabilities, and plug gaps while striving to break new ground. Ultimately, it is this which will propel understanding of and empathy for customers. 

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