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2024 AI Outlook: 4 Trends AI Experts Are Talking About

Dan O'Keefe, Appian
January 3, 2024

2023 was a breakout year for artificial intelligence. It dominated news headlines as well as LinkedIn feeds. But its impact goes beyond the professional—I often overhear conversations at coffee shops about AI from people who aren’t knee-deep in the field. Whoever you are, AI is likely having a transformative impact on your life.

While AI has been around for decades, it took the seismic release of ChatGPT to widely showcase the power of natural language processing, machine learning, and generative AI and get the world to pay close attention. But what future trends do enterprise organizations need to know about? And how do businesses across industries translate the promise of artificial intelligence into real-world results? 

To answer these questions, we reached out to eight experts on the forefront of AI. Below are four key findings from their responses.

[Get the 2024 AI Outlook for a full commentary from leaders at KPMG, RSM US, Deloitte, Amazon Web Services, and more.]

1. Strong data is critical.

Artificial intelligence is built on data. Strong data leads to great outcomes; poor data yields poor results. Like the old adage goes, “garbage-in, garbage-out.” Without a good foundation of data, the output of an AI model could make unfounded predictions, miss critical information, or generate false information—aka, hallucinations. 

As Piyush Kumar from Wipro puts it, “You need the right data to get the output or results you want. How do you cleanse that data? How do you prepare that data? How do you perform feature engineering on top of that? All that is critical for driving results and proving value with confidence.” 

Unfortunately, many organizations maintain scattered, disparate data silos. These barriers make it difficult for both humans and AI to access and use data. For example, training a customer service chatbot on your own data can help you provide quick, accurate answers to customer inquiries. But if information is scattered across multiple systems, you may not be able to effectively train the AI chatbot in the first place. A data fabric can help here.

Data fabric answers the problem of siloed data elegantly by enabling you to access data across multiple systems without heavy data migration projects or the long-term costs of developing and maintaining integrations. And if you’re looking for a platform solution to AI and data, keep in mind that the best process platforms come with a built-in data fabric architecture.

[Want an in-depth look at data fabric? Get The Data Fabric Guide: De-Silo Your Data for Rapid Innovation.]

2. Look for buy-in from upper management.

Another common refrain was the need for C-suite or executive leadership involvement. Hasit Trivedi, CTO of Tech Mahindra, put it nicely when he said, “The success of a project hinges on obtaining management’s alignment on a strategic plan. This alignment should stem from a top-down approach, unless there are remarkable instances of bottom-up leadership.” 

Bottom-up leadership can be effective in some instances, but it usually leads to small-scale projects and limited results. Transforming into a formidable AI enterprise calls for deep cultural shifts, requiring you to marshall resources across the organization. 

For instance, many employees harbor fears about AI taking their job (and frankly, the media often stokes these anxieties). This can lead to employees dragging their feet when it comes to AI adoption. To combat this, leadership teams must steward their organization with proper change management—and communication to allay these fears. 

The future will hinge on mixed autonomy—with humans and AI working together. But it will take organizational leadership to bridge this gap, whether that involves reskilling the workforce, setting project budgets, or fostering wider adoption.

3. Have strong, clear use cases.

Luckily, it’s easier now than ever to get upper management buy-in. With all the AI fervor, many organizations have mandated the application of AI—but doing so strategically and with the right use cases is crucial. 

When talking about top-performing organizations, Todd Lohr, Principal for KPMG LLP, said, “They don’t view it as a hammer looking for a nail . . . They stay true to their business strategy and use AI as an accelerant.” 

Artificial intelligence is a powerful tool, but it still must be used properly. Tie your AI use to a wider strategic goal. This applies across a range of industries—whether it’s a life sciences organization using artificial intelligence technologies for drug development, a financial institution adopting AI for fraud prevention, or a manufacturing organization using it for detecting product defects. As George Casey from RSM US LLC mentioned, “I’ve yet to find an industry that couldn’t better leverage data to remove uncertainty or reduce the time it takes to make decisions.”

4. Emphasize strong governance.

All technology requires strong governance—and artificial intelligence is no exception. The flaws in AI technologies can open companies up to large risks such as improper information access or hallucinations. The experts in the 2024 AI Outlook shared some important guardrails to put in place. 

For example, Frank Schikora, CTO of Roboyo, covered information access. Large language models (LLMs) usually answer any question, presenting risks around sensitive information. As Schikora put it, “You don’t want employees asking an LLM about the CEO’s salary, then having it answer or even hallucinate parts of an answer.” Take care to prevent employees from accessing answers they shouldn’t. Enlist your security team to ensure appropriate permission restrictions. 

AI hallucinations—where generative AI confidently makes up an answer or adds artifacts that are incorrect—poses another potential risk. For instance, if you use AI to summarize vast swaths of information, you don’t want it introducing errors in any report it generates. To prevent this, build in traceability. In other words, have the AI model tell you how it reached its conclusions. Citing an article that contributed to the answer allows the user to verify information—and helps prevent accidental plagiarism or IP theft.

Want even more insights from AI experts?

These are only four of the takeaways the experts highlighted in our conversations. Get the full 2024 AI Outlook to read more in-depth perspectives from leaders, including: 



  • Amazon Web Services

  • Roboyo

  • Wipro

  • Xebia

  • Deloitte Consulting LLP

  • Tech Mahindra

They cover a wide range of topics, including reskilling the workforce, AI use cases, maturity levels across industries, and risks—all to help you navigate the rocky and ever-changing terrain of the AI economy.