‘By the end of 2024 ( and during 2025), value will be largely derived from projects based on familiar AI techniques, either stand-alone or in combination with GenAI, that have standardized processes to aid implementation. Rather than focusing solely on GenAI, AI leaders should look to composite AI techniques that combine approaches from innovations at all stages of the Hype Cycle.’
This is a theme that has been advanced by many who look beyond GenerativeAI pilot mania to actual outcomes
Over the last 60 years, many AI tools have been developed with proven results. Algorithms are nothing new, but the immense growth in computing power, data centers, industry hype, and FOMO has spawned the current flood of GenerativeAI pilots and associated hyperbole. That has masked the value and utility of mature AI tools, for example:
- Extractive AI
- Conversationa lAI
- Predictive AI
- Knowledge Management AI
- Automation AI
- Robotic Process Automation AI (RPA)
- Computer Vision AI
- Advanced Analytics AI
Those are just a few of the tools and applications available and there is a lesson to be learnt from the implementation of RPA where more often than not projects have failed to deliver the outcomes expected. Not for bad technology but rather a lack of commitment, resourcing, planning, change management, and buy-in across the enterprise. In fact, the key reasons many technology projects fail. Even not asking the important questions ‘Why do we need to do this, what should we prioritise, and how can we ensure success?’
‘The sheer volume and scale of AI projects means that AI leaders must expand the focus of AI beyond technical conversations. Organizations would be wise to place considerable attention on areas surrounding AI, like governance, risk ownership, safety, and mitigation of technical debt.’ Gartner
Legacy technology and system silos are a major hurdle to overcome. AI applications need relevant, timely, and accurate data for successful outcomes. Yet too often that step is missed.
See Surveys highlight data maturity holding back AI deployment ambitions for detailed analysis from industry surveys and playbooks for successful data management.
There are data automation AI technologies to find, categorize and label unstructured data hidden in data silos across the enterprise, creating one universal information layer. Applying term matching and contextual awareness shrinks this to the relevant and real-time data required by employees, and the AI tools required to meet departmental and enterprise needs.
Succeed in that and you can decide why, what, and how to choose the optimal combination of AI technologies that will deliver short-term outcomes and holistic strategic outcomes. Beware of choosing technologies for just one tactical outcome or you will end up with duplication and a new set of legacy technology before you know it.
To review different potential use cases read: -
Composite AI represents the next phase in AI evolution. It involves combining AI methodologies — such as machine learning, natural language processing and knowledge graphs — to create more adaptable and scalable solutions.
This approach enables businesses to maximize the impact of their AI initiatives, leading to more accurate predictions, decisions and automations — even within complex environments. Composite AI is particularly powerful compared to singular forms of AI, because it doesn’t rely on a single technique, thus spreading out its points of failure across multiple techniques instead of one.
For example, integrating rule-based systems with machine learning allows enterprises to better handle unstructured data, thereby enhancing their ability to derive insights from diverse datasets. By embracing composite AI, organizations can solve problems that were previously too complex for single-technique AI models to address.
To optimise such deployments needs technology partners that are willing to put the effort into understanding your current business and future plans in order that they can manage POCs, MVPs and viable deployment of the optimal mix of AI technologies discussed above.
This is a good check list provided by Chris Surdak
1. Executive adoption decisions based on something other than FOMO
2. Compelling use cases
3. Honest and complete business case ROI
4. Systems engineering thinking
5. Effective test campaigns
6. User buy-in
7. Understanding of the technology's true capabilities
8. Understanding of the technology's true limitations
9. A back-up plan
10. Self-reflection over prior failures
That will help avoid this problem faced by many CIOs trying to meet the C-suite's FOMO when it comes to GenerativeAI: -
“The enterprise landscape is littered with Version 1.0 generative AI proof of concept projects that did not materialize into business value and have been dumped. While the industry remains in the early stages of uncovering and implementing AI use cases into business workflows, the blueprint for success remains elusive for many CIOs.”
Salesforce CIO Juan Perez
Generative AI (GenAI) receives much of the hype when it comes to artificial intelligence. However, the technology has yet to deliver on its anticipated business value for most organizations. The hype surrounding GenAI can cause AI leaders to struggle to identify strong use cases, unnecessarily increasing complexity and the potential for failure. Organizations looking for worthy AI investments must consider a wider range of AI innovations — many of which are highlighted in the 2024 Gartner Hype Cycle for Artificial Intelligence.
https://www.gartner.com/en/articles/hype-cycle-for-artificial-intelligence
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