Amid rising financial pressure and increasing consumer expectations, business leaders across all industries are turning to AI as the silver bullet to drive greater efficiency, reduce costs, and secure a competitive advantage. No longer seen as just another tech buzzword, today AI is considered a pivotal tool in an organization’s digital armory, with 60% of CEOs expecting generative AI (GenAI), in particular, to improve product or service quality over the next year. As a result, nine-tenths (87%) of C-Suite executives feel pressured to rapidly implement GenAI solutions, at speed and scale.
The excitement surrounding GenAI – known for its ability to create text, images, and other media from simple prompts – is well-founded. It promises to revolutionize content creation, customer service, and numerous other domains. In fact, according to Gartner’s research, global spending on AI is expected to reach £229 billion by 2027, with enterprise applications embedding of GenAI comprising a significant portion of this investment.
However, despite the hype, it is essential to approach GenAI with a balanced perspective. GenAI is one form of AI, and whilst it offers potentially significant opportunities, enterprise adoption is currently somewhat limited. In fact, to date, it delivers low returns for most organizations and many early projects have failed to deliver the expected benefits. Broader forms of “traditional” AI, such as Machine Learning, can be better suited, providing a better ROI and results in more transparent, explainable forms.
With pressure mounting to transform and implement AI rapidly, getting swept up in the promise of GenAI is understandable. However, using it to tick the AI box in your organization is not necessarily the answer – at least not the most effective, safe, and impactful one.
The reality is – the efficiency gains and increased productivity that can be obtained by standalone GenAI platforms are limited in the grand scheme of things. They won’t have a transformational impact on the vast majority of services delivered by organizations across all sectors.
An integrated approach
The true power of AI in the enterprise extends far beyond a few expensive GenAI-driven “co-pilots” assisting knowledge workers with administrative tasks and content generation. The future of AI lies in its seamless embedding within business processes and systems, ensuring that AI capabilities are integrated, not standalone.
Enterprise software applications, known for their high scalability and integration capabilities, offer organizations the perfect solution to AI deployment. In fact, Gartner predicts that by 2027, 70% of GenAI spend will be via these tools.
Customer engagement solutions that can embed GenAI with Enterprise Applications can deliver benefits safely. Such tools can allow simple creation of chatbots and virtual assistants, and provide valuable tools to workers such as content summarization, keyword extraction, sentiment analysis, translation, and text enhancements such as spelling, grammar, and tone of voice.
In addition to enabling a more secure approach, enterprise software applications can also allow businesses to incorporate multiple forms of AI such as pre-trained machine learning (ML), natural language processing (NLP) and AI-powered bots, as well as adjacent technologies such as RPA. ML models allow organizations to gain rich, bias-free insights that can predict future outcomes, whilst NLP can revolutionize omnichannel contact, and boost efficiency, personalization and satisfaction through AI-powered interactions. Meanwhile, RPA can increase customer service teams, and other departments efficiency, by completing mundane, time-consuming tasks that slow them down.
Ultimately, enterprise-wide AI adoption is about creating a cohesive ecosystem where AI enhances every aspect of operations, from customer service to decision-making. This approach ensures that AI tools are not isolated on desktops but are woven into the fabric of the organization’s workflows, driving efficiency and innovation at every level.
Making an impact
In today’s turbulent landscape, where demand for AI expertise is extremely high, organisations face many challenges when trying to build in-house capabilities. Embedding AI technologies with enterprise applications therefore provides a practical approach to AI delivery.
Platform-based application solutions, that utilize low-code technology to build and develop optimized business processes and workflows, are particularly effective in this scenario, offering business-ready AI capabilities that can be deployed simply, safely and at scale.
Deploying AI safely and securely
Whilst the opportunities on offer from successful implementation are vast, there are also the inherent risks associated with AI – and GenAI in particular – that must be considered. Consumers are becoming increasingly aware of the potential pitfalls associated with AI, such as biased algorithms and invasive data collection practices. For organizations in high-risk industries such as education, healthcare, and essential public and private services, the way in which AI is deployed and the controls placed around it is critical.
The journey to successful and safe AI integration in the enterprise requires a nuanced approach, balancing innovation with risk management. While GenAI offers transformative potential, traditional AI and ML solutions continue to provide robust, lower-risk benefits. By adopting AI with enterprise applications, especially those with a platform approach, organizations can harness the power of AI efficiently and securely, navigating regulatory challenges and skill shortages effectively.
To be impactful, AI implementation should be treated as more than just a box-ticking exercise. As it continues to evolve, enterprises that adopt a strategic, well-governed approach will be well-positioned to lead in the digital age.
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