Strategic AI Implementation: Balancing Hype and Reality in Business

How business leaders can deliver impactful change with AI

AI implementation should be treated as more than a box-ticking exercise 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. 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. 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. 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. We've featured the best AI phone.

The big mistake business leaders make with AI (and how to avoid It)

How businesses can ensure the investment in AI is successful After a couple of years of high excitement around the potential of artificial intelligence (AI) to drive results for business, many leaders are now highly impatient to deploy the technology and have great expectations for what AI can deliver. Tech leaders are hopeful that AI can deliver everything from streamlined operations to game-changing improvements in the way the whole organization does business, and planned AI spending is rising 61% this year, according to new research. Business leaders need to maintain a firm grip on reality, and temper their AI enthusiasm with a grounded view of what the business really needs AI to deliver. In the past two years, many companies have invested in AI only to find that their proof-of-concepts have not delivered results. Getting the right results from AI investment requires careful thought beforehand, combined with precise attention to detail during the project itself. The past two years have seen an unprecedented amount of technology hype around the potential of generative AI, so it's all too easy to understand how a business leader could be tempted to ask their IT teams why they are not using generative AI right this second. The problem is that in those businesses, neither the leaders who are swept away in a wave of AI enthusiasm, nor their IT teams, really know how AI can deliver a business advantage. Before rolling out AI, leaders need to be certain that they are doing so for the right reasons (and not just using it because their competitors are). The gap between exciting technology built in the laboratory and the day-to-day reality of business applications is very large, and it's crucial not to fall into the trap of becoming over-excited about technology that has yet to cross that gap. Taking a short-sighted view and moving forward too early is how AI investments end up wasted. Even the very best technology is just a science experiment if it cannot be adopted and used in the real world. The single biggest reason AI 'doesn't work' for businesses is that people try to 'do AI' rather than identifying where problems or inefficiencies exist. To find such problems, business leaders should first talk to partners, and listen to consumers and front-line employees. Does the business lack staff to talk to customers? Does the business need to find a way to cut fuel emissions? Beyond the hype, the real excitement of this technology comes not from thinking about AI as a standalone solution, but by adding AI into the solution to a real business problem. All too often, the approach to AI is to have a specific 'AI team', rather than applying the technology across the whole business. This siloed approach is a key mistake. AI must be integrated with a holistic approach, and a view to scaling it across every part of the business. Business leaders must connect multiple teams together to initially implement the technology, and avoid cutting corners to ensure seamless integration. Business leaders need to design an effective proof-of-concept solution that includes AI appropriately in order to mitigate a business problem, and then scale it accordingly. For example, a generative AI chatbot that can answer niche questions could be made available to a small subset of customers initially, but rolled out to larger groups thereafter. Internal communication is also key as the business benefits of the proof-of-concept must be effectively communicated within the organization, as AI projects often fail to be exciting to leadership until they grow to a certain size. Even experts who have worked in the field for many years were caught by surprise at how the launch of ChatGPT made the pinnacle of AI technology so easy to adopt. This, in turn, made it easy for business leaders to imagine that generative AI should be adopted universally. But they should pause to think about whether such technology is the right choice, or if other forms of AI might do the job better. The enthusiasm around generative AI has meant that it's sometimes used in areas which don't play to its natural strengths. Generative AI is great for conversational user interfaces such as chatbots, knowledge discovery and content generation. It's also highly useful in segmentation and intelligent automation and anomaly detection. For example, one leading UK Industrial AI & IoT technology company used machine learning and computer vision AI technologies to enable its composite manufacturing process to be smoother and greatly reduce anomalies. This demonstrates how AI is already improving manufacturing quality control through various systems that accurately detect defects. Artificial intelligence is already helping organizations to solve real problems in sectors such as retail and manufacturing. AI helps to streamline and speed up processes, eliminating the amount of time spent by employees on mundane tasks. In both retail and manufacturing, computer vision is emerging as an interesting and successful use of AI, linking the physical and digital worlds, and helping to spot defects on production lines and offering valuable insight in retail settings. Computer vision also has an important role in allowing retailers to draw important insights from cameras in retail stores, far beyond simply dealing with theft or similar incidents. One current system is able to offer insights into important trends around what customers are looking at and buying, and to validate the success of promotions. The system can identify everything from misplaced products to how retail media (advertising) within the store is performing in terms of views. In manufacturing, computer vision helps make factories and laboratories more efficient and also safer for employees. For example, computer vision is already helping to conduct quality control checks on products, ensuring they are not missing any components, and monitors the number of products coming off a production line in any time period, also scanning for defects. But even more importantly, new computer vision systems are helping to make factories safer, scanning for smoke and fire, while also detecting accident-prone machinery. With excitement swirling around AI and generative AI in particular, business leaders need to ensure their feet are firmly planted on the ground, and take a sensible approach to the technology. This means focusing on real, tangible problems within the business, and working out how AI can deal with those problems. It's also key to ensure that AI projects are 'woven into' the business effectively: not only should AI integration be closely linked to real-life problems, but the AI project should also be something that as many employees as possible can be 'hands on' with. This sort of holistic, integrated approach is the way to ensure AI projects do not fail in their early stages, and a foundation stone to using AI to gain a true competitive advantage. We've featured the best productivity tool.

The art of strategic GenAI adoption

Have you seen this post on GenAI - now almost an adage of the times? "You know what the biggest problem with pushing all-things-AI is? Wrong direction. I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes." The post is sassy, but spot on. It's a stark reminder that humans want AI to assist with undesirable and painstaking tasks, not to assimilate creativity. Companies may ultimately want the same thing. They need creative productivity from humans, and they need GenAI to deliver the ROI that keeps the financial house in order, rather than falling into the AI money pit. If you think this message is coming from yet another company looking to jump on the GenAI bandwagon, think again. We come at GenAI and data management - which is critical to GenAI success - from a position of unrivaled understanding, experience and commitment. Here's what we've learned, and what to do in transforming your organization with GenAI. Organizations sometimes fail to take the time to define what it is that they want to get out of GenAI. This has led many companies to go too big too fast or proceed with excess caution, or not move forward at all. Everest Group says that 2023 saw more than 1,200 GenAI proofs of concept (PoCs), signaling strong enterprise engagement, but less than 18% of PoCs reach production stage. Gartner adds that growth in 90% of GenAI enterprise deployments will slow by 2025 as costs exceed value. Position your GenAI efforts for success and growth by first defining the problem you are trying to solve. Specify what outcomes you expect. And be selective in using GenAI, because it requires tremendous compute resources and scaled out IT infrastructure that can handle large data sets, so it can get expensive fast. Also keep in mind that consuming a lot of compute and power will have an impact on the planet and your sustainability goals. So, make sure that you're using GenAI in a sensible manner, and only use GenAI to solve problems that couldn't otherwise be solved. If someone is using GenAI to write a poem, there are no right or wrong answers. But mission-critical enterprise applications are going to require near 100% accuracy. If you don't employ high-quality data in your GenAI efforts, you won't get the results that you are expecting. Assess where you are with your GenAI and data strategies. Our recent research indicates that less than half (44%) of organizations have well-defined GenAI policies, and even fewer (37%) believe their infrastructure and data ecosystem are well-prepared for GenAI implementation. Work with data experts to establish and implement robust data management solutions and strategies that address data security and integrity wherever that data may reside. Also, make sure your GenAI strategy positions you to be agile in this fast-moving environment in which there are a lot of acquisitions and consolidation. Plan and build for GenAI in a way that keeps you flexible because what worked a few months ago may not work in the future. Using high-quality data sets is also important considering the growing regulatory scrutiny around AI and GenAI. For example, the European Union's Artificial Intelligence Act went into effect Aug. 1. This applies to any providers that put AI systems into service within the EU. The new EU AI Act calls for AI systems that are classified as high risk - such as systems that are used for energy and transport, medical devices, and systems determining access to education or employment - to implement risk-mitigation strategies. The EU explains that includes achieving high standards of accuracy, cybersecurity and robustness; ensuring human oversight; maintaining activity logs; providing detailed documentation; and using high-quality data sets. But the unprecedented volume and complexity of data environments can make that a challenge. Employ the tools and DataOps processes to understand data lineage, do data cost optimization, and ensure reliability, resilience, and visibility throughout the data lifecycle. GenAI now makes it easy for virtually anybody to put AI to work, which is accelerating the pace of business transformation at an exponential rate. And that's an extremely powerful thing. With GenAI, you can drive more automation and save on costs. GenAI also can enable product differentiation to drive revenues. If you can become more proactive by using GenAI's infinite knowledge and capacity to act quickly, you can fix problems and deliver better solutions faster. But, in the process, GenAI is increasing the storage demands and extending the infrastructure concerns at enterprises far and wide. To stay competitive, modern businesses like yours must establish a data foundation for innovation - allowing your business to run, manage, and harness data and applications wherever they exist - on premises, in the cloud, and/or at the edge. Whether your organization opts to leverage GenAI to save on everyday tasks, build revenue by delivering differentiated services or all of the above, keep in mind that getting GenAI right is both an art and a science. And it requires people and organizations to leverage both the knowledge earned with years of experience and the latest innovations in data management. We've listed the best AI tools.