Partner content: When duct tape Isn’t the answer to everything
The world has gone mad with this new era of Generative AI (GenAI). Picture a world where duct tape is the answer to every dilemma. Broken glasses? Duct tape. Car won’t start? Duct tape. Heartbreak? You guessed it – duct tape. The current enthusiasm for GenAI is much the same. The buzz is deafening, and the innovations are groundbreaking. But, as with our duct tape scenario, it’s crucial to understand that GenAI, while transformative, isn’t the fix for every issue under the sun.
GenAI has indeed revolutionised the tech landscape, offering new ways to generate content, simulate scenarios, and even shift mindsets about what Artificial Intelligence (AI) can achieve. Its capabilities can bring immense value, yet it’s not the panacea for every technological challenge we face today. For instance, while GenAI can craft eloquent prose or realistic images, it’s not going to fix your database issues or optimise your network performance on its own.
In a world enamoured with GenAI, technologies like deep learning and neural networks, which once seemed cutting-edge, are now being dubbed as “traditional” or “classic” AI. It’s akin to calling a smartphone from five years ago a “vintage” device. This shift in terminology reflects how fast the AI landscape is evolving, but also highlights the need for a balanced perspective on AI tools.
GenAI has undeniably raised the overall interest and willingness to use AI across various industries. However, its true potential often shines brightest when it’s part of a broader, integrated solution. Specialised AI capabilities such as anomaly detection, predictive analytics, and recommendation systems can significantly enhance the functionality of larger systems. By focusing GenAI on specific areas within a wider framework, businesses can achieve far more comprehensive and valuable outcomes.
Innovation to essential business value
Transitioning from viewing AI as a mere source of innovation to recognising it as a critical driver of business value remains a significant challenge for many companies. Unlike traditional software implementation projects, which are often linear and predictable, AI projects require a different mindset. This transition demands agility, openness to experimentation, and the ability to pivot quickly when an idea doesn’t pan out. Embracing this new way of working is crucial for businesses aiming to harness the full potential of AI.
One of the primary hurdles is that the unfamiliar terrain of AI often tempts businesses to revert to their well-worn habits, leading to unnecessary reinvention. Leveraging existing solutions and customising them to fit specific organizational needs can expedite the process of demonstrating AI’s value. This strategic alignment ensures that AI projects are not just technologically sound but also commercially viable.
Companies must accelerate this transformation process or risk falling behind in a rapidly evolving landscape. As AI continues to evolve and mature, the gap between early adopters and laggards will widen. Those who fail to integrate AI effectively into their business models may find themselves outpaced by competitors who have successfully made AI an integral part of their strategic arsenal. Therefore, embracing AI, not just as a tool for innovation, but as a cornerstone of business value, is imperative for long-term success.
Building value-driven AI solutions
At Celfocus, we understand that creating AI solutions that deliver real business value is a complex but rewarding endeavour. Our approach, honed through years of experience, spans from building a solid business case to deployment and continuous improvement. Here’s how we do it:
Figure 1 – Building value-driven solutions from business case to deployment and continuous improvement.
- Use Cases Won’t Take You Far, But Business Cases Will
Creating value-driven AI solutions, rather than just data-driven ones, requires starting with a discussion of initial ideas to validate if AI, including GenAI, is suitable for specific needs and to determine the potential return on investment. This approach ensures that AI initiatives are aligned with business goals and deliver tangible value.
We use a proprietary Celfocus tool to guide AI strategy from use cases to business cases, maximising the value of AI and prioritising development roadmaps. This tool includes relevant indicators from our extensive experience and can be further customised with client-specific metrics to more precisely determine the value of each use case for a particular client.
The business cases developed using this approach have yielded significant benefits, including a 20% improvement in Net Promoter Score (NPS) for customer retention, a 28% improvement in first-time resolution with a 2-5% reduction in field service costs, and a 15% increase in revenue for marketing and sales initiatives.
2. Data Alone Won’t Make You AI-Ready
Having data alone doesn’t make an organization AI-ready. True AI readiness requires more than just data; it involves preparing the necessary infrastructure, ensuring data quality, and implementing robust data governance. Many companies are not AI-ready because they lack some of these essential components. Effective AI readiness also entails integrating AI technologies into business processes and fostering an environment that supports continuous learning and innovation.
Celfocus has over 20 years of experience delivering a wide range of data platforms, including data warehouses, data lakes, lakehouses, and more, all with real-time processing capabilities. Our expertise covers crucial aspects such as data quality, data governance, and multi-cloud environments, ensuring that our clients are fully prepared to implement and scale AI solutions effectively.
We have deployed several high-volume data solutions. Some of our projects include building a consolidated data platform from five data warehouses resulting from mergers and acquisitions, integrated with over 70 data sources. We’ve also developed near real-time analytics platforms, processing more than 4TB of events daily and managed a system that processes 2.5 billion lines daily, with over 5,000 pipelines.
3. Don’t Waste Time Reinventing the Wheel
Having established frameworks is crucial for accelerating delivery times in AI solutions. These frameworks provide a proven foundation, reducing the need for unnecessary reinvention and allowing teams to focus on innovation and customisation. By leveraging these pre-built structures, organisations can ensure efficiency, reliability, and consistency in their AI solutions, leading to faster deployment and quicker realisation of benefits. Frameworks also help in maintaining high standards of quality, as they incorporate best practices and lessons learned from previous projects.
At Celfocus, an extensive library of frameworks is available, enabling the delivery of robust solutions that effectively meet clients’ needs. This library includes both generic frameworks, applicable to a multitude of use cases, and domain-specific ones tailored to particular industries or business challenges. By using these well-established assets, Celfocus ensures that each solution is not only technically sound but also aligned with the specific requirements and goals of our clients. This approach maximises efficiency, reduces time to market, and ensures the successful implementation of AI initiatives.
Figure 2 – AI frameworks to accelerating delivery time
4. Don’t Let Your Data and Models Control You
MLOps (Machine Learning Operations) and DataOps (Data Operations) are essential for building effective AI solutions. They ensure the efficiency, scalability, and reliability of machine learning models and data pipelines, facilitating continuous delivery and automation. These practices streamline workflows and enhance collaboration between teams, preventing issues like data inconsistencies and model degradation, ultimately leading to robust and adaptable artificial intelligence systems.
MLOps and DataOps are integral to our AI strategy. Our Cloud Centre of Enablement aligns multicloud practices with hyperscalers and covers analytics and cognitive services to ensure effective operations. By applying best practices from DevOps to AI and data workflows, we achieve continuous integration, delivery, and monitoring of AI models. This approach guarantees that our AI solutions remain cutting-edge and perform optimally in dynamic environments.
5. Watch your budget to avoid unpleasant surprises
FinOps (Financial Operations) is a critical aspect of managing the costs associated with setting up and maintaining AI solutions in the cloud. Effective FinOps practices bring transparency to cloud expenses and enable immediate reactions to unexpected cost changes. By focusing on cost predictability, resource optimisation, and cost control, FinOps ensures that AI initiatives remain financially sustainable and do not lead to unpleasant budget surprises.
Celfocus Cloud Centre of Enablement also extends to FinOps strategies. Our approach has proven to deliver significant benefits, such as cost reductions of around 35-40%. This comprehensive strategy ensures that our AI solutions are not only innovative but also cost-effective, helping our clients achieve their financial and operational goals.
Conclusion
In conclusion, while the GenAI frenzy is fun and filled with potential, it’s important to remember that it’s one tool among many. Embracing a more holistic approach to AI—leveraging both the new and the classic—will ultimately lead to more innovative and effective solutions. So, let’s enjoy the ride, but keep our toolkit diverse and our expectations grounded in reality.
Know more at celfocus.com
About the author
Carla Penedo is the Director of Offer Development & Innovation at Celfocus, a European high-tech system integrator company dedicated to creating business value through analytics and cognitive. Carla specializes in providing data-driven technological solutions to accelerate digital network transformation, enhance and monetise business services, and deliver highly personalised customer experiences.
With over 20 years’ experience delivering high-value transformative data solutions, Carla has a track record in telecommunications, financial services, energy, and utilities sectors. She has in-depth knowledge and expertise in Analytics, Big Data, and AI & Machine Learning technologies. Carla holds an MBA and a degree in Computer Science with a focus on Artificial Intelligence.