Get ahead of the competition: How CIOs can Implement an AI Strategy for Success
Artificial Intelligence (AI) is an increasingly important technology that has the potential to bring great value to organizations of all shapes and sizes. For CIOs, developing a successful AI strategy is paramount to capitalize on this opportunity and ensure their organization is not left behind. It’s an exciting and scary time to be a technology leader: Exciting for the endless opportunities offered by rapidly evolving digital technologies — and scary due to the associated feeling of FOMO (fear of missing out).
Driven by the desire to tap unprecedented volumes of data for a broad array of real-world applications, many organizations see AI as a magic wand that CIOs can swing to generate customer delight and executive exhilaration. CIOs know better, of course. The challenges that come with any new technology hit technologists harder and faster than the optimism driving it. This is especially true with AI and related areas such as machine learning (ML), data science, deep learning, natural language processing (NLP), and cognitive intelligence. Not only is talent scarce in these fields, but their vocabulary and application development are also different.
Extracting value from AI is about making a real-world impact with demonstrable quick wins while developing the organization on an enterprise-wide scale.
Here are four key priorities for CIOs looking to make the most of AI:
1. Provide unique products and services using AI-based decisions
One of the most crucial priorities is discerning which business processes would be improved with real-time decisions made by AI. The ability to quickly process contextual information and make on-the-spot choices differentiates products, services, and experiences in a very saturated market.
For example, by incorporating AI into their business processes, insurance firms can automate claims processing for real-time approvals based on pictures and videos provided by the claimant right from the place and time of the incident. Lenders can use AI to analyze risks in real-time based on collateral and background information to offer instant loan approvals. Organizations can also use AI to personalize and customize products and services across various applications.
The key to success is figuring out which areas need the most improvement and ensuring those changes are visible and noticeable to customers. For example, you don’t want to add chatbots just because everyone else has them if they won’t improve customer experience.
2. Use AI engineering/MLOps to make AI work better
According to Gartner research, only 53% of projects using AI make it from prototypes to production. This happens because CIOs and IT leaders lack the tools to manage a production-grade AI pipeline effectively. This is problematic because unless engineering processes are mature enough to create a consistent pipeline of deployable models, businesses cannot take full advantage of Leveraging AI capabilities despite proof-of-concept or investment in research.
CIOs that want to institutionalize AI and ML methodologies must establish a strategy that differs from traditional software engineering. The most effective way many enterprises have found to do this is by establishing a robust platform supported by a governance model. By combining various aspects, from experimenting and designing all the way to deployment, such platforms are incredibly powerful for anyone utilizing them. In other words, it allows Chief Information Officers to focus on the engineering side of AI while still being able to produce business results and a clear roadmap.
3. Choose a cloud-based AI platform to ensure your system is flexible and scalable
A recent survey from McKinsey, The State of AI in 2021, found that high-performance organizations use cloud infrastructure much more than their peers do for AI workloads. 64 per cent of these companies’ AI runs on public or hybrid cloud, compared with 44 per cent at other enterprises. Additionally, this group is accessing a wider range of capabilities on the cloud than others. This last part is key because access to capital for up-front infrastructural investment can be difficult to obtain and one of the most significant deterrents for AI progress in many enterprises.
A practical approach to implementing an AI strategy is starting small, experimenting and then scaling up when the value has been determined. A cloud-based platform helps organisations focus on business goals rather than technology. This “experiment, pilot, and scale” strategy will save you a lot of trouble as you navigate the early stages of your AI journey.
4. Ensure that your company is prepared for the expansion of artificial intelligence
Apart from technological infrastructure, another obstacle to implementing data science and modeling might be the lack of expertise in these domains. The specialized vocabulary and tools may be off-putting or challenging for people outside the core group of experts. Additionally, a lack of understanding of common terminology may also hinder deployable models and interoperability.
The enterprise-level potential of AI can only be reached when the platform is accessible to business users, experts in the domain, and developers from other areas so they can work together to create value-driven AI assets.
In order to make the most of AI, it’s important for businesses to first figure out which areas need improvement and then make those changes visible and noticeable to customers. Additionally, in order to institutionalize AI and ML methodologies, CIOs should establish a strategy that differs from traditional software engineering. A cloud-based AI platform can help organisations focus on business goals rather than technology while ensuring the expansion of artificial intelligence. Finally, another obstacle to implementing data science and modeling might be the lack of expertise in these domains — a problem that can be solved by preparing your company for the expansion of artificial intelligence.
About the Author:
Avinash Velhal, Chief Information Officer, Katonic.ai
Avinash is a business technologist with over 35 years of cross-functional experience in IT across a wide gamut of areas in IT Governance, Compliance management, IT Outsourcing, Infrastructure and Service Management, Software Development, Vendor Management, Business Delivery, and Marketing
He possesses broad competence in strategic management of technical matters with the distinction of launching and driving new IT initiatives, re-designing IT infrastructures, and achieving organizational objectives. Managed a major IT integration across 8000+ employees across 7 metros across India in coordination with the Global teams.
Avinash is also an effective communicator with exceptional analytical, technical, negotiation, and relationship management skills with the ability to relate to people at any level of business and management.
