{"id":1874,"date":"2024-11-25T07:00:19","date_gmt":"2024-11-25T07:00:19","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1874"},"modified":"2024-11-23T14:30:11","modified_gmt":"2024-11-23T14:30:11","slug":"are-you-ready-for-ais-realities","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/are-you-ready-for-ais-realities\/","title":{"rendered":"Are You Ready for AI\u2019s Realities?"},"content":{"rendered":"

From conference stages to office corridors to political campaigns, there\u2019s no denying it, AI is the obsession of the world right now.<\/p>\n

I read this last week that Nvidia\u2019s staggering $35bn quarterly revenue, obviously fuelled by the generative AI boom, is proof that businesses are throwing their weight behind the chips that will accelerate development and innovation of the next AI tools.<\/p>\n

But there is a bigger question here, are companies prepared for what it takes to make AI work for them?<\/p>\n

In Cisco\u2019s 2024 AI Readiness Index a different picture is painted. Approx. 8,000 senior executives were surveyed across six critical categories: strategy, infrastructure, data, governance, talent, and culture. Out of those, only 13% emerged as “pacesetters,” those that are fully prepared to leverage AI\u2019s potential. That figure is 1% down from last year. Most businesses fall into the four different categories \u201cchasers, (moderate preparedness)\u201d \u201cfollowers, (limited preparedness)\u201d or \u201claggards(unprepared)\u201d.<\/p>\n

From my perspective, is this about whether you are already using AI? Or is it about how well you\u2019re using it? Those questions will help you see where the gaps are in your usage or not of AI.<\/p>\n

The Illusion of AI Readiness<\/strong><\/h3>\n

A number of folks like Eddie Short<\/a> have commented that AI isn\u2019t just another technology to bolt onto your existing operations, it\u2019s a fundamental shift in the way that you think about your business and how it will fight for its position in the future.<\/p>\n

But here\u2019s the thing, even with many of us saying this, many organisations still approach it with the same mindset they\u2019ve applied to every tech trend before it:<\/p>\n

\u201cThrow some money at it, hire a few specialists, and poof magic dust is sprinkled.\u201d<\/p>\n

Do you think that works?<\/p>\n

Nope I don\u2019t!<\/p>\n

Because, without a clear strategy tied to measurable business outcomes, AI investments risk becoming expensive experiments. Infrastructure without alignment to real business needs is just sunk cost. Governance without clarity creates bottlenecks. Talent without the right culture is wasted potential.<\/p>\n

Tick Tock<\/strong><\/h3>\n

Back to Cisco\u2019s research, 85 percent of leaders believe they have less than 18 months to implement an effective AI strategy or face tangible negative consequences. This isn\u2019t a scare tactic; it\u2019s a reflection of the accelerating competitive landscape. Those who fail to act, or act poorly, will definitely be left behind.<\/p>\n

But time isn\u2019t the saviour that you think it is! Many leaders may understand\u00a0what<\/em>\u00a0AI can do, but where they fall down, is when it comes to defining\u00a0why<\/em>\u00a0and\u00a0how<\/em>. That\u2019s where the game is won or lost, not in the technology itself, but in its execution.<\/p>\n

Focus on Outcomes, Not Inputs<\/strong><\/h3>\n

The key to AI readiness lies in reframing the conversation. Forget the jargon and the hype, and as I always say, start with the basics. Here are four areas that you could initially start to focus on to bring some reality to your situation:<\/p>\n

    \n
  1. What problems are we solving?<\/strong>\u00a0AI for AI\u2019s sake is a waste of resources. Gosh how many times have I said that before about some other forms of hype! Define the use cases that directly tie to value creation, whether it\u2019s reducing operational inefficiencies, improving customer experiences, or unlocking new revenue streams.<\/li>\n
  2. What\u2019s holding us back?<\/strong>\u00a0Most organisations struggle with tangled systems, siloed data, and unclear decision-making frameworks. Addressing these foundational issues is far more critical than investing in the latest algorithm. I mean you can experiment and do some \u201cinnovation\u201d but when it comes to putting your experiment into production, you will have a bigger hill to climb if you don\u2019t at least attack some of the root causes.<\/li>\n
  3. Who\u2019s accountable?<\/strong>\u00a0AI isn\u2019t just a tech problem; it\u2019s a business imperative. That means clear accountability from the top, with leaders driving the cultural change needed to make AI stick. In most of the conferences or panel talks I\u2019ve been on, most of the conversations start with tech imperatives and guess where they ned up: at the cultural end of the barrel as that kills pretty much every kind of transformation you are attempting to achieve.<\/li>\n
  4. How do we measure success?<\/strong>\u00a0If you can\u2019t define success in business terms, your AI strategy is flawed. Every initiative must tie back to measurable outcomes, from cost savings to revenue growth. Measuring isn\u2019t about data or technology; it\u2019s about measuring whether you achieved the outcomes you were looking for. Many transformations get stuck on such measures as data quality metrics, sure you can look at those from a use case perspective, but please don\u2019t make that front and centre. These are back-office metrics, put the business outcomes measures front and centre.<\/li>\n<\/ol>\n

    Your Data & AI Readiness Checklist<\/strong><\/h3>\n

    You may want to review your AI readiness and have a long list of data and technical components to review, but that\u2019s far from what you want to be looking at! If I\u2019m going to start reviewing my data quality, what\u2019s the point? I don\u2019t know what I\u2019m doing yet in terms of business outcomes or use cases, so anything related to data or technology, should be fairly low down the pecking order.<\/p>\n

    To help you get a clear picture of where you stand, here\u2019s a checklist of questions tailored to ensure your data and AI strategy is built around the use cases that will drive value:<\/p>\n

    Strategy:<\/strong><\/h4>\n
      \n
    • Have we identified specific business use cases that AI can address?<\/li>\n
    • Are these use cases aligned with our strategic objectives and measurable outcomes?<\/li>\n
    • Have we determined which type of AI (e.g., machine learning, neural networks, generative AI etc.) is best suited for each use case? Or do we really need AI?<\/li>\n<\/ul>\n

      Data:<\/strong><\/h4>\n
        \n
      • Do we have access to the data needed to support our chosen use cases?<\/li>\n
      • Is our data structured and clean enough to enable meaningful insights?<\/li>\n
      • How well does our data align with the requirements of the AI models (e.g., training data for machine learning)?<\/li>\n<\/ul>\n

        Infrastructure:<\/strong><\/h4>\n
          \n
        • Do we have the technical capabilities to support the identified use cases?<\/li>\n
        • Can we scale our infrastructure as the use cases evolve or expand?<\/li>\n
        • Are our systems flexible enough to integrate with AI tools designed for these specific needs?<\/li>\n<\/ul>\n

          Governance:<\/strong><\/h4>\n
            \n
          • Are there clear governance frameworks to ensure that AI use cases are ethical, compliant, and secure?<\/li>\n
          • How do we monitor and manage risks specific to the use cases we are deploying?<\/li>\n
          • Do we have a clear escalation process if AI outcomes don\u2019t align with expectations?<\/li>\n<\/ul>\n

            Talent:<\/strong><\/h4>\n
              \n
            • Do we have the expertise to select and implement AI models that fit our use cases?<\/li>\n
            • Are we investing in the right mix of technical and business skills to ensure use case success?<\/li>\n
            • How are we fostering collaboration between data, technology, and business teams?<\/li>\n<\/ul>\n

              Culture:<\/strong><\/h4>\n
                \n
              • Is there a shared understanding across the organisation of how these use cases will drive value?<\/li>\n
              • Are teams empowered to experiment with AI solutions for their specific challenges?<\/li>\n
              • How are we addressing resistance to AI adoption tied to these use cases?<\/li>\n<\/ul>\n

                I hope that these areas and the checklist will help in the definition and development of your AI use cases.<\/p>\n

                Remember, and this is fact. AI is a tool, not a magic wand. Its success lies in how well it is aligned to your business needs and embedded into your operations. The next 18 months for those 85% of leaders consulted in the Cisco survey are critical. Start with the use cases that matter most, build from there, and focus on execution.<\/p>\n

                The question is, will your organisation be one of the 13% that gets it right?<\/p>\n

                If you’re struggling to translate AI ambition into actionable outcomes, let\u2019s talk. I can help you align your data and AI strategy to the use cases that will deliver real value. Don\u2019t wait, because your competition certainly isn\u2019t.<\/p>\n","protected":false},"excerpt":{"rendered":"

                From conference stages to office corridors to political campaigns, there\u2019s no denying it, AI is the obsession of the world right now. I read this last week that Nvidia\u2019s staggering $35bn quarterly revenue, obviously fuelled by the generative AI boom, is proof that businesses are throwing their weight behind the chips that will accelerate development […]<\/p>\n","protected":false},"author":546,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[96],"tags":[],"acf":[],"yoast_head":"\nAre You Ready for AI\u2019s Realities?<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Are You Ready for AI\u2019s Realities?\" \/>\n<meta property=\"og:description\" content=\"From conference stages to office corridors to political campaigns, there\u2019s no denying it, AI is the obsession of the world right now. 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