Ghufran Ahmed’s Post

I'm seeing more companies create Chief AI Officer roles. It's easier said than done, however. Unsurprisingly, determining the role's objectives and responsibilities is still a work in progress in many organizations. To ensure that the CAIO role does more than provide short-term marketing benefits and avoids becoming a figurehead with little actual impact, it's crucial to design it thoughtfully. Here are some key areas where a CAIO can make a significant difference: 1. Alignment and Coordination: The CAIO should provide strategic direction and vision, helping to coordinate resources and find the right balance between the risks and rewards of AI adoption. This can prevent extreme outcomes like a free-for-all system or total paralysis due to IP, privacy, and security concerns. 2. Governance and Oversight: AI implementations come with potential risks, and the CAIO must work closely with compliance, risk, business, and technical teams to strike the necessary balance. Organizations may ignore the risks without a leader navigating these complexities or become paralyzed. 3. Prioritization and Direction: The CAIO should suggest, triage, and prioritize AI initiatives, focusing on small, successful projects that generate excitement about job enhancement rather than replacement. This approach can foster a company environment that is friendly to AI adoption. 4. Center of Excellence (COE): The CAIO organization should host a COE to coalesce and develop the required AI competencies with available experts. The focus should be on quickly testing business outcomes and determining where to invest or cut back. To be effective, the CAIO role must pervade the entire organization over time without becoming a bureaucratic chokepoint that slows adoption. The ideal candidate should be a trusted insider with experience in senior roles and organization knowledge, reporting no further than one level below the CEO. How is your organization preparing to embark on its AI transformation journey?

Hasan, thanks for your insights on the evolving CAIO role. The recent developments in AI bring great promise but also new risks, causing many organizations to be slow to benefit from it. Your thoughtful approach strikes the right balance and provides a way forward. Curious to see how others are structuring their AI leadership roles to drive meaningful transformation!

Everyone thinks implementing AI in a business is like riding a roller coaster. In reality, it's even crazier than that. A roller coaster follows set tracks with predictable ups and downs. But the AI implementation journey doesn't follow any defined rules, and if there were statistics, I'd say most of it consists of downs with very few ups. I remember some of the initial euphoric highs when we first started exploring AI at my company: - The potential for process automation was exhilarating - Hiring our first AI engineers felt like a dream  - Even without a roadmap, we were building AI capabilities from scratch But just like a roller coaster has dips, there were tremendously challenging lows too: - Weeks where models didn't perform as expected - Months with little tangible ROI from our AI investments   - Constantly firefighting data issues and integration challenges This unpredictable reality is just par for the course when pioneering AI adoption. One month ago, AI defects caused customer escalations that nearly derailed our implementation. Executive sponsors threatened to pull the plug if we couldn't course-correct immediately. Another time, our AI system enabled record $1M cost savings, but we faced a regulatory probe over potential bias in the models that dragged on for months. The point is, I have countless stories of existential highs and lows from our AI journey. So I always advise clients to understand this tumultuous reality. Then when roadblocks emerge, you don't panic, thinking it will derail everything. It might rain challenges for weeks, but you know the storm will eventually pass, as it always does in an AI transformation. There are crazy twists and turns, ups and downs. Despite the benefits, the pressures and volatility are intense. This is the price of being an AI pioneer. Most organizations take the safer path of being followers rather than leaders. But I wouldn't have it any other way! What has your organization's AI journey been like so far? Any nauseating drops or thrilling climbs to share?

👀 6 mistakes your enterprise is making on your way to Gen AI capabilities! Embarking on Gen AI capabilities? You're in good company but watch out for common missteps. Like 70% of companies, you might be overlooking some key mistakes. I've identified six areas where you might slip up. 1. Lack of Clear Strategy and Vision: Without a well-defined strategy, future integration and scaling become difficult, leading to wasted resources and missed opportunities. 2. Inadequate Data Management: Poor data quality and management hinder AI model training resulting in inaccurate models, reducing the effectiveness of AI-driven solutions. 3. Ignoring Ethical Considerations: Ethical considerations like bias and privacy are often overlooked. This may lead to legal challenges and reputational damage in the future. 4. Underinvestment in Talent and Skills: The lack of skilled professionals in AI leaves a talent gap that can slow down innovation and cause enterprises to fall behind competitors. 5. Failure to Align with Business Objectives: AI initiatives that are not aligned with core business goals can lead to a lack of support from stakeholders, diminishing the overall impact of AI. 6. Overemphasis on Technology, Underemphasis on Process: ⭐ Focusing solely on technology without considering process integration will lead to bottlenecks in workflow, limiting the real-world applicability of AI solutions. The journey to Gen AI capabilities is an exciting endeavor. However, if you rush to launch it will cause you more harm than good. We can help!

Nice post here from Bernard Marr on the Top 10 Mistakes in AI Strategy. 1. Lack of Clear Objectives Diving into the AI pool without a clear set of objectives is like embarking on a cross-country road trip without a map.  2. Failure to Adopt a Change Management Strategy Adopting AI isn't simply about integrating new technology into existing processes. 3. Overestimating AI Capabilities AI is powerful, but it's not a magic wand. Overestimating what AI can do often leads to unrealistic expectations and disappointment. 4. Not Testing and Validating AI Systems Failure to adequately test and validate AI systems can lead to inaccurate outputs, system errors, and in worst-case scenarios, serious harm.  5. Ignoring Ethics and Privacy Concerns 6. Inadequate Talent Acquisition and Development AI is a complex field that requires specialized skills. Many companies that are creating AI strategies fail to invest in acquiring and developing the right talent for their initiatives. Not having the right skills for AI is often the cause of project failures. 7. Neglecting Data Strategy Data is the lifeblood of AI, and neglecting data strategy can starve AI systems of the vital information they need to function correctly. 8. Inadequate Budget and Resource Allocation Adopting AI requires substantial investment in technology, talent, data, and infrastructure. Companies often underestimate these costs, resulting in insufficient budget and resource allocation.  9. Treating AI as a One-Time Project AI strategy is not a "set-it-and-forget-it" process. 10. Not Considering Scalability Companies often pilot AI projects on a small scale without considering how those efforts will scale. It's a good list but I would add - Not having a clear Leader - who will actually drive this. - Not having a clear business case - how do me measure the benefits What do you think? https://lnkd.in/eGAtQmYX

AI is a vital part of digital transformation, which can not only improve business performance, but redefine it – and 49% of mid-market organisations are adjusting their long-term vision to accommodate digital transformation.   To ensure the best digital transformation outcomes, many organisations are turning to AI strategy consulting to help them navigate this complex landscape. Partnering with an experienced AI consultant can provide businesses with the necessary skills and expertise to drive digital transformation successfully.   BDO Digital’s latest insight explains all businesses need to know about AI Consulting for Digital Transformation, including how to find the best AI consulting services to drive your digital transformation.

Governance – Is the Chief AI Officer for real? There's been a lot of talk about the rise of the CAIO; and at least one survey suggests that it's on its way to being a big thing, but is this something that's actually happening or is it just another part of the hype around generative AI? For tech companies, with a need to respond to new client demands, it probably makes sense, but is it percolating out to other businesses or, is it just title inflation or the wishful invention of imaginative headhunters? What would a good CAIO have to do? I think that the needs are clear: - validate the commercial case against real AI capability - manage a route through an evolving legal and regulatory risk landscape  - make the tech understood across the business - ensure the change and project management actually delivers value, and of course,  - lead on the technical implementation Aside from the stalwarts of the CEO, CFO and maybe the COO, membership of the exalted circle of C titles can be a moveable feast. For example, there seem to be more Chief Risk Officers in non-financial services businesses, but the Chief Diversity Officer seems to be slowly disappearing. I would argue that for AI adoption to be real that it has to be understood, and owned widely. It seems obvious that the most successful AI implementations will be those which seamlessly become part of BAU. I'd suggest then that if there is a role, it is a transitional one, a bit of technological midwifery. Within a non tech organisation, those change management skills means that as well as understanding the AI side a useful CAIO also needs to understand the risk, regulatory and commercial dimensions at a fairly deep level. Finding one person who can cover this broad horizon might be a bit of an ask. A unicorn indeed. If you thought that this post was useful then please: 📣 add your comments below 📫 repost to your network (thanks!) and: 🏃♂️ 🏃♀️ Follow me : https://lnkd.in/e4ywHqe7

The majority of small businesses I've spoken with are in the early stages of their AI journey. We developed an SMB AI Maturity Model to assist MSPs with guiding these users on that journey. Initially, most companies fall into the category we call "AI Novice" or "AI Explorer". The companies have either never used AI, or only a select few in the company have. And in most cases, they're yet to implement formalized policies or procedures around AI usage. Now, when I meet these clients, often they're eager to make the leap straight to the top. They want to be "AI Leaders", with revolutionary AI systems scanning their files and streamlining their processes. Yes, this is achievable. But it requires time and progression. The best approach is to guide users along their AI journey, gradually helping them understand how AI can have a meaningful impact on their operations. Most small businesses should reasonably aspire to reach the "AI Enthusiast" or "AI Specialist" stage in the first few weeks or months of their AI implementation. This stage encompasses formalizing AI policies, developing a list of approved AI applications, and building out use-cases that cater to their specific business needs, both horizontally (e.g., marketing, HR etc.) and vertically (industry-specific implementations). The ultimate goal is to evolve into an "AI Leader", but this progression demands a lot of work which includes proper data classification, tagging with user permissions. It's important not to leap from novice to leader too rapidly. It can result in confusion, rejection, or even potential data leakage. Instead, have a longer term AI strategy. Star by identifying quick wins with high ROI. Over time, you can advance through the maturity model and bring the rest of your business along for the ride. But typically this can't be accomplished single-handedly. It often requires the assistance of a technology provider like an MSP. And it may even take a few months of experimentation, adjustment, and learning to create an AI system that truly works for your company. Adoption of AI isn't a sprint. It's an endurance race. And in the long run, those businesses who take consistent, informed steps towards their AI journey with the help of dependable providers are the ones who will transform into true AI leaders.

Here’s how to avoid making the same mistakes with AI, that many companies made with digital transformation… 1) Focus more on people and processes than the AI tech itself. If everyone in the market has access to the same technology then people and how you operate is your strategic advantage. Technology enhances how you operate, so if you don’t address you core operating model as a business, you will limit your ability to progress. 2) You can’t avoid messy data AI success will come down to the quality of data it’s fed. Companies who invested in data consolidation and management will be well ahead. You can’t avoid any messy fragmented data now. 3) Address siloed product centric organisational structures Many organisations are still structured by product, working in silos competing against each other for their customers attention. Companies who have addressed this and re-organised around the customer are already ahead today and will jump even further ahead when they implement AI. 4) Don’t view AI as a way to cut costs but as a way to improve the customer experience Viewing AI as a way to purely cut costs, will lead to impersonal subpar customer experiences. View AI as a way to take care of all the manual mundane tasks to free up humans in the organisation to be better humans. Sure, it will mean some reduction in headcount, but it shouldn’t be the objective. 5) Building a specialist team in isolation Many organisations built standalone digital teams as a silo who are still not as well integrated into the organisation as they could be. AI is going to be even more embedded into our ways of working than digital, so it’s important to take this into consideration and not build a siloed AI team, that’s distant from the business. 6) View AI transformation as continual program vs a one off project Much like digital, AI is going to be a continual program of change and optimisation, so having this mindset from the start is important as opposed to viewing as a one-off set-up project. Are there any that you would add? And curious to hear what you’ve observed so far with AI implementation. Drop a comment below, don’t be shy.

The integration of Artificial Intelligence (AI) into business operations is rapidly transforming industries, disrupting traditional models, and reshaping the competitive landscape. While the potential benefits are vast, the challenges cannot be overlooked. Let's explore the opportunities and challenges that come with the rise of AI in business. Opportunities: 1. Operational Efficiency: AI-powered automation can streamline processes, reduce human error, and optimize workflows, leading to increased productivity, cost savings, and operational excellence. 2. Data-Driven Insights: AI's ability to process vast amounts of data and identify patterns can provide businesses with valuable insights, enabling data-driven decision-making and informed strategic planning. 3. Personalized Customer Experiences: By leveraging AI's predictive capabilities, businesses can tailor their products, services, and marketing efforts to individual customer preferences, creating personalized experiences that drive customer satisfaction and loyalty. 4. Innovation and Competitive Advantage: AI can fuel innovation by accelerating research and development, enabling businesses to explore new products, services, and business models, ultimately gaining a competitive edge in their respective markets. Challenges: 1. Ethical Concerns and Trust: The rise of AI raises ethical questions about data privacy, algorithmic bias, and the potential displacement of human workers, challenging businesses to navigate these concerns while maintaining public trust and transparency. 2. Skill Gap and Talent Shortage: As AI adoption accelerates, businesses may face challenges in finding and retaining talent with the necessary skills to develop, implement, and manage AI systems effectively. 3. Regulatory and Governance Challenges: The rapid pace of AI development often outpaces the establishment of appropriate regulations, governance frameworks, and ethical guidelines, creating uncertainty and potential legal risks for businesses. 4. Integration and Change Management: Integrating AI into existing business processes and systems can be complex, requiring significant investments in infrastructure, data management, and change management strategies to ensure a smooth transition. The rise of AI is not just a technological shift; it's a transformative force that demands a strategic approach, ethical considerations, and a willingness to adapt and innovate. Businesses that embrace AI responsibly and harness its potential will undoubtedly gain a competitive edge in the ever-evolving digital economy. We’d love to hear from you, share your thoughts and experiences using AI for business in the comments below.