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Every company—regardless of business type or business model (i.e. product, SaaS, service, media, etc.)—will go through a highly predictable cycle of growth and maturity called the “S Curve of Business”. As we’ve seen time and again, impressive growth in the short term may be achievable for many companies, but sustaining that momentum without facing moments where growth stalls simply doesn’t happen. The market fluctuates. Competitors make adjustments to stay relevant and spearhead new campaigns targeting and drawing on your customer base. Your ground-breaking product is no longer as impressive and unique as it used to be. So why spend much of the beginning of this podcast talking about all of this? Well, in short, embracing AI will require navigating the 'S' Curve and sustaining funding, executive sponsorship and Board-level support. Each of these challenges present unique challenges and hurdles on their own; they will distract most companies from focusing on topics relating to fairness, accountability, transparency and ethics and perhaps other social issues relating to availability, affordability, equity, bias, etc. Taken together, all of these ultimately affect the overall human experience of both 1) employees, engaging in AI projects to create products, services and solutions, and 2) customers, expecting a positive "human" experience when using AI-powered products & services. The biggest mistake a business can make is jumping straight into a major change initiative that is akin to "a square peg, round hole" scenario which may lead quickly to the significant failures. With these principles in mind, let's move on to the main content of today's podcast. From start-ups and scale-ups to small and medium-sized enterprises (SMEs) or large corporations, AI initiatives are crucial due to their innovative and competitive power. That said, businesses should consider taking a "humanistic" approach to AI adoption by 1) working together in public-private partnerships to realise societal and economic opportunities of AI. 2) focusing on "Explainable" and "Responsible" AI — to uphold fairness, transparency and equity. 3) focusing on equitable hiring which supports diversity and embraces remote working. 4) establishing AI applications that serve the interests of people and society — whilst avoiding #codedbias and other situations that lead to division. 5) opting for an inclusive approach that puts people first whilst we strive for reliable AI. But this is not a straightforward task for any business. There are some key areas that every company should consider when embarking on their AI/ML journey, namely, 1) Investing in an AI-enabled Operating Model 2) Building (and sustaining) an "AI" Talent Pool 3) Establishing collaboration structures to support "AI" Product & Service implementation. In short, there are no shortcuts. Additionally, it is not just about technology. You also need to develop strategies that relate to how your organisation will manage data (to support analytics), people (to encourage collaboration) and processes (to support governance). Without introducing strategies that address the areas listed above, your company may end up trying to fit "a square peg in a round hole" — which will ultimately affect your bottom line and allow your competition to "leap frog" ahead of you. Some thoughts on an AI-enabled Operating Model. For any company, the "Operating Model" is HOW a business runs itself. It is how it uses its people, processes, data and technology to execute its "Mission and Vision" and bring value to its customers which may extend to its employees and workforce. In contrast, the "Business Model" is a strategy that a company sets to drive how it will create and grow value, and WHAT it shall do to achieve it. It is a model used in strategy and planning. It fails when it doesn’t achieve its projected targets such as a particular revenue stream or profitability. In today's world of big data, large corporates have access to a wealth of information about their customers, as well as about how they do business and deliver value to their customers. By using AI and ML to build technology that can model and predict these customer and user journeys, or logistical challenges, companies can really leverage their data to find new sources of value. Building new technology that streamlines a company's logistics and powers its Operating Model is valuable. When a company has AI assets that can be licenced and sold to other businesses whilst also serving its own challenges, the value (i.e. revenue and profit) of the company has the potential to increase. When embarking on your AI journey, there are essentially THREE types of Operating Models that should be considered. 1) Centralised model 2) De-centralised model 3) Hub-and-Spoke model Each type needs to be underpinned with a data capability that most likely will need to be established in your organisation; this will be very different from building a digital capability. Many organisations are mistakenly shoehorning AI initiatives into digital transformation — which, in my view, may be a bit short sighted given they are very distinct topics. Checkout the link in the podcast description for details of the full article which provides more details. Now let's turn our attention to Building a "Humanistic" AI Team. Whether an AI team is an outgrowth of an existing analytics team or an entirely new group, there are many different activities that it can and should pursue. Some of these — like developing AI models and systems, working closely with vendors, and building a technical infrastructure — can be done in collaboration with an IT organisation; others will involve working closely with business leaders. Demand for skilled AI professionals is a major priority for countries like — the US, China, India, Israel, Germany, Switzerland, Canada, France, Spain and Singapore — in that order. One of the most critical factors for a business looking to acquire and develop an AI Team (or Competency Centre) is recruiting, attracting, or building talent. It is no secret that leading-edge AI engineers and data scientists are difficult to hire — even in Silicon Valley. Most organisations will require a few people with the ability to develop and implement AI algorithms—say, a Ph.D. in AI or computer science. But many of the business-focused tasks of a CC can be carried out by graduates and MBA-level analysts who are familiar with AI capabilities and who can use automated machine learning tools. Alternatively, to get started, businesses may consider hiring consultants or vendors to engage in the early stages of AI projects. In this case, these resources should be combined with internal employees to enable a level of "shadowing" and "on the job training". Building an AI Team through recruitment and sourcing isn't enough. Every business needs to also invest in adapting its internal organisational structures to encourage and incentivise collaboration to leverage opportunities relating to AI and Data Analytics. Since AI talent is scarce, it is difficult to develop critical mass if it is scattered around the organisation. Regardless of what Operating Model our business settles for, the organisational structures should incentivise (and reward) cross functional collaboration that is baked into roles and responsibilities. To avoid excessive bureaucracy, a centralised group should embed or assign its staff — at least some of them — to business units or functions where AI is expected to be common. That way the centre staff can become familiar with the unit’s business issues and problems, and develop relationships with key executives. Rotational programmes across business units can improve knowledge growth and transfer. As AI starts to become pervasive, these embedded staff may move their primary organisational reporting line to business units or functions. At a minimum, the following key roles should be included in every AI Team :- 1) AI Engineer Roles 2) AI Data Governance Roles 3) AI Translator Roles (i.e. "business partners") 4) Business Leader Roles 5) AI Ethicist Roles Ultimately, all members of the AI Team should think and act as "stewards" of AI algorithms, applications, products and services. If you're still listening .. thank you! Let's start to wrap up now with some concluding thoughts. If your business wants to embrace AI and embark on its own journey towards intelligent systems and automation, you need to create a vision for AI in your company. This will inevitably require new business models and strategies. Otherwise it may be sub-optimal and impact your business, your culture, profitability, and future growth. Starting your AI journey requires significant commitment - embarking on a change programme, disrupting "ways of working", migrating to new operating models all while developing skills and initiatives that build long term value. To build a great house you start with an architect. You imagine what’s possible and then you make plans. You don’t dig a trench and pour concrete into a hole and then think about where the walls should go, what kind of windows you like, what kind of roof you like. To do it properly, you imagine it. Design it. Calculate the materials. Map out the plans. Hire the best people for the project etc etc. There is an order to success and much of it can be expressed in numbers that help you work it out. It’s the same with your company when initiating your own personal AI journey. That's all for now. Stay tuned for more episodes and don't forget to leave your feedback. Until next time, take care & keep learning!