Artificial Intelligence - What could your company achieve if all interactions were intelligent?




What It Is

AI is a constellation of technologies—from machine learning to natural language processing—that allows machines to sense, comprehend, act and learn.


Why It Matters

Artificial intelligence will transform the relationship between people and technology, charging our creativity and skills.


Where It's Going

The future of AI promises a new era of disruption and productivity, where human ingenuity is enhanced by speed and precision.


RESEARCH REPORT


Scaling enterprise AI for business value


In brief

Our research hows that three out of four execs understand they need to scale AI across the organization to stay competitive—and in business entirely.But many struggle to realize the full value of their AI projects and move beyond POC to production because there's no clear path to "live."To scale effectively, organizations need to have a clear AI strategy, diverse teams and ethical frameworks built into their AI, among other things.Dive into our POV, Ready. Set. Scale. to learn about these success factors and the AI Roadmap, our journey to productionize AI to deliver real value.


In practice, companies still find it difficult to make the transition from thinking about AI as a source of innovation to a critical source of business value. There’s a state of paralysis beyond the pilot. Why? Until now, there hasn’t been a proven blueprint for scaling, and organizations can fall into some common traps. First, companies don’t have an AI roadmap or "route to live"—the steps to take their AI project from POC to production, effectively and expediently. AI is different from "traditional" software implementation projects, which companies are typically set up to deliver. Changing the status quo requires agility, openness to trying a new way of working and the ability to recognize when an idea works—and when it needs to be scrapped.










Second, the unfamiliar landscape of AI also means businesses can be tempted to fall back on their time-honored behaviors, reinventing the wheel and building from scratch. Big mistake. There are many proven, low-cost AI options to buy "off the shelf" and start using right away. It is key to leverage what already exists, customize as needed for the organization and start proving the value of AI as the first step to successful scaling.

But don’t get bogged down in the technology. Be driven by the business strategy and vision, and let that dictate the AI approach. Focus on finding the right way of working that will allow AI to flourish, diversifying skills and talent beyond the data scientists. And get the right governance approach in place from the outset, with outcomes in mind. Applying these critical success factors can help you unlock a new wave of exponential value by scaling AI successfully.




Creating value with the right AI strategy


Your business strategy is your AI strategy

To scale AI successfully, get your ducks in a row early. That means 1) understanding what business value means to you, 2) translating that definition into a business strategy and 3) focusing in on AI solutions that explicitly deliver on the most critical elements of that strategy. Simple, right? If you have already defined value in your own unique context, you can harness AI to multiply that value—not just grow it marginally—charting a course that genuinely aligns with your business’s strategic priorities and delivers unprecedented returns. Strategic Scalers understand this imperative, with more than 70 percent linking their AI ambitions explicitly to their overall business strategy.

Decide what to focus on—and focus.


Look to the highest-level priorities

It seems like more and more applications of AI are emerging each day. So, how do you determine which applications are going to deliver value, whatever that means for your specific context?


Finding true value starts with defining what really matters to a business and aligning the AI agenda to the highest-level strategic plans. Ask yourself: What are the boardroom’s short- and long-term priorities? How can AI help achieve the objectives of the C-suite such as organic growth, expansion into a new industry or development of new products?


Define value for today—with a vision for tomorrow


While you need to look at the short-term return AI can create for your business, you also need to look at value—and therefore your high-level priorities—through a broader lens. Where is your organization headed in the ‘human-plus-machine’ era? What is the future of your industry? Will that change how you define value three to five years from now?

AI has the power to disrupt well beyond individual businesses. It is already blurring traditional industry boundaries, threatening legacy companies and giving agile new entrants the chance to make an impact, fast. Make sure you’re paying attention to what’s disrupting your industry already, how your world and the world at large are changing, and adjust your strategy, act boldly and invest to buy your way into the action. You may find yourself making different choices when you bring the macro into play.








Take a portfolio view of your AI projects


To be successful on your AI journey, think about your AI projects as a portfolio of things you’re trying to achieve. This means thinking holistically about where you’re headed and navigating the iterative nature of AI initiatives while remaining aligned to strategy and value. Scaling value relies on a formally defined AI roadmap which can help you deliver faster with more rigor and get to production more quickly.


The first step of the life cycle is to create an "idea pipeline"—and populate it with potential AI concepts that are yet to be tested for feasibility and value for your business. Shape, develop and investigate those ideas iteratively—but quickly—before a "go/no go" decision. The ideas you generate may vary in terms of their potential to succeed, so having a holistic view of the collective success of your AI projects will be vital.


Therefore, assimilating AI into your business brings a new type of project execution risk with only a portion of your ideas and experiments expected to go to production. But the good news is that following an AI roadmap, like the one here, helps qualify ideas quickly and effectively—so ideas that fail, fail fast and can be shelved with minimal investment before moving on to something else.


Strategic Scalers have mastered this approach. This group pilots more initiatives and successfully scales more often than their counterparts: They reported scaling 114 applications in the past three years, compared to just 53 for companies at the POC stage.


Underpin your AI strategy with a data strategy


Every AI transformation journey starts with data. Our research shows that nearly 75 percent of AI Strategic Scalers agree that a core data foundation is an important success factor for scaling AI. More specifically, they understand the importance of having a data strategy—a design and intent that underpin what data is being captured, in what way, and for what purpose. The data strategy drives value as much as AI does.


And more data is not always better. In a world where data is proliferating and data begets more data, it can be tempting to gather more and more. Having a strong data strategy ensures you’re curating the right data to deliver the desired outcome and then capturing its insights to fuel an AI strategy that delivers that outcome at speed and scale.


Once the data strategy is set, data can be mined to generate insights that help refine both the organization’s strategy and the AI systems themselves. To really get the most out of this constant stream of data-driven insights, you’ll need to explicitly integrate "feedback loops" into business decisions in an orchestrated way—for instance, to fine tune your business strategy and/or make necessary adjustments to your AI initiatives at the same time. This requires a new way of working: an agile, iterative approach to decision making—as well as AI development—with data at the core.


Rethink AI talent in the workplace


Rethink work and get your people ready

AI's disruptive nature means your old ways of working will need to change. Those who can successfully integrate AI into their culture and processes will be able to multiply value for businesses, employees and customers alike. Our AI: Built To Scale research confirms the correlation: AI Strategic Scalers are more likely than those in the Proof of Concept stage to embed AI ownership and accountability into teams and ensure employees fully understand AI and how it relates to their roles.


Start configuring the business of the future—now


There are some practical steps you can take to start configuring business processes and the workforce to support AI at scale:


Move from "workforce" planning to "work" planning

Break down traditional job roles, and look at which tasks and activities will be automated, which will require human-machine collaboration, and how this might impact how people and teams intersect and interact.


Look seriously at new skilling Get a clear view of the knowledge and skills you'll need to generate real value from human-machine collaboration. Look at your leadership, learning and recruitment programs, and invest in new ways to teach new things. For example, we put 60 percent of the money we save from investments in AI into our training programs.


Look at the big picture What entirely new jobs—such as the "AI trainer"—can AI create in the organization? Are we prepared for those in the context of new markets, products and customer experiences?


As we lean into human + machine collaboration, many human tasks will be augmented by AI. For example, AI can provide enhanced views of real-time data to help support decision making—without the decision making itself necessarily being offloaded to AI. It's important to be clear about the right boundary, or process, for the organization when it comes to the split between the human and the machine—including how that boundary may shift as the organization's AI maturity continues to change. Successful scaling relies on understanding how the organizational chart will change with the upskilling and reskilling of people to be "data native" and with new ways of delineating jobs and tasks.


Your workforce may be more ready than you think to adjust. Our research on the Future Workforce says so. Now it's up to you to take action.

AI may be good for workers:












Establish the right talent mix


It’s no surprise that you need new kinds of talent to create AI products and services that deliver value. But beware of thinking data scientists are the only ones who matter when it comes to creating a route to "go-live." You also need data integration experts, business analysts, data engineers, and software engineers among others—and enough of them, in the right configuration.


In addition to the technical skills, it's important your team is interdisciplinary, bringing industry, business, design and governance expertise in the right ways and early on. These areas of knowledge might be easy to overlook, but they play a crucial role in creating successful AI applications.


You need the right mix of talent to move from POC to production


Look at your organizational set-up


Along with establishing who gets the work done, it's important to revisit the "how." Think about the kind of physical set-up that will help you achieve your business goals and integrate AI most effectively. For instance, do you need geographically dispersed business units and AI tools or a more centralized structure?


Our research suggests that a centralized organizational model may be the most effective, with Strategic Scalers saying they now use this approach.


Another variant is "hub-and-spoke," a model that includes both a centralized cross-functional AI group (i.e., the "hub," sometimes called a Center of Excellence) and separate autonomous AI teams (i.e., the "spokes") that sit within business units. Finally, a "distributed" model also exists. Highly autonomous AI teams are housed within each business unit or function, with a delivery focus specific to that business unit or function.


Be guided by your business aspirations, and define a way of working that best supports those goals and your level of AI maturity.


Mind the gap


There can be a gap between the CEO's understanding of AI—what it can do and how—and what the people actually implementing the AI believe. The CEO's perspective will naturally be influenced by the topline strategic intent of the company, what her peers are telling her, and what her long-term aspirations are for the organization. The AI leads doing the work might not always be aligned with the realities and focused goals of the C-suite—but they need to be! In fact, our research indicates that leadership's limited understanding of AI's potential can be one of the top challenges companies face when scaling AI. Strategic Scalers "mind the gap"—they reduce the distance between the goals and understanding of the C-suite and the practitioners when it comes to how AI can and should be applied to change the world, and their world.


Time to implement? Look outside your organization


We are now firmly in the "era of implementation" with an explosion of investment in AI capabilities coming from well beyond Silicon Valley.2 These days, there are myriad tools which are proven, low-cost and academically rigorous. And there are varied and flexible ways to get your hands on AI: open source code, application programming interfaces (APIs), and small and medium-sized enterprise (SME) vendors to name just a few. As AI becomes mainstream, solution price points will also continue to drop.


Now you can reuse, partner or buy to implement and scale AI capabilities before you even need to consider building new proprietary technologies in-house. Take advantage of what's out there for success at speed and scale.


So how do you decide when to reuse, buy, partner or build? This is a full topic in and of itself, but the simple answer is almost always reuse, buy or partner to take advantage of the investment other companies have already made—and get started quickly.


Scaling with AI ethics in mind


Build responsibility into your AI


How do we learn to trust AI? Responsible AI builds trust and lays the foundation for successful scaling by taking a "human first" approach—using technology to help people make better decisions, while keeping them firmly accountable through the right governance processes and technical steps. Our AI:Built to Scale research says responsibility is more than a "nice to have"—with AI Strategic Scalers significantly more likely to brief their employees clearly on how they tackle responsible AI.


You see the value in AI … but how do you trust it?


AI affords tremendous opportunities, from increasing efficiencies and improving outcomes, to reimagining industries altogether. Against this backdrop, it’s easy to forget that AI’s decisions also have a real bearing on people’s lives, raising some big questions around ethics, trust, legality and responsibility. Enabling machines to make decisions may expose a business to significant risks, including reputational, employment/HR, data privacy, health and safety issues.


Enter: Responsible AI. It’s a topic that’s becoming pervasive in the media and a real consideration for clients in the public and private sectors.


What happens when a machine's decision turns out to be erroneous or unlawful? The potential fines and sanctions could threaten the business’s commercial sustainability. And what about other unintended consequences? AI has already shown it can be biased in ways that weren’t anticipated and can hurt a brand’s reputation. AmazonSM, for instance, had to scrap its AI-based recruiting tool that appeared to show bias against women. And if need be, how does a human know when to intervene in a process driven by a machine?


Design trust into how you operate AI


The Board of Directors needs to know what obligations it owes to its shareholders, employees and society at large, to ensure AI is deployed without unintended consequences.


The CEO might be asking, how can I be assured we have thought through AI’s possible brand and PR risks? Meanwhile, the Chief Risk Officer and Chief Information Security Officer need to be thinking: If we deploy AI, how can we do it in a way that complies with data protection regulations? Creating a robust ethical underpinning for AI allows you to "design out" legal and ethical concerns to the extent that it is possible.


However, it's not just about establishing the appropriate governance structures. It’s also important to translate those ethical and legal frameworks into statistical concepts that can be unambiguously represented in software.

So, where to begin?


First, ensure considerations for AI are built into your core values and robust compliance processes. Then, you will need to implement specific technical guidelines to make sure that the AI systems are safe, transparent and accountable to protect your employees, clients, civilians, and other organizations.


Next, identify new and changing roles, and put the right training in place for technology specialists and your diverse team of experts to understand their new roles and remit.


All of these elements are part of an innovation-friendly blueprint for Responsible AI that you can apply across functions and projects—allowing you to understand and manage the ethical implications of everything you do.


Put ethics at the core to build and retain trust


Design in ethical frameworks when you’re planning AI. We program algorithms to give us exactly what we have asked for, so we shouldn’t be surprised when they do. And the problem is that simple algorithms treat all data as immutable, even data about our preferences, income and life situation. What can happen then, is that algorithms can trap people in their origins, history or a stereotype. These "bad feedback loops" can lead to negative impacts on society.


The issues mentioned are not inherent to machine learning algorithms themselves. Instead, issues arise from the way they interact with society and the unintended consequences that can result from those interactions. As such, putting the ethical implications at the heart of the development of each new algorithm is vital.


Just as data privacy and cyber security have moved from department to board-level issues, responsible governance of AI must be quickly elevated in importance by all organizations that use it.


An AI roadmap to maximize the value of AI


Productionize, and get ready to realize value


Scaling value is about understanding how to move from pilot to production; getting your data strategy in place to drive real-time strategic actions; and establishing the right talent mix, operating model and governance framework. Those who succeed will reap the rewards. And those who fail may find their businesses fall by the wayside (75 percent of executives believe they will be out of business in five years if they cannot scale AI effectively).


AI is no longer a "nice to have" or a set of cool tools to impress management. AI and data strategies are becoming the very core of business, and all the while it’s becoming easier and cheaper to get your hands on the technology. The time to act is now.

The journey to live


There’s a lot to think about—and a strong business case to get started quickly. In this primer, we have asked some questions and provided some insights on what it takes to scale AI effectively and move beyond proofs of concept to production. But how does it all come together in practice—and what concrete steps can you take to realize value quickly?


The final chapter of this primer is our AI Roadmap, a start-to-end model we use with our clients to help them realize and multiply value from their AI projects. It details an AI use case's route to live, which includes defining value and formulating a solid AI strategy; bringing together the right AI capabilities; thinking about the optimal talent mix; and getting the appropriate governance and ethical parameters in place. But it doesn't just end there. It lays the path for how to multiply value from the use case through continuous engineering, optimization and the extension of the feature to new use cases.


We invite you to go through our roadmap and evaluate how you’re approaching your AI projects. Stop at each checkpoint and ask yourself the flagged questions to make sure you're setting yourself up for success—with your data, your people, your infrastructure and your organization at large. Whether you’ve been in proofs of concept or are already starting to scale AI, be assured that there are concrete steps you can take to realize even more value from your AI initiatives


Connect with us today to discuss how AI can benefit your organisation

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