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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: