The Future of Innovation: Integrating Human and Machine Intelligence

Executive summary

The traditional models of corporate innovation, driven by collective creativity, have long been the cornerstone of progress. New innovations, from incremental modifications to radical transformations, are made better, more robust through the power of collaboration, diverse perspectives, and expertise. These days, though, corporate innovators are increasingly confronted with the inherent limitations of approaches that rely solely on the “wisdom of the crowd.” Due to those limitations, many companies struggle to see the actual business impact of their various innovation activities—despite ever-increasing corporate innovation spending.

The good news is that at this very moment, we find ourselves on the cusp of a technological revolution: the accelerating adoption of artificial intelligence (AI) across industries. Blending machine intelligence and human ingenuity has the potential to overcome the major sticking points of corporate innovation and unlock previously unimagined levels of productivity.

This guide will delve into the forces shaping the future of innovation and why a shift in how companies innovate is imperative. We outline our vision for the future of innovation, in which humans and technology combine to complement and magnify each other in a single Innovation Operation System (Innovation OS)—a company-wide platform for end-to-end innovation designed to connect the dots from strategy to execution. Finally, we discuss the implications for corporate innovation and how ITONICS makes innovation not only more accessible, collaborative, and inspiring but also smarter, faster, and more transformative.

Forces shaping the future of innovation

What challenges do corporate innovation departments currently face? 

Rising volatility and uncertainty

The pace of change and technology is accelerating. Volatility and uncertainty are rising. Markets are increasingly globalized, commoditized, and regulated. And both customers and competition are constantly evolving.

These forces are felt acutely by companies across industries and geographies. Two out of five global CEOs believe their organization will fail to remain economically viable in the next decade if business as usual is maintained. 

The need to innovate, to keep pace with or try to preempt these forces of change, is well understood. Executives are investing millions in lean strategies, emerging technology, and tools to improve efficiency, business intelligence, and project management. And yet, many of these efforts fail to produce the breakthroughs needed to stay ahead of the competition.

Diminishing returns on innovation spending

Driven by an innovation imperative, organizations globally have steadily increased their innovation investment over the past 15 years. Across industries, innovation has seized larger and larger shares of the company budget; for instance, R&D spending today has surpassed advertising spending by a factor of ten (from about equal budget size 40 years ago).

However, many companies do not see the tangible results they expect from this spending. On the contrary: across industries, analysts report a significant decline in the productivity of corporate innovation efforts, turning innovation spending more and more into a black box. Over the past five years in the United States alone, innovation investments have underperformed to the tune of $1 trillion.  

Companies face many internal obstacles in trying to maximize their return on innovation investment. But most of these can be summed up in three prevailing themes: lack of long-term perspective, lack of focus, and lack of oversight.

The three biggest obstacles to corporate innovation

1. Lack of long-term perspective

An overwhelming majority of CEOs are pessimistic about future global economic growth. And so, despite acknowledging the imperative for change, many companies will figuratively bunker down, fortify business as usual, and protect existing products, customers, and cash flow when faced with this economic uncertainty. They will prioritize short-term gains to the detriment of long-term planning.

The consequence of this risk aversion is a lack of balance in many companies' innovation portfolios. This imbalance often manifests as an overemphasis on pursuing "low-hanging fruit" and incremental innovations in Horizon 1. In turn, companies can lose sight of the big picture, narrowing their innovation pipeline and missing out on promising opportunities. By underinvesting in innovation across Horizons 2 and 3, companies are more likely to expose themselves to disruption from competitors and obsolescence in the minds of customers down the road (see the 70:20:10 rule of innovation).

On the other hand, companies that invest wisely in innovation during economic uncertainty are well-positioned to capitalize on emerging opportunities and secure sustainable success, experiencing, on average, 2.4 times higher profit than their competitors. This correlation hinges on a company’s ability to prevent quick wins from obscuring future ambition.

Companies that build agile innovation portfolios, balancing ambitions across the three horizons, are shown to outperform competitors and have a better price-to-earnings ratio. Maintaining this balance—monitoring and adapting initiatives accordingly—is one of the most important responsibilities innovation portfolio managers must perform. It is essential to fostering future readiness in times of crisis, disruption, and uncertainty. 

2. Lack of focus

Resource allocation is critical to successful corporate innovation. Resources are finite, so organizations must set a strong strategic focus to ensure their most promising innovation initiatives receive adequate support. In many cases, this simply does not happen. Consequently, only 11% of executives report that their company’s strategic priorities have the necessary resources to succeed. 

Unfortunately, prevailing investments into collective creativity have intensified the problem. Well-intentioned initiatives to make innovation more inclusive and activate diverse expertise from both inside and outside a firm have produced a flood of ideas and inflated the volume of innovation portfolios. As a result, weak ideas and “zombie projects” often receive funding and risk cannibalizing the innovation initiatives that matter the most. Nearly two-thirds of business leaders claim their organizations expend high-value resources on low-value endeavors. Meanwhile, smaller firms with modest yet smarter allocation of R&D budgets see a higher return on sales. 

Matching resources with strategic priorities is often impeded by an inability to make quick and effective decisions. Gathering evidence to support long-term impact predictions, let alone securing stakeholder buy-in, can be extremely challenging, especially when trying to anticipate the effects of initiatives five to ten years into the future. 

Allocating a finite amount of resources—be it money, time, expertise, or labor—to new innovations requires empowered decision-making. And this doesn’t happen in a vacuum. Companies that make strategic decisions quickly and successfully do so within a framework that is structured yet agile, integrating data-driven insights, cross-functional collaboration, and continuous evaluation.

3. Lack of oversight

Ultimately, one of the biggest obstacles to corporate innovation is the lack of end-to-end innovation governance and robust oversight of ongoing efforts. Fragmented workflows across the innovation value chain create challenges in engaging people and fostering collaboration. Misalignment between teams that use multiple tools creates information silos, duplication of efforts, and missed opportunities. 

If all that innovation entailed was a great strategy and the collection of bright ideas, there may be little need for an end-to-end perspective. But strategies and ideas are intangible, tenuous things, and their potential value is only unlocked through alignment and smart execution. Companies without robust oversight of innovation’s many moving parts often struggle to anticipate potential synergies and roadblocks early on. 

Even amongst well-formulated innovation strategies, there is a failure rate of 67%. What’s more, of the nearly 30,000 new products launched each year, 95% fail, chiefly because of opaque decision-making that has not properly considered market forces. 

To put it simply, many companies fail to identify key interdependencies and connect the dots from strategy to execution. And it’s this inability to connect the dots, borne out of a lack of transparency and oversight, that dooms the majority of ideas before they’ve even left the drawing board. 

Transforming innovation through augmented intelligence

In summary, here’s what we know about the future of innovation: 

1. Volatility and uncertainty in the world will rise.
2. The imperative to innovate—to adapt, remain relevant with customers, and to do so faster and better than the competition—will grow. 
3. Many organizations that invest in innovation will face internal obstacles that diminish their ROI.

Point three, however, is not a foregone conclusion. We’re experiencing a shift from traditional and fragmented models of managing innovation—where these obstacles prevail—to a more effective model: continuous end-to-end innovation managed in one centralized operating system. 

And here’s what else we know about the future: machine intelligence is driving unprecedented efficiencies, giving rise to previously unimaginable opportunities, and transforming the way we live, work, and innovate. That’s why the ITONICS Innovation OS seamlessly combines machine intelligence and human ingenuity.

The future of innovation will see augmented intelligence seamlessly integrated into the core of decision-making processes, radically upgrading our capabilities for creation, collaboration, and groundbreaking innovation.

A snapshot of the future of innovation

What does the future look like as we replace traditional ways of innovating with a smarter, faster, and more impactful approach?

We are decisively entering the age of augmented intelligence.

Machine learning and artificial intelligence have impacted nearly all industries, completely changing how we process and make sense of data. These technologies enable us to derive patterns, perform diagnostics, and make predictions with increasing accuracy, speed, and scale.

We interact with these capabilities daily, often running in the background, powering the apps and tools we use for everything from monitoring our health and finances to making work more productive and collaborative.

Why should innovation be any different?

As machine learning becomes ubiquitous and the need to innovate crystallizes in the corporate agendas of many companies, there is a tectonic shift occurring in how we innovate.

ITONICS believes that the future of innovating will leverage machine intelligence as a multiplier to human ingenuity. This marks a shift away from siloed idea funnels towards automated systems that connect the dots, identify gaps, and develop opportunities—systematically and continuously.

Why work harder when you can innovate smarter?

From sporadic to continuous innovation

Traditional approaches to innovation have often been project-based and performed in isolation from other projects, departments, and objectives in a business. Even when drawing on more open approaches to involve employees or external actors, such efforts too often take the form of once-off campaigns lacking clear follow-up plans. These efforts tend to be sporadic, and few business leaders are confident in their companies' ability to plan for the long term on a proactive and ongoing basis. 

In contrast, continuous innovation is a sustainable approach by which organizations continually seek and implement new ideas, improvements, and changes in their products, processes, or strategies over time. By emphasizing an ongoing commitment to improvement over one-time breakthroughs, continuous innovation allows companies to adapt to rapidly changing environments and seize new opportunities. 

From ideas-centric to execution-centric

Many organizations still place a heavy emphasis on generating ideas as the primary driver of innovation. The belief is that by brainstorming and collecting a multitude of creative ideas, innovation will naturally follow. But the innovation world is littered with examples of companies generating promising-on-paper and often parallel ideas—only to fail or succeed based on their execution.

Innovation requires more than just coming up with ideas. In fact, ideation may be the easiest part of the process, particularly when new platforms and tools make it easy for people to contribute.

(HBR, 2015)

There is a growing understanding that ideas alone do not lead to innovation; execution is essential. Shifting the focus to execution ensures that ideas are effectively translated into tangible results. The critical differentiators are in the what, when, and how of execution: what ideas, when to act, and how to allocate resources and commercialize.

From siloed to centralized

It’s well recognized that collaboration and knowledge sharing foster more comprehensive and effective innovation. And yet, many companies still approach innovation as siloed teams with a fragmented set of tools—one platform for this department to collect ideas, a different tool for that unit to manage technologies—resulting in inefficient and opaque innovation ecosystems.

Centralizing end-to-end innovation intelligence is the single most important step companies can take to promote an innovation culture where all employees have access to the tools and information they need to participate in innovation. Breaking internal silos is about creating one transparent point of truth to reduce redundancy, save time and resources, and accelerate decision-making. In real terms, this translates to 2.3 times higher profit.

From collecting to connecting

Many steps in innovation begin with information gathering: identifying signals and drivers of change, capturing new ideas, and collecting data on customers, competitors, and potential partners. But companies can easily fall into the trap of simply “piling the haystack”—collecting innovation-related information while failing to convert it into meaningful innovation intelligence.

Connecting dots [...] is how savvy executives are creating breakthrough products and other world-beating initiatives.

(Forbes, 2015)

Building innovation intelligence requires organizations to move beyond collecting information. They must actively form connections, validate assumptions, and integrate organizational knowledge. Underpinning this process with machine intelligence allows organizations to derive more robust, evidence-based insights and solutions while steering strategic decision-making.

From manual to automated

Despite a steady increase in workplace productivity and a growing focus on automation tools and solutions, many knowledge workers remain overburdened with manual, time-consuming tasks. Two out of five innovation professionals report spending more time on documentation than on actual innovation. And it’s not only about speed and efficiency; manual processes are more likely to result in errors and missed opportunities.

While automation is nothing new, its application in innovation is still underestimated and often misunderstood. With the ability to quickly identify patterns, trends, and connections amidst large datasets that may otherwise be indiscernible to humans, machine intelligence paves the way for data-driven innovation—performed at speed and scale. Automating various steps in the innovation process likewise frees human resources to focus on the more strategic aspects of innovation.

From democratized to empowered

The concept of democratizing innovation has been around for decades. However, despite the fairly mature trend of open innovation, the prevalence of crowdsourcing platforms, and plenty of evidence validating the benefits of diverse perspectives in innovation, many organizational cultures, structures, and mechanisms fall short of achieving the full potential of inclusive innovation.

The biggest opportunities generative AI offers to businesses and governments is to augment human creativity and overcome the challenges of democratizing innovation.

(HBR, 2023)

Empowering everyone to innovate isn’t just about opening the floodgates. Empowered innovation guides participants with information and tools that adequately channel their creativity, reduce manual input, and remove any notions of imposter syndrome. This calls for an operating system that not only makes idea generation accessible, intuitive, and engaging—but can also streamline the evaluation process to cope with a greater influx of ideas and separate out the game-changers.