Aligning on Growth in the Age of AI

Your favorite meal isn’t your favorite meal because of the pots and pans it’s cooked in, the refrigerators and stoves that cool and warm it, or the forks, spoons, and plates it’s served on. None of those things matter without the right ingredients. Fresh produce and high-quality proteins will always hit the spot, no matter if they’re cooked in Williams-Sonoma’s finest stainless steel or budget Dollar Tree tins. The same is true of enterprise innovation: artificial intelligence may open vast possibilities in productivity and efficiency, but those possibilities will never be realized if its own core ingredient, your data, is subpar. 

Despite the multi-year hype cycle around AI, it’s still in its infancy. And while the world focuses on generative tools that write emails or create images and videos, we’re still waiting to see how it will impact enterprise innovation. But we do know this: it will usher in a fundamental shift in organizations’ cognitive capacity.

In the past, enterprises were limited by the amount of data the leadership team could integrate and act on. AI will allow organizations to manage complexity on a whole new level.

Simply adding AI won’t lead to organizational growth. (Especially since all your competitors are implementing AI too.) Growth will remain a product of your strategy and execution. And, for better or worse, AI will be an accelerant to the management strategy driving your innovation activities.

Growth Innovation: Because You Still Need a Strategy That Works

There’s a lot of innovation happening in modern enterprises that’s focused more on activities than outcomes. Teams are busy, labs are active, and the pipeline is full. But the link between that labor and the highest-level enterprise objectives is often thin. A 15-year analysis by McKinsey of 5,000 of the world’s largest public companies found that only about 13% grew by more than 10% annually. And only about 6% achieved sustainable organic growth. That’s a huge problem, and it won’t be solved by AI.

The enterprises that already solve this problem do so via growth innovation: an innovation management philosophy that sets growth as the single most important innovation outcome and manages every step of the innovation process accordingly. It becomes actionable when every stakeholder in the organization commits to three critical principles:

• Growth: Articulate measurable growth targets in the innovation strategy, and explicitly connect every innovation activity to at least one of those targets.
• Visibility: Centralize all innovation management data in one place and give every stakeholder access to the information they need.
• Orchestration: Make every decision in the context of a portfolio-level growth strategy.

In innovation structures with a “Let’s try things and see what works” philosophy, AI will amplify the chaos. In contrast, growth innovation’s principles provide a track and some guardrails that allow AI to enhance your innovation processes and generate predictable growth.

Let’s examine the ways we anticipate AI will impact your ability to align on growth.
And if you’re interested in how AI will affect the other principles (or growth innovation in general), you can check out these articles, too.

• Growth Innovation in the Age of AI
• AI Is Transforming Data Visibility, and Innovation Managers Need to Be Ready (coming soon!)
• Innovation Portfolio Orchestration in the Age of AI (coming soon!)

3 Barriers to Growth in Enterprise Innovation (And How AI Overcomes Them)

Due to their small size, startups can be extremely effective at alignment. It happens naturally as stakeholders meet over coffee and in regular check-ins. Proximity and accessibility keep every area of the organization working in harmony.

But enterprises are huge and intricate. By the time strategic objectives filter down to R&D, they’ve been diluted. And the activities in the pipeline end up disconnected from the initiatives.

Articulating growth targets and connecting every innovation activity to them sounds obvious until you consider all of the innovation activities at play. It becomes obvious that it’s almost impossible to do at scale because you run into three specific barriers:

1. The Knowledge Barrier

There’s often a disconnect between organizational goals and innovation activities. Maintaining a real-time, comprehensive view of overarching targets alongside the flurry of innovation activities quickly becomes a problem. No one has the capacity to see the whole picture and keep their eye on the ball across thousands of projects and initiatives.

This leads to a bigger problem: the portfolio becomes an abstract collection of activities rather than an intentional engine for growth. Because no one can have an intimate knowledge of the entire portfolio and all its shifting variables, they end up focusing on the tasks at hand, and the big picture gets lost.

How AI Overcomes the Knowledge Barrier

No one in the organization can read every project brief, financial report, and technical document. That’s where AI steps in. The best manager might remember the highlights from last quarter’s top 10 projects, but AI is familiar with every project in the last decade (provided you’ve uploaded historical data), right down to meeting transcripts and backlogs.

This gives the enterprise a significant leg up in understanding the portfolio, planning initiatives, and building certainty that projects will help hit revenue targets. Not one bit of innovation knowledge will end up overlooked or wasted.

2. The Comprehension Barrier

Even when the growth targets are clear, you run into translation problems. Board-level aspirational targets need to be communicated to innovation teams in a way they can understand and act on. Those goals then need to be rendered into KPIs, metrics, and gate criteria that align with them.

It’s virtually impossible to create the bespoke metrics that will align every unique project in a strategic portfolio with those targets. This means organizations end up settling for cookie-cutter metrics that may or may not actually ensure projects are on track to meet their goals.

Communicating those innovation activities back up to the board introduces more translation problems. Executives are interested in whether or not portfolios are actually on track to hit their targets, not all the granular technical details. This means that someone has to aggregate, filter, and summarize all of this information. By the time it’s turned into a board-worthy report, it’s weeks out of date and subject to the biases of the individuals who compiled it. Ultimately, executives end up relying on sanitized, lagging indicators rather than gaining clarity on real-time activity.

How AI Overcomes the Comprehension Barrier

AI will provide the translation layer necessary to make your data useful. After ingesting your data, it’ll understand the relationship between the various innovation activities and organizational-level objectives.

This will give AI the context to reliably help translate executive mandates into quantifiable KPIs and metrics that equip leaders to monitor the portfolio’s progress toward goals with predictive, mathematical precision. It will also reinforce strategic value checks at every gate, ensuring that projects don’t move forward if they’re not aligned to the growth strategy.

3. The Insight Barrier

In global enterprises, clarity ends up localized to siloed business units. Bottlenecks, human resource misallocations, and technical problems might be obvious to one team but invisible to the organization because it’s impossible for anyone to be mindful of the thousands of projects that make up a portfolio, let alone the interdependencies that connect them.

When teams have a limited view of a few specific projects, they see project failures in isolation. They end up diagnosing specific technical or supply chain issues and miss the patterns that connect these problems to other flailing projects. These patterns are buried in layers of metadata and end up unrecognizable to the people in charge.

The inability to identify patterns consistently makes it difficult to perform meaningful regression analysis to help understand which variables drive success. Instead of using data to predict outcomes, the organization falls back on intuition and internal narratives.

How AI Overcomes the Insight Barrier

AI’s true power lies in its ability to recognize patterns across thousands of projects. An understanding of the vast number of project variables and access to your historical data will enable AI to recognize environmental or technical factors that will likely pull a project off course.

AI will also scan the entire portfolio to identify strategic gaps. If the organization has set specific growth objectives but has neglected to line up initiatives or projects to achieve those goals, AI will identify and elevate those gaps so they can be filled.

Creating a Readiness Roadmap for AI

These AI capabilities are coming to innovation, but you can’t just sit back and wait for them to materialize. When AI makes its way into your innovation processes, it will either magnify your organizational discipline or amplify the chaos. Now is the time to create the data infrastructure AI needs so you can benefit from it later.

Organizational Readiness: The Key to Growth Alignment in the Age of AI

How prepared are you for AI to augment your innovation processes? Preparing for this shift will entail several critical organizational steps, but they’ll all share a non-negotiable requirement: the inputs and outputs of these processes must be machine-readable.

You might have your workflows nailed down, but if the information lives in a slide deck that AI can’t access or it’s documented in abstract language, it’s not going to help.

Here are the five steps to organizational AI readiness:

1. Ensure There’s a Growth Strategy in Place

The most critical step is ensuring that an organizational strategy actually exists. That probably sounds obvious, but you’d be surprised.

In our internal State of Corporate Innovation Survey, examining the global innovation landscape in organizations ranging from the food & beverage industry to chemical manufacturing, we made an important discovery. When asked to rank various impediments to innovation, 1 in 4 enterprises listed insufficient innovation vision and strategy as a significant problem. Another 31% recognized it as a somewhat serious problem.

Before you even begin to think about using AI for strategic alignment, you need to align on a clear growth strategy.

NOTE: For a granular discussion on crafting a strategy, check out “How to Craft a Growth Innovation Strategy.”

2. Articulate the Strategy

Five years ago, you may have gotten away with an innovation strategy that lived in the minds of folks in the C-suite, but not anymore. In the age of AI, this is a technical bottleneck. If it isn’t articulated and in your IM system, it simply won’t exist to AI.

This strategy needs to be in your system and machine-readable, clearly defining areas like:

• Financial targets
  “How much money do we need to make?”
• Constraints
  “What restrictions impact how this money is made? What other factors, like legal or ESG, do we need to consider?”
• Revenue streams
  “Where is this money going to come from?”
• Initiatives
  “What are the strategy-level ‘buckets’ that innovation needs to be applied to make this strategy a reality?”

Documenting these elements creates the architecture necessary for AI to monitor progress in your portfolio.

3. Structure This Strategy With Quantifiable KPIs

Ensuring this strategy is machine-readable involves setting specific metrics for every activity. Some of these will be the same metrics you already use today, others will be new, bespoke indices that translate “softer” objectives into numbers a machine can understand. With these inputs and outputs defined, your artificial intelligence platform will be able to more clearly understand what exactly it can measure.

4. Connect Innovation Projects to Specific Growth Objectives in Your IM System

AI needs a structured environment where it can instantly recognize which strategic bucket a project belongs to and which metrics it’s intended to impact. This means that every project and activity will need a clear, calculatable line drawn between it and the strategic objectives it’s intended to support.

As best as you can, you need to define the quantitative and qualitative relationships between individual projects and overall objectives. This will involve setting up your data architecture with metadata fields that define (or allow AI to infer) the relationships between a project, its portfolio, strategic objectives, and other projects. Sometimes this will be direct; other times you’ll need to link together various metrics in order to turn the way your organization works into a series of formulas AI can understand.

This means linking projects to specific initiatives and to the specific variables that ladder up to your growth targets. When every team across every business unit consistently uses the same metadata conventions, you give AI the clean, structured data necessary for portfolio analysis.

5. Establish Governance Frameworks and Data Boundaries

If you get steps 1–4 right, AI will have what it needs to augment your processes. But giving AI access to your data infrastructure requires some clear boundaries to ensure you don’t end up in a security bind. The final step defines where AI is allowed to go and what you do with its suggestions.

You need clear limits on the data AI can access and how it handles intellectual property. This means identifying sensitive data that should be sequestered from AI, and setting up rules to ensure that AI behavior doesn’t lead to IP leaks. This step ensures that the benefits AI brings aren’t offset by significant security breaches.

Organizational readiness is only one leg of the tripod. To be a truly AI-ready organization, you need to address some of the technical and operational foundations that will allow AI to function.

Data Readiness

AI can’t orchestrate what it can’t see. Data is AI fuel, and readiness requires centralizing your innovation data into an innovation management system. It’s only when your historic and present data is gathered into a single database that AI can identify long-term patterns and potential problems so as to bring about strategic certainty.

NOTE: For a deep dive on this element of readiness, read “AI Is Transforming Data Visibility, and Innovation Managers Need to Be Ready” (coming soon!).

Workflow Readiness

To achieve workflow readiness, you need machine-readable documentation of your business logic. This requires defining the activity triggers, governance steps, and authority chains that drive your innovation pipeline. It also includes logging the rationale behind critical innovation decisions like pivots or project kills. This gives AI the context it needs to optimize your processes.

NOTE: This process is detailed in “Innovation Portfolio Orchestration in the Age of AI” (coming soon!).

An IM Platform Is a Non-Negotiable

In the future, every one of your competitors will have access to the same AI platforms. It’s your readiness that will give you a competitive advantage. To beat your competition off the starting blocks, you’ll need to be ready to feed AI the highest-quality data.

An IM system like Accolade innovation management software is a prerequisite for digitizing your strategic hierarchy and goals. Accolade’s highly configurable infrastructure allows you to tag and label data in a manner that aligns with your actual structure and needs. This way, an AI model can easily understand your growth objectives, portfolio mix, and innovation processes, so it has a clear map to follow.

By automating workflows and governance with an IM platform, you reinforce best practices, enabling AI to learn the sequence in which a project moves through the pipeline. This creates a baseline AI will use to identify anomalies, suggest pivots, and automate more routine processes.

Getting started today will ensure you have the data history necessary to train AI tomorrow. Get a demo of Accolade.