Innovation Portfolio Orchestration in the Age of AI

It has been theorized that humans can keep up with only about 150 friendships — approximately the population of a village. The theory is based on the size of the neocortex of our brains and evolutionary necessity. For most of human existence, people have only ever needed to maintain relationships with the people immediately around them. But as the world has gotten a tad smaller through the internet, FaceTime, and TikTok dances, we’ve boiled over the natural limits of our social biology. It follows, then, that we humans sometimes struggle to swallow the oceans of data being fed to us. 

In any global enterprise, innovation is a chaotic collection of R&D cycles, development pipelines, market considerations, and supply chain constraints. Under a growth innovation philosophy, the goal is to move beyond managing these as isolated activities and instead to orchestrate the portfolio as a single, unified business case.

This ensures that every innovation activity is directly linked to at least one strategic objective and that all decisions are made with a focus on the portfolio’s interdependencies. Growth innovation aligns your innovation processes with the enterprise strategy.

And it’s challenging to do.

All of the moving parts in enterprise innovation make it incredibly difficult to manage. We can only track a fraction of the interdependencies within a modern enterprise portfolio. We simply can’t wrap our minds around everything, and so we spend a lot of time rushing to put out fires.

This is where AI will be transformative, if we prepare correctly. By building the right foundation today, innovation leaders can prepare their teams for a future where they’re not operating from a defensive posture but making better-informed decisions than ever before.

Human Limitations in Portfolio Management

Innovation departments are full of talented leaders who struggle to juggle all of the activities and projects in their portfolios, and that’s because the human brain isn’t wired for this much detail and this level of complexity.

If you want to orchestrate your enterprise portfolio as a unified business case (and you should), you’re going to hit the biological ceiling in three areas:

1. The Knowledge Gap

No single leader can maintain a mental map of every project and activity that makes up a portfolio. More importantly, they can’t possibly track all of the interdependencies between them, especially when they’re not static. The projects in a portfolio are made up of constantly shifting variables, and the impact of one on another is always in flux.

2. The Comprehension Gap

Even if it were possible to see every moving part, understanding the cascading implications of any single decision is impossible. Calculating the potential fallout from moving a deadline is a gargantuan task in a portfolio with hundreds or even thousands of projects. We can anticipate some ripple effects, but we discover more in the ensuing crises.

3. The Insight Gap

Humans are incredibly adept at recognizing patterns, but we’re also really good at ignoring data that doesn’t confirm our opinions and biases (or inventing new patterns to reinforce them). This means that critical historical and current data can be overlooked when they don’t fit our project narrative.

AI Was Made to Fill These Gaps

AI thrives in the kind of complex data environment that overwhelms people. For innovation, once you provide the ideal data environment, AI will bridge gaps in your knowledge, comprehension, and insight.

AI Retains All the Knowledge You Give It

Once AI accesses your innovation database, it clocks and retains every bit of information. Every single project, target, and dependency is stored. And it’s all available to be recalled in granular, relevant ways in moments. A manager might forget a minor resource conflict in another factory, but AI won’t. It’s mapped and logged, so it can be resolved.

AI Can Calculate Implications at Scale

AI doesn’t actually think, but it can make connections, recognize relationships, and identify patterns in your data at speeds and scales that humans cannot match. Among other things, this empowers:

• Real-time interdependency mapping
  AI can recognize and track all of the independent strands that make up the intricate web of your portfolio. When any single variable changes, it can trace the connections throughout the entire system.
• Monte Carlo simulations
  AI can run thousands of what-if scenarios on your portfolio in moments. If you need to pivot on a project, AI will be able to simulate the effects on other timelines, budgets, and targets before you pull the trigger.

AI Provides Predictive Insight

AI is indifferent to internal politics and logical fallacies, so it’s more objective when examining current and historical trends. If a project is stalling or consuming too much labor relative to its projected value, it can escalate the issue to the appropriate stakeholders.

It can also provide predictive data based on historical patterns and your current trajectory to justify killing projects before they drain more value from the portfolio. But this is a tool that provides decision support, and not an algorithmic takeover. It flags projects and initiatives based on diminishing returns, but you’re still making the decisions.

Preparing to Incorporate AI Into Growth Innovation Orchestration

Many organizations believe that AI will bring some order to their innovation processes, but that’s not a given. If you apply it to a messy, decentralized data infrastructure, it will only accelerate the confusion. To truly experience the benefits of AI, you’ll need to build the structural foundation it relies on.

1. Workflow Readiness

For AI to understand your portfolio, it needs to understand how decisions are made. Ideally, this kind of formal documentation already exists, but if it doesn’t, it’s time to formalize it. AI aside, formalizing and documenting these areas will only strengthen your innovation processes.

• Document the decision process
  Define the steps that your organization takes, the specific events that trigger a review, and the governance system for approvals and escalations. AI needs to know who has the authority to make certain decisions, and what happens after decisions are made.
• Log the rationale for decisions
  This is a critical and often overlooked step, but AI needs to understand why a project was killed or a pivot was made. This includes specific fields for rationale in your innovation management system. Over time, AI will be able to discern what constitutes a good or bad decision, as well as your risk appetite and strategic boundaries.

• Define your AI policies
  Establish clear guardrails for the data AI is allowed to train on, and identify processes it can automate (like low-level reporting) versus those it should simply alert on for intervention.

2. Data Readiness

Without a foundation of centralized, high-fidelity data, AI will lack the context to truly understand your organization. Instead of strategic clarity, you’ll get “help” that may actually move your organization away from its goals.

You won’t be able to microwave the data architecture AI needs at the last minute. So it’s imperative that you start auditing your fragmented data sources and compiling them into a single IM system as soon as possible. Once that’s done, it will be essential to create a culture of continuous syncing so AI is always learning from the latest information.

NOTE: For a more detailed understanding of AI’s impact on data visibility, read “AI Is Transforming Data Visibility, and Innovation Managers Need to Be Ready.”

3. Organizational Readiness

True organizational readiness is about having a clearly articulated, machine-readable growth strategy. Too often, strategy is abstract or spread around the organization in decentralized documents and slide decks. For AI to augment your innovation processes, your innovation strategy needs to be AI-accessible and presented precisely.

This means you need quantifiable KPIs that ladder up to your growth objectives, and consistent metadata applied to every innovation activity. This gives AI a readable map to analyze your portfolio.

NOTE: To dig into this a little deeper, check out “Aligning on Growth in the Age of AI.”

You Need an IM Control Tower

There’s a huge difference between a repository and a control tower. A repository stores files, but a control tower (especially one with the hyperconfigurability of Aligning on Growth in the Age of AI) learns the logic of the business.

If you want a helpful chatbot, a repository will do. But if you’re hoping AI will help you with strategic orchestration, you’ll need centralized data that represents your innovation ecosystem across three dimensions:

1. The Strategic Architecture (The Why)

By centralizing your bespoke growth targets, metrics, and KPIs in the platform, you’re giving AI the strategic filter necessary to make sense of the data. This moves it from simply tracking projects to auditing them against your goals.

2. The Portfolio Map (The What)

AI needs to see every active project, product, and brand. When your IM system allows for this kind of granularity, AI can perform real-time interdependency mapping, so you know how a delay in one project will ripple throughout the portfolio.

3. The Resource Engine (The How)

A lot of innovation is about juggling ambition and capacity. When your IM system allows you to track expertise, budgets, and technologies, you have a clear view of how your resources are being used across the portfolio. Instead of wondering if you have the human resources necessary to pivot, AI will be able to simulate 1,000 versions of a pivot against your actual resource constraints to find the path with the highest probability of success.

Accolade: More Than Data Storage

If you don’t have an IM infrastructure that captures the mathematical relationships between your strategy, your projects, and your resources, AI will lack strategic context. It will inevitably lead to missing critical connections between data or hallucinating connections that don’t exist. Accolade’s hyperconfigurable data architecture means that you create the bespoke labels, tags, and relationships necessary to give AI the perspective necessary to be an actual asset.

With a control tower like Accolade, you provide the structural foundation. This will make all the difference, allowing AI to be a core component of your growth innovation orchestration.

Get a demo of Accolade.