Preparing Strategic Planning for AI

AI promises to be a major disruption over the next decade. The current state of AI technology is not yet reliable enough for mission-critical strategy tasks, but most organizations have seen enough of its potential to start planning for its future advances.

Source: AI Preparedness Scorecard.

Where is AI Trending?

Apart from the algorithmic part, one of the key driving forces in AI is the cost of computational power. What can we expect when it becomes cheaper enough?

It will be cost-effective to:

• Train models on larger amounts of data.
• Train models on proprietary data, respectively.
• Eventually, increase training frequency to the level where the model is updated in real-time.

What Could a Perfect Strategy Copilot Look Like?

Imagine AI that:

• Understands current trends, driving forces, “watches” news, and “reads” business magazines.
• Monitors competitive and regulatory landscapes.
• Knows who the stakeholders of your organization are and what their needs are.
• Remembers all the strategic hypotheses that your team tried and their results.
• Knows the rationale behind all the goals.
• Understands current action plans, success factors, and expected outcomes.
• Has access to current and historical performance data.
• Understands comments your team made regarding performance spikes.
• Can follow links to goals or KPIs that you mentioned in internal discussions.
• Eventually, can watch the recording of the last strategy meeting.

What kind of questions could we formulate to such AI?

• Even if its backing technology remains at the current level of development, the insights we could get from this data are enormous.

Preparing Strategy for AI

An optimistic estimation is that we are still 3-5 years away from the described scenario.

What can we do today to ensure an easier transition to AI when it is good enough for the specific application?

A similar question is posed at different levels. For instance, the International Monetary Fund formulated¹ its AI Preparedness Indicators based on various indicators within the perspectives of Digital Infrastructure, Human Capital and Labor Market Policies, Innovation and Economic Integration, Regulation, and Ethics.

Digital Strategy Workflows

In the described scenario, AI can help you with your strategy, but it needs to understand the context of your organization. More importantly, it needs to understand the context of your historical decisions.

It won’t be enough to upload sales data to AI and expect some magic.

With this logic, our recommendation is to move all your strategy workflow to specialized software, like BSC Designer.

There is no need for the tool to have any AI functionality today (although we’ve been experimenting with some).

What’s critical is that the tool allows mapping all the relevant strategy-related data, which you could reuse in the future to train AI.

In BSC Designer, your strategy-related data is stored:

• In connected strategy and functional scorecards.
• In an audit trail that keeps all the activity logs.

At any time, you can export your data to CSV or Excel format.

AI Awareness and Data Specialist Training

Before, we were writing about digital transformation and Big Data. AI might sound like another digital initiative, but I see one fundamental difference.

With digital transformation and big data, our recommendation was to start with the needs of the stakeholders and find the best technology to support these needs.

We could say the same about AI, but at the current state of development, the technology is quite different from what we experienced before. Respectively, it will take more time for stakeholders to understand the potential of technology and formulate strategies. AI looks similar to the early days of electricity when it was not a commodity yet.

At this stage, the best thing we can do is to increase awareness about existing AI technology and train data specialists on properly preparing data for the near future of AI.

AI Awareness Index

When discussing the general awareness of AI, we can employ this simple index:

• 0 – Organization lacks awareness of suitable AI applications.
• 1 – Organization has identified potential use cases for AI.
• 2 – Organization has established integration with existing AI models (e.g., utilizing the ChatGPT API).
• 3 – Organization trains AI models using its own data.

Compliance

From the very beginning, questions of ethical and legal use of data were in the focus of AI evolution. We have already seen some AI regulations released. For example, EU regulation² bans biometric surveillance and emotion recognition. If you intend to make significant investments in AI, it’s crucial to monitor the legal landscape for forthcoming regulations.

Users of BSC Designer have access to the compliance template that can be adapted to new regulations, map fundamental driving forces, and track specific policy implementations.

A Roadmap to Prepare for AI

Let’s summarize some ideas to prepare organizations for AI by formulating a general AI readiness strategy. Tailor our AI readiness template to address your specific challenges.

General for strategic planning:

• Transition to fully digital strategic planning in specialized software. If you are using spreadsheets, here are some arguments to convince the stakeholders.

Focus organizational efforts towards AI preparation with this strategy map. Source: AI Preparedness Scorecard.

Learning goals:

• Formulate goals related to AI awareness training.
• Develop necessary data-related competencies in the organization.

Business systems level:

• Formulate a strategy for data generation quantified by data availability.
• Establish compliance monitoring for AI regulations (use the compliance template).
• Conduct a risk analysis to identify and quantify risks.

Internal and External customers:

• Identify the use cases where AI can potentially help (cybersecurity, automation, customer service, trend analysis).

Stakeholders:

• Align the use cases with the needs of stakeholders.
• Estimate cost savings, cost avoidance, etc.

What's next?

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